End of Dumb Tables in Web Analytics Tools! Hello: Weighted Sorts

Arthur C. Clarke said:"Any sufficiently advanced technology is indistinguishable from magic."That quote comes to mind when I think of a new feature in Google Analytics that carries the unassuming name of Weighted Sorts. It is an advanced implementation of technology (mathematical algorithms in this case) and when used it very much feels like magic!In this blog post I want to share with you why I am so incredibly excited about this feature, how it works and how going forward you will reject every tool that does not come built in with this feature (ok so maybe that's a stretch, but I promise you this is so cool that at least for a few minutes you'll think other tools are lame by comparison!).Let's take a couple of steps back, get some context before we dive in.The Problem.We have a very long tail of data in web analytics. Tens of thousands of rows of keywords in the Search Report (even for this small blog!). Hundreds and hundreds of referring urls and campaigns and page names and so on and so forth.Yet because we are humans we tend to look at just the top ten or twenty rows to try and find insights. The problem? The top ten of anything rarely changes (except in rare circumstances like a sale or on a pure content – think news – site).Hence I have persistently evangelized the need for true Analysis Ninjas to move beyond the top ten rows of data to find insights.How? Advanced table filters, tag clouds and keyword trees are a good start.But we need more.One more problem though.As if massive data we have is not enough of a problem, we also rely on Averages, Percentages, Ratios and Compound/Calculated Metrics in a profoundly sub optimal way, as a drunken man uses lamp-posts – for support rather than for illumination.Take a percentage, for example Bounce Rates. The top ten won't change.Hmmm. what to do. what to do?You know what I'll try to  find the keywords with the highest bounce rates and fix them! After all I don't want to have all those visitors say: "I came. I puked. I left!"Ok analytics tool: Sort descending!Arrrrrh! Useless!See all those single visits? Would improving these bounce rates have a huge impact?Ok maybe I should learn from keywords with low bounce rates so I can perhaps take the lessons from my awesomness and apply it to others. Tool: Sort ascending!Arrrrrh! Again! Useless.What could I possibly improve by focusing on these keywords with so few visits? Nothing.So to recap:We tend to...<br/><div align='right'>2010-09-07 11:07:51</div>

Qualitative Web Analytics: Heuristic Evaluations Rock!

Every believer in Web Analytics 2.0 knows that awesomeness comes not from answering just the "What" question but from also answering the "Why" question.What comes from Google Analytics, Adobe Site Catalyst, WebTrends, CoreInsight / NetMetrics and more.Why comes from lab usability studies, website surveys, "follow me home" exercises, experimentation & testing, and other such delightful endeavors.Why gives context to the What, and delightfully helps you not have to overlay your biases when you try to infer visitor intent form all the What (clickstream) data.I know that you agree Why is important.I know that you even realize Why is ever easier to accomplish (usability studies are economical, surveys and testing platforms start at the sweet price of free!).Yet your site stinks like a skunk.The reasons are complicated.You are smart, so that is not it. Maybe it is internal politics. Maybe it is the agency you have outsourced the site to, the agency whose only competence seems to be gratuitous use of flash. Maybe it is that it is not your job, you are the "quant" guy or "GA girl". Maybe even after taking one of the team and going out on three dates the IT Dude still refuses to put Website Optimizer tags on the site. Maybe the well meaning but "never met our real customers" HiPPO dictates site design.Bottom-line: Your site stinks and you need to fix it.Allow me to introduce you a User Centric Design that is, I think, the solution you have been waiting for: Heuristic EvaluationsI love heuristic evaluations because they are cheap, fast and you probably already have resources you need in your company. A large part of my adoration also comes from the fact that heuristic evaluations are us going back to the basics in an attempt to create un-stinky websites.What Are Heuristic Evaluations?A heuristic is a rule of thumb. In as much, heuristic evaluations follow a set of well established rules (best practices) in web design and how website visitors experience websites and interact with them.When conducting a heuristic evaluation a user researcher (or an HCI expert) acts as a website customer and attempts to complete a set of predetermined tasks related to a website's existence. For example: Trying to place and order, or looking to find out the status of an order, or the solution to an error code, or decide which of many products on a site are optimal for a specific customer persona.But here is the lovely part, and why...<br/><div align='right'>2010-08-23 11:35:55</div>

Win Big With Web Analytics: Eliminate Data & Eschew Fake Proxies

The hardest nut to crack in any type of analytics is getting our decision makers (bosses, leaders, marketers) to take action based on data.The hard nut is not that we all are doing basic reporting about Visits and Bounces. Ok doing just that is lame. But still that's not all of it.It might shock you that the hard nut is not even that we, the people who "play" with data, the Analysis Ninjas if you will, are not leveraging custom reports and advanced segmentation and activity alerts and all that cool stuff.It is that no one wants to take action on our data. Ok not no one. But most of the time no one takes action based on our hard work.Why?To illustrate perhaps the most obvious, and yet hidden, problem I wanted to share with you a recent experience I had with data. It was not in the context of web analytics and yet I think it presents a solution to our problem.On my (beloved) Nexus One android phone one of my absolute favorite applications is Cardio Trainer. I just love it. The UI and the UX and how it is so darn easy to use (and much more well thought than competitors like My Tracks). You must get it.I use it when I do any kind of exercise (or hike with the kids). You just open the app, say Start Workout, choose the type of work out (running, biking, elliptical, horseback riding, and 15 other choices) and you are up and running.It starts tracking you (GPS and what not), pauses when you pause, and if you choose even talks to you (motivation!).When the workout is done it gives you. . . . data!Awesome right? What analyst / data person would not love this.There is a sweet map (I can switch to the satellite view and see the buildings I went around!) and there is lots of data.Time: 0:22:07  Distance:  6.74 kilometers Speed: 18.3 km/hr Calories: 207It is a great dashboard.For the first few times I used CardioTrainer I loved seeing the data (almost more than the fast bike ride). I would switch the map to the satellite view and drill down into the woods and the lake and the trees.Of course I would look ay the data and maybe remember fragments.Remind you of something?Perhaps your web analytics dashboard. Or perhaps your "data summary report". Or, hopefully not, the data puke that goes to the entire management team.Initially everyone's happy, nothing much happens, yet it feels good.In my case pretty soon I switched to another view of the data, this one. . . .Shorter summary. No gimmicks like the map. : ) A bit more data.I can see Total...<br/><div align='right'>2010-08-09 10:23:12</div>

Win Big With Web Analytics: Eliminate Data + Eschew Fake Proxies

The hardest nut to crack in any type of analytics is getting our decision makers (bosses, leaders, marketers) to take action based on data.The hard nut is not that we all are doing basic reporting about Visits and Bounces. Ok doing just that is lame. But still that's not all of it.It might shock you that the hard nut is not even that we, the people who "play" with data, the Analysis Ninjas if you will, are not leveraging custom reports and advanced segmentation and activity alerts and all that cool stuff.It is that no one wants to take action on our data. Ok not no one. But most of the time no one takes action based on our hard work.Why?To illustrate perhaps the most obvious, and yet hidden, problem I wanted to share with you a recent experience I had with data. It was not in the context of web analytics and yet I think it presents a solution to our problem.On my (beloved) Nexus One android phone one of my absolute favorite applications is Cardio Trainer. I just love it. The UI and the UX and how it is so darn easy to use (and much more well thought than competitors like My Tracks). You must get it.I use it when I do any kind of exercise (or hike with the kids). You just open the app, say Start Workout, choose the type of work out (running, biking, elliptical, horseback riding, and 15 other choices) and you are up and running.It starts tracking you (GPS and what not), pauses when you pause, and if you choose even talks to you (motivation!).When the workout is done it gives you. . . . data!Awesome right? What analyst / data person would not love this.There is a sweet map (I can switch to the satellite view and see the buildings I went around!) and there is lots of data.Time: 0:22:07  Distance:  6.74 kilometers Speed: 18.3 km/hr Calories: 207It is a great dashboard.For the first few times I used CardioTrainer I loved seeing the data (almost more than the fast bike ride). I would switch the map to the satellite view and drill down into the woods and the lake and the trees.Of course I would look ay the data and maybe remember fragments.Remind you of something?Perhaps your web analytics dashboard. Or perhaps your "data summary report". Or, hopefully not, the data puke that goes to the entire management team.Initially everyone's happy, nothing much happens, yet it feels good.In my case pretty soon I switched to another view of the data, this one. . . .Shorter summary. No gimmicks like the map. : ) A bit more data.I can see Total...<br/><div align='right'>2010-08-09 10:23:12</div>

5 + 4 Actionable Tips To Kick Web Data Analysis Up A Notch, Or Two

We lovingly craft reports every day. We try to make sense of what they are saying. When we hear nothing we try to bludgeon them, hoping for the best.My hope in this post is to share some simple tips with you that might make your reports and analysis speak to you a bit more. Suggestions that might increase the probability that you'll bump into things that might be insightful, and communicate data more effectively.None of them are very hard to do, but I think they make a world of difference.Excited? Here we go. . .#1: Go as deep as you can. Then, a little bit more.Far too often in our daily lives we let our job titles limit how deep we go in our analysis.For example let's say I work at a delightful car / health / spaceship insurance company. Naturally all of my analysis is focused on the efficiency of the website in moving the Visitors quickly from the landing page to click on that delightful Submit Quote button.I am focused on what the site does because that is what my job title says: Web AnalystI am analyzing campaigns (which ones convert better and which worse), I am looking a little bit at the bounce rates, and of course I am totally obsessing about my seven step quote submission funnel (and how to reduce abandonment).Bottom-line: Quote, quotes, quotes.And that is fine.The data is easily available in the web analytics tool so why not.Here's my advice: You should kick things up a notch. Don't focus just on the quote (the part the site does), include the final conversion to a paying customer (even if that data is offline).The picture you get from stopping at Quotes might be very different from stopping at Policies Purchased.Here's what you are focusing on (and it is good):All my experience in these things suggests that it is dangerous to think that the Conversions column is representative of the final outcome.Here is what it probably looks like (and this is going from good to great):See how the ranking changed?You would make different recommendations right? Would it save your company money? Would it make you refocus your efforts on where improvements are needed?You betcha!For straight ecommerce websites the first picture is what you use every day. But for most other types of businesses the final success does not exist in web analytics tool. So what? Get the data out of the crm / erp / "backend" system. . . dump it into excel. . . write a simple formula!Usually you don't need a complicated multi year data warehousing effort...<br/><div align='right'>2010-07-26 11:02:49</div>

Viral, Social, Sentiment, Mobile: 4 Delightful Web Analytics Solutions

Stale.One thing that I never want to be.We all have a tendency to learn up to a point, we get comfortable and keep chugging along rarely investing in our ongoing education.I call it the slow but sure path to irrelevancy.Let me share my prescription for avoiding irrelevancy: Try new things.Simple right?At any given time I have six or seven interesting tools running on this website. That's not including others I actively seek out around the web. Most of them are not even related to my current job or problems I know of. And that's on purpose.I want to constantly be in the know of new and more clever ways of working with data, tools that are often solutions to problems we don't know we have yet or tools that are sometimes seeking problems to solve!!Irrelevancy is not fun. Stale people are not appealing (just like, as your mom taught you, a week old bread). If there is one thing you take away from it post I hope it is the importance in investing in yourself / your education / your ongoing awesomeness.In this blog post I want to share four analytics tools that I have been playing with for a while… tools that solve an interesting problem… tools that point to what might be in terms of our near term analytical future… and in almost all cases they don't even know!I love doing this, I hope you'll have as much fun as I do.First Some Context.Remember I am the creator of the 10/90 rule of investment in web analytics.I had created the rule many years ago, early into my job at Intuit, and quite simply it states:If you have a budget of $100 to make smarter decisions on the web…. invest $10 in tools + vendor contracts and invest $90 in people (big human brains inside or outside the company to do analysis and the process of producing insights).When I had created the rule Google Analytics did not even exist!The rule was borne out from my own experience having inherited a world class tool we were paying $250k a year for and produced crap. Well not crap… lots of data that no one cared about or actioned. I threw out the world class tool, purchased ClickTracks for a fraction of the cost and put money into Analysts and boom!Ok not boom overnight… but over the course of a few months the org started to be more data driven, because analysts we hired produced analysis. That fed a virtuous cycle. More analysts. More insights. More desire to be data driven.So as you look at the tools below remember the 10/90 rule.In the end it does...<br/><div align='right'>2010-07-12 10:56:43</div>

Win With Web Metrics: Ensure A Clear Line Of Sight To Net Income!

We have more web metrics and data than there are stars in the universe (slight exaggeration!). Yet we stink at informing decisions. Our reports are ignored. Sites & online marketing continue to suck.A large part of the reason is that a large part of our job seems to consist of glorified data puking, hoping someone will be impressed. After all there is so much data in those reports!! #failThis blog post encourages you see the forest, the much hyped big picture, and shares a framework that will help you ensure that every single moment of your day is spent on activity that will be:1. of value to your organization, hence appreciated and acted upon2. has a clear line of sight to the one thing that matters: profitIf you don't want your professional life to be frittered away then please come along this short journey.First some context…If you have seen one of my keynotes recently then you have heard my near evangelical fervor when it comes to trying to convince you to compute Economic Value.If you have Web Analytics 2.0 then you already know who much attention is paid to this concept in the book (jump to page 159 for how to compute it for your website).The reason for this emphasis is to help fix our miserable failure at at creating data driven organizations.To steal your energy away from being just in the report / data production business.To encourage you to do better than spend a lifetime implementing analytics tools, building data warehouses, chasing the next shiny object.My recommendation has been:1. Identify your Macro Conversion (focus on this a lot!).2. Report revenue. Report like crazy on the 2% conversion rate.3. Identify your Micro Conversions.4. Compute the Economic Value (see page 159). Show your bosses and HiPPO's the complete value of your website.That last one will get any organization to sit up and pay attention.Why?Because for the first time in their young and passionate life they'll see the complete value your website is adding to the business. And because my dear it will be a huge number that no one can ignore! You are going to tie your work to the bottom line!Revenue = Good. Economic Value = God! [Also slight exaggeration :)]Professor Ken Wong's Magic PotionProf. Wong is the award winning Commerce '77 Teaching Fellow in Marketing at Queen's School of Business (and an awesome speaker, you should hire him for your next event!).He took the stage after my talk and said, I am paraphrasing here, "Avinash did not...<br/><div align='right'>2010-06-28 10:59:27</div>

Identify The Known Unknowns: Leverage Analytics Custom Alerts

Most of the time spent by Marketers & Analysts tends to be spend looking for "known knowns".Things we know and expect to see in the data, we look to see if they are there. "Oh look Google is still our Number 1 referrer and we are selling lots of product x as we always do. Yea!"Some of our time is spent reacting to the "known unknowns". Looking for things we know might be happening but don't know when they happen. "I would like to know when conversion rate dips below q%, let me go see if that happened last week."None of it is spent looking for the "unknown unknowns"…. mostly because it is a hard problem to solve. But one that is important for Omniture and WebTrends and Coremetrics and other tools to solve. "I did not even know 20% of our customers were from Australia and that 9 days ago they all stopped coming to our site."[For one approach to solving the unknown unknowns problem, and source of this framework, please see the second video in this blog post: Analytics Becomes Intelligent. Hello Insights!]I believe that actions taken based on web analytics data dramatically increase when we shift from our obsession with the known knows to the known unknowns.From: "Oh my God I did not know that metric had crashed for that segment!! If only I had known that I would have taken action sooner."To: "Thank goodness I had an alert in my inbox about that big drop yesterday, I'm off to fix landing pages for that segment. No I can't talk to you about Desperate Housewives, I have to go take action!"And you know what? That is easier to accomplish than you might think.All you have to do is use the built in Custom Alerts feature in your web analytics tool (and every single tool worth its salt now has one, so you have no excuse not to use it!).How does it work? You want to know when something of value happened. But you don't want to hunt and peck at data. You want to be poked with a stick that it happened. You need. . . .Being told when to look at important things you can take action on, sounds magical and revolutionary? It is. :)In this blog post I want to share some alerts with you with the hope that it'll spark your creativity.I also want to hear from those of you who have already use this feature. What is your favorite alert in Omniture? What is the one alert that you created in WebTrends that saved your job? What is the first alert you create for a client, and why?The most...<br/><div align='right'>2010-06-15 11:53:21</div>

Online Marketing Still A Faith Based Initiative. Why? What's The Fix?

The world of the intertubes should be a lot more data driven and awe-sexy than it really is.Yet for all our collective efforts at writing and tweeting and kvetching online marketing is still based mostly on faith. Not data.Surprising at so many levels right?Last week I had the privilege of being invited to deliver the keynote at the annual CMA President's Dinner. John Gustavson, President & CEO of the Canadian Marketing Association, invites a hand selected audience consisting of the crème de la crème of Canadian executives from a vast array of industries. This year they were joined by senior Canadian government officials.It is difficult to choose something for an address to such a diverse, accomplished and senior audience. My choice was the above thought, faith & data.My plan was to challenge the status quo, deliver tough love, and inspire transformation.There were no slides, no notes, just me up on the stage talking. Ok there were around 10 or so bullet items, the talking points. On the flight to Toronto in order to prepare I also wrote down the speech (though I don't read my speeches, so it stayed on the computer).I wanted to share the speech with you in the hope that it helps you accept the challenging reality we face. I hope it also provides you with a practical set of recommendations to kick your work up a notch or two so we can all win at this web thing.TV. Internet Marketing. Faith. Data. Problems. Solutions. . . .__________________________________________________CMA President's Dinner Keynote.Good evening.It is a pleasure to be here tonight and address such a beautiful audience. I want to thank John for inviting me.My plan tonight is to present some thoughts on how to transform people and companies in the age of the Web, for about 15 minutes, and then address your questions. You are welcome to ask me questions about my talk or anything else connected to the web, companies – marketing – opportunities.I must admit up front that I am as hard core as any evangelical born again Christian in my passion when it comes to the web. The raw innovation and empowerment that a connected digital world has unleashed is the reason I lovingly refer to it as "God's gift to humanity".To truly appreciate some of this let us consider the world where marketing is done on faith. Television. Or for that matter magazines or newspapers or radio. All wonderful channels, that are needed and will be around for a long time! But when it...<br/><div align='right'>2010-06-01 11:11:55</div>

Web Analytics Segmentation: Do Or Die, There Is No Try!

My love for segmentation as the primary (only?) way of identify actionable insights is on display in pretty much every single blog post I write.I have said: All data in aggregate is "crap".Because it is.One of my earliest blog posts extolled the glorious virtues of segmentation: Excellent Analytics Tip#2: Segment Absolutely Everything.Many paid web analytics clickstream analytics tools, even today (!), don't allow you to do on the fly segmentation of all your data (not without asking you to change javascript script tags every time you need to segment something, or not without paying extra or paying for additional "data warehouse" solutions).So it was with absolute delight that I wrote a detailed post about the release of Advanced Segmentation feature in Google Analytics in Oct 2008: Google Analytics Releases Advanced Segmentation: Now Be A Ninja!Of course Yahoo! Web Analytics, the other wonderful free WA tool, had advanced segmentation from day one.And as recently as two weeks ago I stressed the importance of effective segmentation as the cornerstone of the Web Analytics Measurement Framework.The Problem.You can imagine then how absolutely heartbreaking it is for me to note that nearly all reporting that I see is data in aggregate.All visits. Total revenue. Avg page views per visitors. Time on site. Overall customer satisfaction. And more. Tons of data "puking", all just aggregates.The achingly tiny percent of time that the Analyst does segmentation it seems to stop at New vs. Returning Visitors! I have to admit I see that and I feel like throwing a tomato against the wall.Yes new visitors and returning visitors are segments. But they are so lame that I dare you to find any insight worth, well, a tomato based on those two. You can't. Because new and returning are still two big indefinable globs!Even if your business actually is tied to understanding the first and then subsequent visits by a person then you are far better off segmenting using Visitor Loyalty (in GA count of visits).But I am getting off track (this whole non-segmentation business drives me bananas!).Deep breath.The Unbearable Lightness of Being.Segmenting your data is key to your success and that of your company.It is not very difficult to segment your data. Many tools include some default segments you can apply to any report you are looking at.For example when you look at your revenue or goal performance it takes a trivial amount of effort to look at All...<br/><div align='right'>2010-05-18 10:24:16</div>

Analyze This: 5 Rules For Awesome Impromptu Web Analysis

The hardest kind of "analysis" to provide is in response to open ended questions. That is why I love asking open ended questions!They expose a person's critical thinking ability (something I highly recommend you test when you hire web analysts: Interviewing Tip: Stress Test Critical Thinking. Please).They also help you understand if someone really grasps key concepts.Recently on behalf of Market Motive, my start up that focuses on online marketing education, I had the opportunity to offer one scholarship for the latest round of Master Certification in Web Analytics.So at the end of my 10 Fundamental Web Analytics Truths blog post I requested readers who were interested in the scholarship to complete this simple task:Pick a site you love and tell me three things you would change about it, and why.Seems straight forward right? It is not!First I must say that I was overwhelmed by the responses (thanks!) and I was impressed with the time people took to do the analysis. I got wonderfully created pdfs / Word docs and well written emails. I was amazed at the creativity on display (which validated the fact that I have chosen to be in the right industry!).Based on the responses, some wonderful and some not quite as wonderful (!), in this post I thought I'll share with you some tips should someone (like me!) ask you an open ended question ("what would you and why").The first part covers 5 rules, sourced mostly from what people did not do. The second part contains 4 things people did that delighted me.Let's go.When someone asks you an open ended question, at least connected to web analysis, here's what's important. . .1. Don't offer your opinion, at least not right away.This is a very very hard temptation to resist. But try.These were most common fixes people wanted to make on sites they loved:Remove big header I don't like the colors. I would change the entire site design. Reduce font size / increase font size. The font type is not great.I have to tell you that the last thing anyone wants to hear, in this context, is your opinion.Not your boss. Not your friend. Certainly not the HiPPO (Highest Paid Person's Opinion).Even if you believe that you are "absolutely right"! [Note: I often think I am "absolutely right". :)]You and I are poor proxies for the customer. And just because you don't like something… how should I put it so you'll understand…. oh let's try this…. you not liking...<br/><div align='right'>2010-05-04 10:44:22</div>

Web Analytics 101: Definitions: Goals, Metrics, KPIs, Dimensions, Targets

It is surprising how often these "simple" things come up."What is the difference between a metric and a key performance indicator (KPI)?""What is a dimension in analytics?""What is segmentation?""Are goals metrics?"And many more.There seems to be genuine confusion about the simplest, most foundational, parts of web metrics / analytics. So in this short post let's try and see if we can fix this really basic problem.Definitions and standard perspectives on these terms will be covered in this post:Business ObjectivesGoalsMetricsKey Performance IndicatorsTargetsDimensionsSegmentsA standard definition will be provided, but more than that my hope is to solidify your understanding with concrete examples and pictures.The post will end with a "Web Analytics Measurement Framework" – a very lofty name for something that will help you put your understanding of this post into practice.Business Objectives:This is the answer to the question: "Why does your website exist?"Or: "What are you hoping to accomplish for your business by being on the web?"Or: "What are the three most important priorities for your site?"Or other questions like that.Without a clearly defined list of business objectives you are doomed, because if you don't know where you are going then any road will take you there.The objectives must be DUMB: Doable. Understandable. Manageable. Beneficial.90% of the failures in web analytics, the reasons companies are data rich and information poor, is because they don't have DUMB objectives.Or they have just one (DUMB) Macro Conversion defined and completely ignore the Micro Conversions and Economic Value.Your company leadership (small business or fortune 100) will help you identify business objectives for your online existence. Beg, threaten, embarrass, sleep with someone, do what you have to get them defined.Point of confusion: People, like me, often also use the term Desirable Outcomes to refer to business objectives. They are one and the same thing.[Full disclosure: Depending on the specificity of your business objectives my asking you for your "desirable outcomes" could refer to "what are your goals". See below...]Goals:Goals are specific strategies you'll leverage to accomplish your business objectives.Business objectives can be quite strategic and high level. Sell more stuff. Create happy customers. Improve marketing effectiveness.Goals are the next level drill...<br/><div align='right'>2010-04-19 10:36:50</div>

Excellent Analytics Tip #17: Calculate Customer Lifetime Value

Some Marketers / Analysts use Click-thru Rate (CTR) to measure success of their acquisition campaigns. Nothing much to write home about, but certainly better than executing faith based initiatives.A smaller percent of those Marketers / Web Analysts will move beyond clicks and measure Visits / Visitors and Bounce Rates to measure success. Lovely, warm hugs and smiles for them.A fraction of those Marketers / Directors will calculate Conversion Rates for those marketing campaigns. They deserve our love. [And if they measure Micro Conversions they deserve our love AND respect for exhibiting savviness by using economic value.]But all of the above is still focusing on short term success. Even measuring Visitor conversion rates (Visit based conversion rates promote bad marketing behavior) is akin to declaring success after a one night stand.I reserve the best hugs, kisses, smiles, love, respect and my deepest admiration for Marketers and Analysts who use Lifetime Value computations!That is focusing on real success, not simply the first conversion (the one night stand!).That is focusing finding the customers that create value for the company, long term.That is truly doing the kind of Analysis Ninja work that solves tomorrow's problems today!For the above reasons I have been meaning to write a post on computing Lifetime Value for a very long time. But perhaps a better idea is to get an expert to do it, the result will clearly be far better than anything I would write. So I emailed my friend David. : )David Hughes is the Co-Founder of the email marketing consultancy called The Email Academy and the author of one of my most beloved phrases: Non-line Marketing! His blog, Non-line Blogging, is a favourite of mine.There are a handful of people in the world I could spend the whole day talking work and still have things left over to discuss, to learn. David is one of those super-smart, funny, and nice people. I have consistently found his ideas to be practical, grounded in common sense and instantly useful.I could not be more thrilled that he agreed to cover this tough, yet rewarding, topic.In this post David covers:Why Life Time Value is important (especially in context of Acquisition)How to optimally leverage value based segmentation & Lifetime ValueShare a sample analysis and, this is so sweeeet, a spreadsheet with a sample model that you can use to jump start your own LTV journey!Buckle up, this is going to be fun and it just might change your life!...<br/><div align='right'>2010-04-05 10:58:34</div>

10 Fundamental Web Analytics Truths: Embrace 'Em & Win Big

There are more mistruths and F U D about Web analytics out there than I think is reasonable.Part of it fueled by Vendors. What a competitive bunch!Part of it fueled by some Consultants. I suppose the rational is: self preservation before all else.Part of it is fueled by a vocal minority genuinely upset that 10 years on we are still not a statistically powered bunch doing complicated analysis that is shifting paradigms. They generally feel it is beneath them to use a standard tool, they push a utopian world that is hard for anyone to accomplish, including themselves, even after spending a minor fortune.This is sad. Even a little frustrating.My problem with these mistruths and FUD is that they result in a ton of practitioners and companies making profoundly sub optimal choices, which in turn results in not just much longer slogs but also spectacular career implosions and the entire web analytics industry suffering.Let's try to change that. If you agree to help I am confident we can accomplish a lot.Web Analytics, this beautiful child, was born just the other day in the midst of tumultuous times, quite literally, when everything changes every day. This constant evolution means that every time it learns how to do something the world changes around her and then it is on to learning the new things to stay relevant.It has simply not had a break to catch a breath and mature.And I doubt it is going to happen soon. The web is changing too fast. Too many new things are happening too fast and those of us charged with measuring it have to change the wheels while the bicycle is moving at 30 miles per hour (and this bicycle will become a car before we know it – all while it keeps moving, ever faster).Yet. Yet. Yet, yet, yet, yet…. there is so much we can do.Now.This instant.We can make use of what we have. Javascript tag driven click data processed in the cloud provided through a web based front end that allows you to segment and create meaningful views of the data unique to you.Even with the tools we have, in the state we have them, we can be smart. In fact smarter than you would be through any other channel on the planet!Don't fall for the FUD. See through the mistruths. Don't go down rabbit holes.The opportunity is too big for you to be distracted.In this blog post let me share with you some ground truths from my own humble experience. It's a bit of black and white in a world that admitted has lots of gray.My hope is that it inspires you....<br/><div align='right'>2010-03-23 10:48:23</div>

Kill Useless Web Metrics: Apply The "Three Layers Of So What" Test

Data, data everywhere yet nary an insight in sight.Is that your web analytics existence? Don't feel too bad, you share that plight with most citizens of the Web Analytics universe.The problem? The absolutely astonishing ease with which you can get access to data!Not to mention the near limitless potential of that data to be added, subtracted, multiplied, and divided to satiate every weird need in the world.You see just because you can do something does not mean you should do it.And yet we do.Like good little Reporting Squirrels we collect and stack metrics as if preparing for an imminent ice age. Rather than being a blessing that stack becomes a burden because we live in times of bright lovely spring and nothing succeeds like being agile and nimble about what we collect, what we give up, and what we deliberately choose to ignore.The key to true glory is making the right choices.In this case its making right choices about the web metrics we knight and sent to the battle to come back with insights for our beloved corporation to monetize.A very simple test can allow you to figure out if the metric you are dutifully reporting (or absolutely in love with) is gold or mud.It is called the Three Layers of So What test. It was a part of my first book, Web Analytics: An Hour A Day.What's this lovely test?Simple really (occam's razor!):Ask every web metric you report the question "so what" three times.Each question provides an answer that in turn raises another question (a "so what" again). If at the third "so what" you don't get a recommendation for an action you should take, you have the wrong metric. Kill it.This brutal recommendation is to force you to confront this reality: If you can't take action, some action (any action!), based on your analysis, why are you reporting data?The purpose of the "so what" test is to undo the clutter in your life and allow you to focus on only the metrics that will help you take action. All other metrics, those that fall into the nice to know or the highly recommended or the I don't know why I am reporting this but it sounds important camp need to be sent to the farm to live our the rest of their lives!Ready to rock it?Let's check out how you would conduct the "so what" test with a couple of examples.Key Performance Indicator: Percent of Repeat Visitors.You run a report and notice a trend for this metric.Here is how the "so what" test will work:"The...<br/><div align='right'>2010-03-08 10:18:50</div>

Kill Useless Web Metrics: Apply The ?Three Layers Of So What? Test

Data, data everywhere yet nary an insight in sight.Is that your web analytics existence? Don’t feel too bad, you share that plight with most citizens of the Web Analytics universe.The problem? The absolutely astonishing ease with which you can get access to data!Not to mention the near limitless potential of that data to be added, subtracted, multiplied, and divided to satiate every weird need in the world.You see just because you can do something does not mean you should do it.And yet we do.Like good little Reporting Squirrels we collect and stack metrics as if preparing for an imminent ice age. Rather than being a blessing that stack becomes a burden because we live in times of bright lovely spring and nothing succeeds like being agile and nimble about what we collect, what we give up, and what we deliberately choose to ignore.The key to true glory is making the right choices.In this case its making right choices about the web metrics we knight and sent to the battle to come back with insights for our beloved corporation to monetize.A very simple test can allow you to figure out if the metric you are dutifully reporting (or absolutely in love with) is gold or mud.It is called the Three Layers of So What test. It was a part of my first book, Web Analytics: An Hour A Day.What’s this lovely test?Simple really (occam’s razor!):Ask every web metric you report the question “so what” three times.Each question provides an answer that in turn raises another question (a “so what” again). If at the third “so what” you don’t get a recommendation for an action you should take, you have the wrong metric. Kill it.This brutal recommendation is to force you to confront this reality: If you can’t take action, some action (any action!), based on your analysis, why are you reporting data?The purpose of the “so what” test is to undo the clutter in your life and allow you to focus on only the metrics that will help you take action. All other metrics, those that fall into the nice to know or the highly recommended or the I don?t know why I am reporting this but it sounds important camp need to be sent to the farm to live our the rest of their lives!Ready to rock it?Let?s check out how you would conduct the “so what” test with a couple of examples.Key Performance Indicator: Percent of Repeat Visitors.You run a report and notice a trend for this metric.Here is how the “so what”...<br/><div align='right'>2010-03-08 10:18:50</div>

The Definitive Guide To (8) Competitive Intelligence Data Sources!

Competitive intelligence, the “what else”, is one of the core tenets of Web Analytics 2.0.The reason is simple: The ecosystem within which you function on the web contains mind blowing data you can use to become better.Your traffic grew by 6% last year, what was your competitor’s growth rate? 15%. Feel better? : ) When should you start doing paid search advertising for tours to Italy for 2011? In May 2010 (!). What is your “share of search” in the netbook segment compared to your biggest competitor? 9 points higher, now you deserve a bonus! How many visitors to your site go visit your competitor’s site right after coming to yours? 39%, good god! Where to do display advertisements to ensure you get in front of men considering proposing to their girlfriends (or boyfriends)? Go beyond targeting men between the age of 28 and 34, use search behavior and be really smart.I am just scratching the surface of what’s possible.It is simply magnificent what you can do with freely available data on the web about your direct competitors, your industry segment and indeed how people behave on search engines and other websites.The secret to making optimal use of CI data lies in one single realization: You must ensure you understand how the data you are analyzing is collected.Not all sources of CI data are created equal. It is key that before you use the data that comScore or Nielsen or Google or HitWise or Compete or your brother-in-law shove into your face that you understand where the data comes from.Once you understand that you choose: 1. The best source possible that is 2. The right answer for the question you are asking (which implies you have to be flexible!).Here are the sources of competitive intelligence data.#1: Toolbar Data.Toolbars are add-on’s that provide additional functionality to web browsers, such as easier access to news, search features, and security protections. They are available from all the major search engines such as Google, MSN, Yahoo! as well as from thousands of other sources.These toolbars also collect limited information about the browsing behavior of the customers who use them, including the pages visited, the search terms used, perhaps even time spent on each page, and so forth. Typically, data collected is anonymous and not personally identifiable information (PII).After the toolbars collect the data, your CI tool then scrubs and massages the data before presenting it to you for analysis. For...<br/><div align='right'>2010-02-22 10:19:12</div>

The Definitive Guide To (7) Competitive Intelligence Data Sources!

Competitive intelligence, the “what else”, is one of the core tenets of Web Analytics 2.0.The reason is simple: The ecosystem within which you function on the web contains mind blowing data you can use to become better.Your traffic grew by 6% last year, what was your competitor’s growth rate? 15%. Feel better? : ) When should you start doing paid search advertising for tours to Italy for 2011? In May 2010 (!). What is your “share of search” in the netbook segment compared to your biggest competitor? 9 points higher, now you deserve a bonus! How many visitors to your site go visit your competitor’s site right after coming to yours? 39%, good god! Where to do display advertisements to ensure you get in front of men considering proposing to their girlfriends (or boyfriends)? Go beyond targeting men between the age of 28 and 34, use search behavior and be really smart.I am just scratching the surface of what’s possible.It is simply magnificent what you can do with freely available data on the web about your direct competitors, your industry segment and indeed how people behave on search engines and other websites.The secret to making optimal use of CI data lies in one single realization: You must ensure you understand how the data you are analyzing is collected.Not all sources of CI data are created equal. It is key that before you use the data that comScore or Nielsen or Google or HitWise or Compete or your brother-in-law shove into your face that you understand where the data comes from.Once you understand that you choose: 1. The best source possible that is 2. The right answer for the question you are asking (which implies you have to be flexible!).Here are the sources of competitive intelligence data.#1: Toolbar Data.Toolbars are add-on’s that provide additional functionality to web browsers, such as easier access to news, search features, and security protections. They are available from all the major search engines such as Google, MSN, Yahoo! as well as from thousands of other sources.These toolbars also collect limited information about the browsing behavior of the customers who use them, including the pages visited, the search terms used, perhaps even time spent on each page, and so forth. Typically, data collected is anonymous and not personally identifiable information (PII).After the toolbars collect the data, your CI tool then scrubs and massages the data before presenting it to you for analysis. For...<br/><div align='right'>2010-02-22 10:19:12</div>

Analysis Ninjas: Leverage Custom Reports For Better Insights!

Here is a key difference between Reporting Squirrels and Analysis Ninjas: The latter almost exclusively leverage custom reports (powered by advanced segmentation) and the former flirt with one standard report and then another and then other and in the best case scenario pull only half of their hair out.There is nothing particularly wrong with the standard 19,000 reports in your web analytics tool. But they do represent the Vendor’s best guess about what you should look at. Sometimes they even get it right.Most of the time though your business is absolutely unique (even as it exists amongst hundreds of competitors) and it is absolutely important that you take your web analytics tool and mold it around you. The the power that is given to you even in free tools like Yahoo! Web Analytics and Google Analytics and create a view of data that will help you find faster insights.This post is inspired by a suggestion from Horia Neagu in reply to my tweet asking for blog post ideas. My thanks to Horia.Horia’s question was: How about a post entitled “10 Google Analytics Custom Reports You Absolutely Must Set Up”?I am not going to write about that, simply because the very idea that a report is custom means that there are probably no “ten standard custom reports” to set up.I am going to share one recommendation and two ideas for making your own custom reports better.This is a “teach a person to fish” type post. Sorry. :)No Goals, No Glory.Here’s a cliché: If you don’t know where you are going, any road will take you there.Nowhere is this more applicable than when it comes to trying to find insights from your data you can action.You report your poor heart away, no one seems to be able to take anything you give and take action.Often it is the case that you and I have not bothered to sit down with he HiPPO / the boss’s boss and tried to understand what in the name of all that is holy and pure is our website trying to do!What are the goals?No custom report (or advanced segment, the life giving oxygen) was ever created without an answer to that question.So ask that question. Get an answer before you go about your customization ways.If your leaders / clients truly want wisdom from you they will answer the question. But it does happen sometimes that begging or throwing yourself at her/him does not elicit anything of value.In those rarest of rare cases (after you have already submitted your resume to other...<br/><div align='right'>2010-02-01 11:01:52</div>

Dear Avinash: Search / SEO Metrics & Analytics Questions + Answers

How do you measure success of a online webinar?I recently did a webinar for the Search Engine Strategies conference (I am doing the opening conference keynote at SES London and SES New York) and my Market Motive co-faculty member Greg Jarboe sent me this KPI via email:“Your webcast was a big success. Your KPI questions per attendee was off the chart!”I don’t know why I had not thought of this wonderful KPI. So much better than # of attendees.As always though context is king.It could be a good thing (“you were great, engaged the audience”) or a not such a good thing (“no one understood a thing you were saying, hence so many questions”). Only upon reading the actual questions could I figure out which case it was (mercifully case #1 for me!).End of a minor web analytics lesson on going beyond obvious metrics and never, ever, never forgetting context.Back to our story. . . an hour is too short a time to answer all the questions (even in a webinar just focused on attendee questions). So here is a small selection from the 80 questions I could not answer in the wide ranging webinar. We will cover measuring success of SEO efforts on one web page, how to do search engine optimization for b2b websites, how to rank for highly saturated industries / categories / keywords, and which competitive intelligence tools do I use for search program optimization (and targeting display ads using search data!).I hope you all find the answers to be of value.#1. How do you measure SEO performance on a page level? I’d like to know how well my seo efforts for a particular pages have performed.Every measurement question should start by taking one step back and thinking of goals.In this case here are some obvious ones:Uno: You want to get a lot more traffic to the page from search engines.Dos: You want that traffic to come on the optimal set of keywords (why simply bounce traffic?).Tres: For both of those things to happen, you want the page to be indexed by the search engines and finally. . .Cuatro: You want to earn a bonus for yourself so you want the page to make money (e-commerce sites) or add economic value (non-ecommerce websites) for your company/website.Now it is not hard to figure out how to measure performance! [Before you do any kind of measurement please consider going through the above exercise. It is simple, effective and works like a charm - not to mention allows to get going faster.]Before you analyze do one small thing....<br/><div align='right'>2010-01-20 09:47:01</div>

Five Sweet Web Analytics Resolutions To Kick It Up A Notch

The new year is such a wonderful time. Wonderful smells in the air. The world is full of hope. Unachievable things seem achievable and are being polished into shiny resolutions. World peace seems within grasp.As we spring to action full of passion I wanted to share with you all a short list of things that will expand your little world of online marketing & web analytics.We all have a tendency of getting caught in a rut, using the same tool to do the same things and spew forth the same data. Change is hard, even if we know that we should be executing a multiplicity strategy to win in the web analytics 2.0 world.Before all the excitement of the new year wears out, here are five simple things I would love for you to try so that your company will have a glorious truly data driven 2010!#1: Don’t suck.Seems obvious. And yet in our quest for ever more hard problems to solve we forget that the number one goal of every website is not to suck. Especially at the really simple and basic things.At a recent conference there were three keynotes.One was extolling the wonderfulness of their multi channel campaign tracking. When I went to their website it was a 100% flash website with a constrained small size where it took too much looking to click on anything and then too much scrolling to read anything and unclear calls to actions (if any). That’s sucking. No amount of great multi channel tracking will save this company, they suck at the basics.The second was about predictive analytics and how using massive integrations between online and offline databases they had accomplished some really cool reporting of data (and make no doubt the IT work done over 18 months to accomplish this was cool). Their home page is a mess. 24% of the content covers what any visitor might want, rest is the company shouting at you (in many annoying ways). That’s sucking.The third was about how to create data driven cultures and how this person had created a impressively big cross functional team across multiple countries and standardized on Omniture after a lot of work over two and half years. I did a search on some of their products and they did not have page one search listings (on Google or Bing) for what should be their head terms. (That’s sucking.) They did have PPC ads, which I click on the ad for specific product they land me on generic nonsense pages. That’s sucking.I share these stories to illustrate vividly how we in the web analytics world get lost in...<br/><div align='right'>2010-01-06 10:13:37</div>

Analysis Ninjas: Move Beyond The Top Ten. Find Love (/Insights).

You know what is the one thing stopping you from finding truly actionable insights from your web data?Web analytics gems lie deep in the data and we spend our lives looking at the top ten rows of data.It does not matter which report you look at. Affiliates. Products sold. Referring URL’s. Pages viewed. Search keywords. Promotions. Geographies. Really pick any report with any dimension you want to look at, we spend our time (and valuable space on our dashboards) looking at the top ten.We look at the top ten rows of data because:1. Too much data from our web analytics tools.2. Lack of clarity from our business leaders about what the site is solving for.3. Not enough hours in the day to overcome challenge #1 and #2.But if you just look at the top ten rows of anything here are the two corrosive problems:1. The top ten of anything rarely changes (with the exception of hourly changing content – news – sites).2. The top ten only focuses on the head, while the magic is in the long tail of anything. Magic related to finding challenges in your business. Magic related to finding opportunities. Magic that will help identify things you can actually action.Allow me to make the case for you to look beyond the top ten rows in your reports by sharing three short stories. In each case I request you to look beyond the specific request and tool, rather focus on the analysis and how you could possibly apply it. I hope is to inspire, not to prescribe.For example take a look at this report. . . .I am sure when you look at it now it appears all mysterious, full of potential. You can’t wait to take it out for a first date and then another and by the third date if you do the same old thing it gets boring. You are done looking at the bounce rates and time on site and conversions of these keywords. In the best case scenario you have even optimized landing pages. Good.The first week’s over, now want? Why keep reporting the top ten keywords on you Executive Management Global KPI Dashboard?Look at the top of that table. For this website 86,837 visits came from 8,939 keywords!What’s going on with the other 8,929 keywords?We never bother with them both because it is really hard to look at more than 10 rows of data. Harder still to look at 30 or 50 or 70 rows of data. Not only do we have a hard time interpreting insights from lots of data, we can’t actually physically look at that much data and find insights.Last month this blog received 40,662...<br/><div align='right'>2009-12-21 13:31:07</div>

Who Owns Web Analytics? A Framework For Critical Thinking.

It is rare for me to work with a organization where the root cause for their faith based decision making (rather than data driven) was not the org structure.It is almost never tools. Not any more.Surprisingly it is often not their will to use data, that is there in many cases.Sometimes it is that they don’t follow the 10/90 rule.It is always the organization structure.Specifically: Who owns web analytics / who it reports to from a org structure perspective.[Let me hasten to add that this, web analytics ownership, does not exist in a vacuum. If your overall web business is misaligned from an org perspective then honestly there is no hope for you, regardless of where analytics sits.]This is a topic I cover in my new book, Web Analytics 2.0. Chapter 14: HiPPOs, Ninjas, and the Masses: Creating a Data-Driven Culture.In this blog post I’ll share a unique “case study”, more like one person’s problem, and my advice to them about how to think about the organization problem.Here’s the question / challenge:I?m facing an issue I?m sure many large organizations struggle with: where should an organization place its web analysts? Currently, I lead a small team of analysts at a medium-sized bank. We are part of the Web Sales division, along with an e-commerce (online media) team and the content crew.Web Sales is considered a channel in the same way our call-centre, local branches and customer account managers are. As such, we are not a part of the central Marketing (and Marketing Intelligence) teams at corporate. I see a few different options but would be happy to hear your opinion.You will all agree that it is really hard to answer a question like the one above without spending time with the company and understanding its strengths and meeting the political players involved.In this post let me share with you a common sense framework I use in my consulting engagements to figure out a home for web analysts.Each facet of the framework also contains a peek into what I am thinking, best practices I have developed from all the bruises I have (as a Practitioner and a Consultant) and how I end up making the choices I do. I hope it is of value to you all (and now you don’t have to pay me large sums of money to do this for you!).The four pronged real world tested probing and loaded with politics framework to find a home for Web Analytics:1. How long has the company been doing web analytics, what is the landscape of tools?Are there standard...<br/><div align='right'>2009-12-09 10:30:30</div>

Social Media Analytics: Twitter: Quantitative & Qualitative Metrics

Twitter is amongst new media channels that are challenging how we communicate, with whom we communicate and perhaps most fundamentally how we (Marketers) influence people.Analysis of these new social media channels has been hobbled by old world thinking, when it comes to marketing, from the world of Television and Magazines or, when it comes to measurement, from the world of traditional web analytics.These new channels, twitter and facebook and youtube and tumblr and, yes, even blogs, are very distinct customer / participant experiences. Stale marketing or measurement thinking applied to them results in terribly sub optimal results for all involved.So in this post my hope is to share with you what is unique about measuring one such channel, Twitter. The blog post is also sprinkled with my own words of folksy wisdom as to how you should use the channel for maximum impact.My new book Web Analytics 2.0 also covers social media measurement, but I am going to cover something very different in this post.First: An Ode to New Thinking:One common thing between the all tools in this post is that they were built by “outsiders”.One of the things I love and adore about Twitter (besides all that connection and conversation) is how it has completely lit a fierce fire of innovation when it comes to analytics. Anyone and their brother and ma-in-law can develop a tool, and they have! Much to the benefit of the rest of us.Perhaps the most beneficial thing to me is how much out of the box innovation this has brought.For example just look at traditional web analytics tools, there is absolutely no fresh thinking when it comes to Social Media Measurement. The in their thinking from all the constant “let’s figure out how to collect and report every more data and not bother with truly immersive understanding of these channels and what makes them unique”.While there is some stale thinking in the new twitter tools, most of them have a lot of fresh thinking from people untainted by Omniture or CoreMetrics or WebTrends or, ok ok ok, Google Analytics.I consider this massive proliferation of new thinking to be a gift from God.To all of you developers who are toiling out there, you have my love and gratitude.In this post four twitter analysis tools. Each while not yet all developed yet show sweet signs of1. Truly understanding the medium and uniqueness of the channel and2. Are not just reporting “hits”, rather coming up with clever...<br/><div align='right'>2009-11-24 11:28:44</div>

Social Media Analytics: Twitter: Quantitative & Qualitative Analysis

Twitter is amongst new media channels that are challenging how we communicate, with whom we communicate and perhaps most fundamentally how we (Marketers) influence people.Analysis of these new social media channels has been hobbled by old world thinking, when it comes to marketing, from the world of Television and Magazines or, when it comes to measurement, from the world of traditional web analytics.These new channels, twitter and facebook and youtube and tumblr and, yes, even blogs, are very distinct customer / participant experiences. Stale marketing or measurement thinking applied to them results in terribly sub optimal results for all involved.So in this post my hope is to share with you what is unique about measuring one such channel, Twitter. The blog post is also sprinkled with my own words of folksy wisdom as to how you should use the channel for maximum impact.My new book Web Analytics 2.0 also covers social media measurement, but I am going to cover something very different in this post.First: An Ode to New Thinking:One common thing between the all tools in this post is that they were built by “outsiders”.One of the things I love and adore about Twitter (besides all that connection and conversation) is how it has completely lit a fierce fire of innovation when it comes to analytics. Anyone and their brother and ma-in-law can develop a tool, and they have! Much to the benefit of the rest of us.Perhaps the most beneficial thing to me is how much out of the box innovation this has brought.For example just look at traditional web analytics tools, there is some fresh thinking but not a lot of it. There is a staleness to their thinking from all the constant “let’s figure out how to collect and report every more data and not bother improving analytical horsepower or truly emerging new mediums“.While there is some stale thinking in these new tools, most of them have a lot of fresh thinking from people untainted by Omniture or WebTrends or, ok ok ok, Google Analytics.I consider this massive proliferation of new thinking to be a gift from God.To all of you developers who are toiling out there, you have my love and gratitude.In this post four twitter analysis tools. Each while not yet all developed yet show sweet signs of1. Truly understanding the medium and uniqueness of the channel and2. Are not just reporting “hits”, rather coming up with clever metrics.Quantitative Metrics / Analyses.Most twitter analytics...<br/><div align='right'>2009-11-24 11:28:44</div>

Web Analytics 2.0 Book: In Stores Now!!

I am absolutely thrilled that my book Web Analytics 2.0 has been released and is in retail stores now, online and offline! Hurray!!Even with a broken right hand I can’t help but write this post!The waterfall of positive feeling stems from the fact that this book was very hard to write.I only had one job, at Intuit, when I wrote my first web analytics book. I now have several full time jobs, plus this blog, plus speaking around the world, plus a family, plus… so much more.It took weekends of writing and nights of editing and days of research combined with practicing the preaching by doing oodles of analysis and, more importantly, the support of the most understanding wife in the world.At the end of it all it is rather gratifying to see one’s book at a bookstore, helps grasp the magnitude of the process. And there’s absolutely nothing quite like hearing your five year old yell in a busy Borders bookstore: “I FOUND DADDY’S BOOK!”This blog post is in three parts: The pitch. Request for help. A lovely contest.You don’t have to read the whole thing & skip ahead, but that would hurt my feelings. :)Here we go. . .The Pitch:I invite you to consider buying my second web analytics book. It is not only the most current book on everything important and bleeding edge in Web Analytics, it is a labor of love that will help you transform your personal thinking and assist in revolutionizing your organization (big or small).It is not a technical book, though it will make you technically dangerous. It is not just a business book, though every dna strand in this book is more about online marketing than online analytics. It is not a hard book to read, though it is brain food.Here’s why I think you’ll love it:Chapter 1 The Bold New World of Web Analytics 2.0No dragging of the feet, the book starts with a bang by laying out the framework that will be the center of every company that will leverage data (qualitative, quantitative, competitive) on the web. It ends with a challenge to embrace Multiplicity – without this it’s goodbye greatness.Chapter 2 The Optimal Strategy for Choosing Your Web Analytics Soul MateIt will be hard for you to find a more compelling four step process to choose the right web analytics tool for your company. Soul searching, questions to torture vendors with, comparing vendors, running a pilot and negotiating a contract, it’s all in there. You be off to the races right.Chapter...<br/><div align='right'>2009-11-13 10:38:17</div>

Analytics Becomes Intelligent. Hello Insights!

A while back I walked into a meeting and said:“You know what… web analytics tools like Site Catalyst, Yahoo! Web Analytics, WebTrends, and yes even Google Analytics, are mostly glorified data pukers. Each tries to outdo the other in trying to collect ever more data and regurgitating it. For all the math they do, it is astonishing how little intelligence they have, how little actual smarts are applied.”Silence for a a few mins.Awkward glances.Then this: “What do you mean, and what can we do?”Me: “I wish the tools would use an algorithmic approach to highlight the things an Analyst needs to know, give ‘em some starting points. Why make people dig for hours?”You have to hand it to the team at Google, you “provoke” them and they respond. : )Today Last week the Google Analytics team announced a raft of sweet features that take the current functionality in GA, wrap a liquid hydrogen fuel tank on it and shot it into a higher value orbit. Take some time to learn more about how you put more power behind your analysis punch: Google Analytics Now More Powerful, Flexible And Intelligent.In this post I’ll want to share rest of the story, what came of the above provocation.The first thing you’ll notice in Google Analytics is a new cool ability to better identify the “known unknowns”, i.e. we know what we want to know, but we don’t know if and when it is happening.The feature is, rather cutely, known as Custom Alerts.Here’s an example. Everyone tells me that Twitter is nothing but hype. But[sidebar] i started to write this post in preparation of the GA new features launch, unfortunately the next day i broke my right hand. that meant going to emetrics to do the announcement in a temporary cast, and of course no blog post. i had surgery this past thu. metal plate and some screws in, things will be normal in a few weeks.i unfortunately still can’t type the thoughtful teachable post i had in mind, rather here are two videos that tell you about two features i am really proud of. hope you’ll love ‘em as well. [/sidebar]custom alerts: identifying the known unknownsvideo: 8 mins:sweet? : )intelligence: identifying the unknown unknowns!video: 16 mins:love it?i hope you had fun learning a bit more about these two cool features. promise me you are going to set up two segmented custom alerts today!let me answer one question that might be top of mind: the features are...<br/><div align='right'>2009-10-26 09:24:36</div>

Web Analytics Success Measurement For Government Websites

If you know what the desirable outcomes are from your website, it is not hard to measure performance of the website for you and your customers.Measuring top line success of ecommerce websites is not very complicated, all the sweet revenue based outcomes are there (at the least).Measuring non-profit websites is a bit complicated, but not really all that hard because we can, with a small amount of love, figure out outcomes to focus on (donations, # of sign-ups for the protest in DC, # of petitions signed, volunteer applications, etc).Measuring government websites is a bit more complicated, if for no other reason than that it takes a pinch of effort with a dash of imagination to figure out what one is solving for. What are the desirable outcomes one can focus on to measure success?The above question came to mind from a kind note I got from Ines Jans who is a part of the team that is responsible for www.belgium.beInes and team were just starting to think about analytics (because the love their customers!) and asked for some thoughts.My first question to Ines was, (surprise!):Q: Tell me a bit more about what your site does, like what are the real goals (or give me some ideas about it) and what challenges you face, what do you expect people to get out of it?[Best Practice: Always, always, always start any measurement conversation with the above inquiry. The answer will be key to insights, and without it you'll simply be a glorified Reporting Squirrel.]The answer, which might fit most government websites was:A: The goal of our site is to be a portal to all the official information there is about Belgium and make information easy to find. Visitors should be able to figure out which Ministry is responsible for what tasks.[Best Practice: Don't be surprised in your Analysis Ninja quest if you get answers that just start the conversation, rather then give you a prescription for what you need. Squirrels will despair here, but Ninjas will take clues from what they hear and visit the site and come up with a set of important measurable outcomes.]Based on the answer above and some time spent on the English language site as well as those in other languages (Google Translate!), I came up with the following five questions I could ask data to measure success.~ Are Visitors able to find the information they are looking for?~ Are the Visitors satisfied with their experience?~ What is the most popular content on the site? What area can we prioritize higher than it currently is?~...<br/><div align='right'>2009-10-12 10:45:59</div>

Brand Measurement: Analytics & Metrics for Branding Campaigns

One of the ultimate excuses for not measuring impact of Marketing campaigns is: “Oh, that’s just a branding campaign.”Admit it, you’ve heard it.I suspect you’ve even used it liberally!! : )Before we go any further I must clarify that I love branding campaigns just as much as the next guy.I love campaigns that Visa runs. I love watching the IBM ads (with the Linux kid perhaps the best of the lot). I loved the I’m a PC ads from Microsoft (and I am a proud PC!). I loved the Wario Land: Shake It ad from Nintendo on YouTube (now that’s creative!). I love a good billboard ad, Budweiser does good ones. My absolute favorite branding campaign of all time: Think Different.I could keep going on.The common theme through the above campaigns is that their primary purpose is “branding”. The hope is by connecting with you, or interrupting you, a lasting impressing, a feeling, might be left in you so when its time to get a credit card you think of Visa and not MasterCard, when it comes time for hiring consultants for a multi year project you’ll choose IBM and so on and so forth.All well and good.Here is the minor problem.There is a very tenuous connection between these campaigns and outcomes, they are for the most part faith based initiatives. If supported by “data” then it tends to be of the most fragile kind (usually the the fact that the CEO saw it during the Super Bowl and felt happy suffices as actionable data).None the less they persist.Online it does not have to be that way.It is criminal not to measure your direct response campaigns online. I think we have established that. I also believe that a massively under appreciated opportunity exists to truly measure impact of branding campaigns online. Paid Search or affiliates or email or display or YouTube or whatever channel you end up choosing.[Oh and don't tell me that your "branding" campaign is to increase "engagement"! Remember: Engagement is not a metric, its an excuse.]The Top Secret Hidden Never To Be Reveled Come Hell Or High Water Key To Measuring Branding Campaigns:Answer this simple question: Why %&#$!^ are you doing the “branding campaign”?Every campaign, and in turn website has a purpose. All you need to do is figure out what the purpose of your campaign is, no matter how outlandish (or childish) your goal.The typical focus by companies, and the creative types in their employ, is to simply focus on figuring out what you...<br/><div align='right'>2009-09-29 10:34:04</div>

Web Analytics Books!

Yes, books with a s. : ) It is with immense excitement that I am sharing the news that I have just finished writing my second book! Web Analytics 2.0: The Art of Online Accountability & The Science of Customer Centricity It is a long title ain’t it? The good news is we are going to refer to it simply as Web Analytics 2.0. In this post I wanted to share thoughts about the book, the process of writing it (and doing three rounds of edits!) and outcomes. The Background Since mid-2008 Willem Knibbe, my wonderful Acquisition Editor at Wiley, was very kindly encouraging me to update my (best selling!) first book, Web Analytics: An Hour a Day. The “problem” was the book continued to sell at a nice rate and I was not sure what to update because 90% of the content was still current and relevant. Still there was a lot of new stuff I had written, new models I had developed, new and more advanced techniques, new problems we were dealing with in the world and so on and so forth. That lead to my proposal to Willem to write a new book that would use Web Analytics: An Hour a Day as a starting point. The second book would be an advanced book that would allow the first book’s readers to truly become Super Analysis Ninjas, and for those that had not read the first book to have the finest possible immersion in web analytics. And that’s just what Web Analytics 2.0 is. The 2.0 Book The book’s core philosophy is based on the framework you have seen me talk about on this blog. . . the quest to answer four key questions: the What, How Much, Why, and What Else. . . The awesome thing about writing a advanced book is that I can start with a bang! No history and what not. It starts with: Here is how your world should look like and this is why its important, now let’s get down to business. That’s by page 9. : ) And then it just keeps kicking it up a notch. Bam! Bam! Bam! Like the first book this is not a book about Omniture or Xiti or Google Analytics. It is not a “press this button in the tool and then press that one” book. It hopes to be brain food. Here is how you should think. Here are the traps to avoid when picking key performance indicators. Here are the core analytical techniques you should apply. Here are a bunch of reality checks. Here is how to embrace outcomes, regardless of the size of business you have. Here is how to achieve higher highs with testing and by listening to customers (literally). Here is how...<br/><div align='right'>2009-09-14 11:07:05</div>

Six Tips For Improving High Bounce / Low Conversion Web Pages

In my travels around the world the most frequently asked question is: “What’s your favorite web analytics report? “ A close second is: “How can I improve my web pages with high bounce / low conversion rates?” Or “I have done all I can and I don’t know how else to improve my webpage, ideas?” If you think about it for a moment it is not a very hard question. I believe the insights for improvement exist at the intersection of customer intent and the webpage’s purpose. Let me explain. The Customer Intent – Webpage Purpose Gap There is a very simple reason many websites and web pages have a very high bounce rate, and in turn very low conversions… There is no connection between why the customer came to the page and what the page exists for. This could be someone typing in vegetarian shoes into Bing and landing on your web page for swim suits (as happened to me recently). This could be me visiting www.couponcabin.com and clicking on a $10 off a $35 coupon link to Snapfish and landing on a page for “great new gifts” or, the other day, landing on a page that said “Get a free deck of cards”. What! Where’s the scent? Never let your campaigns write chq’s that your website can’t cash. Fix that, your outcomes (revenue, leads, donations…) will improve. The second type of problem is a lot more common… There is some overlap between what your customers want and what your web page exists to do. But the overlap is not very much, only the most dedicated (say your mom) will put up with the pain required to complete the task. I land on your site to buy QuickBooks Simple Start but you have it very well hidden because you want me to buy the $400 QuickBooks Premier product. This could be an email campaign you sent me and the landing page does not have a one click checkout link, though it does have a ton of irrelevant content. This is every site with a painful flash intro, this is pretty much every site ever created by every big CPG company, this is Propel Water’s website where the only reason for your existence is to be impressed by a slow site with a dancing water bunny that hops! What rarely happens, and what we should all aspire for, is this… Not only is there a large overlap between Customer Intent and Webpage Purpose, the company’s own objectives are subservient to customer needs. That’s how you get to nirvana. That’s how you...<br/><div align='right'>2009-08-25 10:11:29</div>

Web Analytics Career Advice: Play In The Real World!

Interviewing candidates for a “data job” (analysts, marketers, ppc specialist) can be surprisingly depressing. Sometimes they can be unqualified. Usually they are “qualified”. The depression comes from this singular flaw: The candidate’s education is limited by the companies they work/worked at. All I know is ecommerce because that is all my company does. All I know is lead gen because that’s my world. All I know is PPC because my job involved just Search. All I know is B2B because that’s my company’s vertical. These are summaries of the excuses I hear. They don’t actually use their words, but it takes 10 mins of questions for that essential summary to emerge. These excuses are extremely corrosive and and sadly indicate how the candidates have allowed their environment to limit their full potential, stunt their professional growth. Here’s some bad news: Companies will never give you the time to truly learn and grow. Sometimes they explicitly won’t give you the opportunity, at other times they will give you the opportunity (and even some funding) but you still have your daily work load and you don’t take advantage. Here’s a news flash: The world around you is always changing and growing. If you don’t keep pace, you become stale. Quickly. So? Here’s my recommendation:Step out, take charge of your own learning. Why let your employer take you down? Why let them add just tactical experience to your resume? Why let their online tactics limit your growth? So what to do? My own learning about web analytics truly transformed after I started my blog. The total cost was $65 (five bucks to buy a domain and five bucks a month to host it with a ISP). Web Analytics Education. Just writing a few simple posts a month got a couple thousand page views a month. That was more than enough for my blog to become my learning platform, a place where I could implement web analytics tools, get to play with real world data and educate myself. In the last couple of years I have implemented atleast 25 analytics tools on my blog. In fact at this very moment here are the tools implemented on my blog: ClickTracks, Percent Mobile, TigTags, Urchin, StatCounter, Yahoo! Web Analytics, Xiti, GoingUp, Statsit and CrazyEgg. I have learned so much about implementation, customizing data capture, data analysis, and tracking challenges. Having all these tools on my blog, or having them on your blog, means that...<br/><div align='right'>2009-08-10 11:45:50</div>

This I Believe [A Manifesto for Web Marketers & Analysts]

National Public Radio had a long running show called This I Believe. In it a person shares their private philosophy. Each story is extremely inspiring and I am a huge fan of the show. A couple of my favorites: Muhammed Ali, and from 1953 Margaret Chase Smith. I wish I had something personal that was insightful enough to share. I don’t. But I had the program This I Believe on my mind when last year I was asked to write something for the Korean version of my book, Web Analytics: An Hour A Day. Here’s my little professional manifesto of a few things I believe….   I have to admit that I love the web. Love, love, love. It’s quite simple really, in our humble history there are few things that have transformed human beings in the way that web has. And to think that it is still just a baby. I am amazed at how it has democratized access to information, it has allowed the little guys compete with the big guys, it has created opportunities for people and businesses where none existed. The web also provides a unique and wonderful opportunity to understand our customers (visitors to our websites), to learn from their actual behavior and transform how we do business, to achieve the near impossible: improve satisfaction and revenues! Here are five things I believe in when it comes to achieving success with web data: 1. I believe in the power of people (your employees), not tools. I have a 10/90 rule . If your budget is $100 then spend $10 on tools and professional services to implement them, and spend $90 on hiring people to analyze data you collect on your website. The web is quite complex, you are going to access multiple sources of data, you are going to have to do a lot of leg work. Blood, sweat and tears. You don’t just need tools for that (remember 85% of the data you get from any tool, free or paid is essentially the same). You need people! Hire the best people you can find, tools will never be a limitation for them. 2. I believe that reporting does not equal analysis. Most “Analysts” and Marketers think that their job is to simply churn reports out and drop them over the fence and that’s enough to create data driven cultures. False. Reporting is the act of providing data. Analysis is the act of providing actionable information. No company will succeed by having a army of report writers, they will succeed by having a small group of “Analysis Ninja’s ” who transform data into information....<br/><div align='right'>2009-07-27 11:23:25</div>

Barriers To An Effective Web Measurement Strategy [+ Solutions!]

I was quite impressed by the Econsultancy’s Online Measurement and Strategy Report. Many Analyst “reports” tend to push a company / vendor / consultant agenda, refreshingly the Econsultancy report did not. They asked a wide spectrum to actual customers and reported the reality on the ground. They had some biting, but fair, observations about short comings of Google Analytics. I appreciated that very much. But the most valuable part for me was section 6.7.2. It was a listing of 11 barriers to an effective online measurement strategy. 11 painful reasons why extracting value from web analytics is still worse than attempting to climb Mt. Everest for some of the top companies. Curious? Here they are: Lack of budget/resources (45%) Lack of strategy (31%) Siloed organization (29%) Lack of understanding (25%) Too much data (18%) Lack of senior management buy-in (18%) Difficulty reconciling data (17%) IT blockages (17%) Lack of trust in analytics (16%) Finding staff (12%) Poor technology (9%) Makes for a slightly depressing read does it not? Many, if not all, of these challenges are really hard and often the solutions are unique to each company. In as much it would be impossible to write a here is how you fix it all blog post. Rather I am going to try and share some thoughts / ideas on that will atleast help you take step one. I very much encourage you to share your wisdom with us through comments. First: A Brilliant Insight / Borderline Rant. : ) Before we go on nothing something absolutely astonishing…. we live in a culture where every Analyst, Blogger and Consultant is writing / posting / talking / presenting comparisons on web analytics tools. We can’t seem to take one step without stepping into one more pile of opinions about why this tool is great and what one is bad. Yet the top ten barriers have absolutely no connection to features, and barely have any connection to tools. Its #11. An afterthought. I wonder why we are not writing / posting / talking / presenting on how to solve these non-tool problems, things that actually matter to companies and practitioners in the real world. Just because we are programmed to publish reports comparing tools? Tools can provide a marginal advantage to a company of any size. But given where we are in our evolutionary stage we have much bigger fish to fry. I hope its out with lets drop our clothes and compare sizes and in with adding real value to practitioners by focusing on issues like the...<br/><div align='right'>2009-07-13 11:49:57</div>

Paris Hilton, Kim Kardashian & Telling Stories With Data

It is such a cliché: Don’t just present data, tell a story. Yet it is rarely followed. We almost always present data. Actually we don’t present data, we send out reports. With data. Lots of it. With 6 size font and some pies and stacked bar graphs thrown in. Then we are frustrated that no one seems to pat us on the back, sing songs in our glory, give us more money. We don’t truly tell stories because it seems like a lot of work. And it can be. But you’ll be surprised at how often it is simply a matter of framing things differently, letting your imagination roam free. Last month I had to present to a group of executives in New York. One of the key things I wanted to communicate was the power of not doing random advertising but rather using freely available data to target the advertising on sites where relevant audiences exist. Goals Summary: 1. Show the power of free tools available. [Google's Ad Planner specifically.] 2. Highlight the importance spending money on advertising to relevant audiences. 3. Tell a memorable story. Below is how I did it. . . . hopefully it will inspire you to look for stories in your data, stories that will hold interest and might even get you some smiles (and you know that a raise is not far behind!). My first step was to try and tap into current events / pop culture. That calls for some research. I use Google Insights for Search as the best way to get a pulse on what people find interesting. Specifically what I often do is run this query: Who are the most popular celebrities in New York in the last 30 days? Turns out it is someone called Kim Kardashian. It also turns out I have no idea who this person is, an unfortunate side effect of not have time to watch television. Quick Google search and I am caught up on why Ms. Kardashian is “famous”. She has some overlap with Paris Hilton in terms of the path to fame. The key ingredient for any story is to have interesting protagonists. For this story due to their popularity it will be Ms. Hilton and Ms. Kardashian. The plot: Your business has a need to market something related to Ms. Hilton and Ms. Kardashian, a perfume or a clothing line or a cd/dvd. Amongst other things you’ll want to make use of display advertising (banners / widgets etc). How do you figure out who the right audience is, and where you’ll find them? As opposed to of course buying the main banner spot on www.yahoo.com were your ad might be a hit or a miss. Tools for...<br/><div align='right'>2009-06-29 10:40:24</div>

Paris Hilton, Kim Kardashian Telling Stories With Data

It is such a cliché: Don’t just present data, tell a story. Yet it is rarely followed. We almost always present data. Actually we don’t present data, we send out reports. With data. Lots of it. With 6 size font and some pies and stacked bar graphs thrown in. Then we are frustrated that no one seems to pat us on the back, sing songs in our glory, give us more money. We don’t truly tell stories because it seems like a lot of work. And it can be. But you’ll be surprised at how often it is simply a matter of framing things differently, letting your imagination roam free. Last month I had to present to a group of executives in New York. One of the key things I wanted to communicate was the power of not doing random advertising but rather using freely available data to target the advertising on sites where relevant audiences exist. Goals Summary: 1. Show the power of free tools available. [Google's Ad Planner specifically.] 2. Highlight the importance spending money on advertising to relevant audiences. 3. Tell a memorable story. Below is how I did it. . . . hopefully it will inspire you to look for stories in your data, stories that will hold interest and might even get you some smiles (and you know that a raise is not far behind!). My first step was to try and tap into current events / pop culture. That calls for some research. I use Google Insights for Search as the best way to get a pulse on what people find interesting. Specifically what I often do is run this query: Who are the most popular celebrities in New York in the last 30 days? Turns out it is someone called Kim Kardashian. It also turns out I have no idea who this person is, an unfortunate side effect of not have time to watch television. Quick Google search and I am caught up on why Ms. Kardashian is “famous”. She has some overlap with Paris Hilton in terms of the path to fame. The key ingredient for any story is to have interesting protagonists. For this story due to their popularity it will be Ms. Hilton and Ms. Kardashian. The plot: Your business has a need to market something related to Ms. Hilton and Ms. Kardashian, a perfume or a clothing line or a cd/dvd. Amongst other things you’ll want to make use of display advertising (banners / widgets etc). How do you figure out who the right audience is, and where you’ll find them? As opposed to of course buying the main banner spot on www.yahoo.com were your ad might be a hit or a miss. Tools for...<br/><div align='right'>2009-06-29 10:40:24</div>

Slay The Analytics Data Quality Dragon Win Your HiPPO?s Love!

Two truths: 1] Turns out the readers of Occam’s Razor are exceptionally gifted, they understand the challenges of web data 2] They are deeply motivated to do something about it, just not totally sure what. This is a special unplanned post just for you, to help with issue #2. My last post, Web Data Quality: A Six Step Process To Evolve Your Mental Model , unleashed an unusually exceptional set of comments from you all (sweet!). Today I want to share a cogent set of “next steps” ideas. Practical strategies to deal with the problems you highlighted, nuances you can exploit, things you need to give up on, things you might consider doing more, bosses you need to ditch (!!). Here’s my core premise: Uno.You understand that data collection is imperfect (even as we collect more and better data than any other channel on the planet bar none). Dos.You accept (or soon plan to) the Six Step Mental Model on data quality . Tres.Your gallant efforts to make progress with the data you have are stymied by the Overlords (or as we often lovingly refer to them as: the HiPPO’s). You there? Then let’s go rock this thing. My recommendations, mostly in the order of importance: #1: Give up. Pick a different boss. #2: Educate them about the “perfect” source they love. #3: Distract your HiPPO’s from data quality by giving them actionable insights. #4: Dirty Little Secret One: “Head” data can be actionable in the first week / month. #5: Dirty Little Secret Two: Data precision actually goes up lower in the “funnel”. #6: Realize the solution to your problem is not implement one more tool! #7: Pattern your brain to notice when you’ve reached Diminishing Margins of Return. #8: If you have a small site, you have bigger problems than data quality. #9: Be Aware of two upsetting distractions: Illogical customer behavior. Inaccuracy benchmarks. #10: Remember you can fail faster on the web. Curious? The rubber meets the road now. . . . #1: Give up. Pick a different boss. Did you think I was kidding? There is a entire generation of leaders in place today that don’t get it. Many of them, sadly, will never get it. I don’t blame them. They have seen the world in one way and they can’t change now. We simply have to wait that generation out. For now we have to wait for them to get promoted / take on other life challenges. When I have found myself in situations where there is just no chance of...<br/><div align='right'>2009-06-01 10:10:26</div>

Dear Avinash: Web Metrics Analytics Questions, Facebook Edition

A few weeks back I had asked this question on Twitter: Inspire me: If there is one web analytics question you want answered what would it be? What’s your juiciest / mundane, daily, challenge? The result was this post: Top Web Analytics Questions, Twitter Edition. Those 16 questions (!) were just one part of the story. My twitter account is linked to my facebook account , so my tweets get posted as my status updates. That means I got a bunch of questions on the facebook account as well. . . . Here is a summary of the 9 questions / topics that are addressed in this blog post: Twitter’s impact on bounce rates. Does complete information translate into absolute action? The most important business questions addressed by Web Analytics. How to judge someone’s talent/ability in being a Web Analyst? The mystery of “Returning Visitors” having 1 Visit to Purchase! <- Important. Reliability, and effectiveness, of Predictive Web Analytics. How to measure impact of Branding activities? Metrics / Key Performance Indicators to check Daily (!), for any site (!!). Tips and best practices for Filters and Expressions in Google Analytics. So here we go, replies to my facebook friends, things that keep them up at night. . . . #1: Dror Zaifman: How do you think Twitter will effect bounce rates on web sites ? Meaning do you think that someone reading a Twitter post will get more excited due to the heighten hype on Twitter and therefore might be disappointed with the end result increasing the bounce rates? Twitter will no more increase your bounce rates than say a digg or a stumbleupon or pick your favorite “hot right how” web 2.0 thingy . In the sense that each of these channels tends to bring new traffic to your site, perhaps a higher percent of them might not be totally relevant for you. But I am not sure that the traffic from Twitter has any higher levels of ADD. :) As to weather they should be disappointed or not, that’s your call. If you/others just use Twitter to hype what you do or push sub optimal content then you lose credibility, followers and more. So the system is “self correcting”. #2: John Quarto-vonTivadar: True or False? If you had 100% metaphysical certitude analytics coverage and could know anything you wanted to know, would some companies still be unable to increase their conversion rate? I depressingly suspect the answer is True. Remember I am not a rocket scientist. You need to dumb this...<br/><div align='right'>2009-05-04 10:07:46</div>

PPC / SEM Analytics: 5 Actionable Tips To Improve ROI

A very wise friend, ok Craig, once said this to me: “Paid Search is like playing chess with a Supercomputer.” At that moment I think my reaction was something like “meh!”. : ) Of course Craig was right. Search Engine Marketing (Organic + PPC) continues to be a huge part of any company’s acquisition strategy on the web. Like other online channels it is targeted, it is effective and it is accountable. Perhaps its most unique asset being the ability to hyper-target relevant customer intent. It would be fair to say that Paid Search it has also gotten very complex (Search Long Tail anyone?). Your campaigns and ads are impacted by the many shiny buttons and pretty dials provided by search engines, complex algorithms that determine if your ad shows up (or not), and a bunch of cool things that search engines are doing (like Universal Search). Given that context measuring impressions and clicks and click thru rates (CTR) and cost per click (CPC) and conversions are now merely price of entry, in fact you focus on those in the first couple days and then very quickly have to elevate your game. Yet with our tools like Omniture or WebTrends or Google Analytics etc that is kind of all we end up focusing on as Analytics Professionals. Partly because of the limitations of the data available in the tools, partly because most Web Analysts don’t have the required deep understanding of what Paid Search is all about. In this post I wanted to share five cool, “non-normal”, analyses that you can do to get a much better understanding of your Paid Search performance. The reports are all inspired from analytical principles I have advocated for quite some time on this blog. All the analyses been created using the ClickEquations PPC Platform’s ability to do impressive deep analysis with its ClickEquations Analyst tool. Disclosure: I am on the ClickEquations board of advisers. Here’s how I think of CQ Analyst: Take one part data from all search engines, one part unique custom metrics, one part custom database front end integrated into excel and the resulting ménage à trois combines to produce incredibly powerful insights. Here are five that we’ll cover here: #1: Identify Keyword “Arbitrage” Opportunities. #2: Rock Your World, Focus on “What’s Changed”. #3: Analyze Visual Impression Share, Compute Lost Revenue. #4: Embrace the ROI Distribution Report [Identify: Lovers, Friends, Losers]. #5:...<br/><div align='right'>2009-06-15 10:43:15</div>

Slay The Analytics Data Quality Dragon & Win Your HiPPO?s Love!

Two truths: 1] Turns out the readers of Occam’s Razor are exceptionally gifted, they understand the challenges of web data 2] They are deeply motivated to do something about it, just not totally sure what. This is a special unplanned post just for you, to help with issue #2. My last post, Web Data Quality: A Six Step Process To Evolve Your Mental Model , unleashed an unusually exceptional set of comments from you all (sweet!). Today I want to share a cogent set of “next steps” ideas. Practical strategies to deal with the problems you highlighted, nuances you can exploit, things you need to give up on, things you might consider doing more, bosses you need to ditch (!!). Here’s my core premise: Uno.You understand that data collection is imperfect (even as we collect more and better data than any other channel on the planet bar none). Dos.You accept (or soon plan to) the Six Step Mental Model on data quality . Tres.Your gallant efforts to make progress with the data you have are stymied by the Overlords (or as we often lovingly refer to them as: the HiPPO’s). You there? Then let’s go rock this thing. My recommendations, mostly in the order of importance: #1: Give up. Pick a different boss. #2: Educate them about the “perfect” source they love. #3: Distract your HiPPO’s from data quality by giving them actionable insights. #4: Dirty Little Secret One: “Head” data can be actionable in the first week / month. #5: Dirty Little Secret Two: Data precision actually goes up lower in the “funnel”. #6: Realize the solution to your problem is not implement one more tool! #7: Pattern your brain to notice when you’ve reached Diminishing Margins of Return. #8: If you have a small site, you have bigger problems than data quality. #9: Be Aware of two upsetting distractions: Illogical customer behavior. Inaccuracy benchmarks. #10: Remember you can fail faster on the web. Curious? The rubber meets the road now. . . . #1: Give up. Pick a different boss. Did you think I was kidding? There is a entire generation of leaders in place today that don’t get it. Many of them, sadly, will never get it. I don’t blame them. They have seen the world in one way and they can’t change now. We simply have to wait that generation out. For now we have to wait for them to get promoted / take on other life challenges. When I have found myself in situations where there is just no chance of...<br/><div align='right'>2009-06-01 10:10:26</div>

Web Data Quality: A 6 Step Process To Evolve Your Mental Model

It seems absolutely dumb to argue that while the quality of data used to make decisions is important, it is actually not that important to have the highest data quality. Generations of Analysts, Data “People”, Decision Makers have grown up with the principle of GIGO. Garbage in, garbage out. It made a lot of sense for a very long time. Especially because we used to collect so little data, its lack of even a little quality crapified the decision a lot. GIGO also fueled our every expanding quest for data perfection and data quality. There are entire companies built around helping your “clean up” your data. Especially if you look at the offline traditional business intelligence, erp, crm, data warehouse worlds. The web unfortunately threw a big spanner into the works. Couple important reasons. First, it is important to realize that we collect a lot of data on the web (type of data, elements of data, what not). Second, our beloved world wide web, remember still a little baby, is imperfect at every turn. We use data collection methodologies that reflect our efforts to do the best we can, but they are inherently flawed. Just take javascript as an example. It is good at what it does. But not everyone has javascript turned on (typically around 2-3%). Zing: imperfection. A lot of data. Imperfect data collection system. Here is the most common result of this challenge: The “Director of Analytics” spends her meager resources in the futile quest for clean data. Money is spent on consultants (especially the “scarady cats” who deftly stir this issue in their business favor). Everyone tries to reconcile everything across systems and logs. Omniture gets kicked out and WebTrends gets put in, supposedly for it “far superior” data quality (!!). Makes me sad. In the debate for perfect data is is important to realize that the reality is a lot more nuanced. No Possible Complete Data on Le Web. I humbly believe that the world of data perfection (”clean auditable data”) does not exist any more. It did for a long time because life was cleaner, mistakes were human made, sources were fewer and there wasn’t enough data to begin with (sure terabytes of it, but of what 300 fields? 600?). On the web we now have too many sources of data. Quantatitive, qualitative, hearsay (sorry, surveys :), competitive intelligence, and so much. [Web Analytics 2.0 ] But these sources are “fragile”. Sometimes...<br/><div align='right'>2009-05-18 10:33:26</div>

Dear Avinash: Web Metrics & Analytics Questions, Facebook Edition

A few weeks back I had asked this question on Twitter: Inspire me: If there is one web analytics question you want answered what would it be? What’s your juiciest / mundane, daily, challenge? The result was this post: Top Web Analytics Questions, Twitter Edition. Those 16 questions (!) were just one part of the story. My twitter account is linked to my facebook account , so my tweets get posted as my status updates. That means I got a bunch of questions on the facebook account as well. . . . Here is a summary of the 9 questions / topics that are addressed in this blog post: Twitter’s impact on bounce rates. Does complete information translate into absolute action? The most important business questions addressed by Web Analytics. How to judge someone’s talent/ability in being a Web Analyst? The mystery of “Returning Visitors” having 1 Visit to Purchase! Reliability, and effectiveness, of Predictive Web Analytics. How to measure impact of Branding activities? Metrics / Key Performance Indicators to check Daily (!), for any site (!!). Tips and best practices for Filters and Expressions in Google Analytics. So here we go, replies to my facebook friends, things that keep them up at night. . . . Dror Zaifman: How do you think Twitter will effect bounce rates on web sites ? Meaning do you think that someone reading a Twitter post will get more excited due to the heighten hype on Twitter and therefore might be disappointed with the end result increasing the bounce rates? Twitter will no more increase your bounce rates than say a digg or a stumbleupon or pick your favorite “hot right how” web 2.0 thingy . In the sense that each of these channels tends to bring new traffic to your site, perhaps a higher percent of them might not be totally relevant for you. But I am not sure that the traffic from Twitter has any higher levels of ADD. :) As to they weather they should be disappointed or not, that’s your call. If you/others just use Twitter to hype what you do or push sub optimal content then you lose credibility, followers and more. So the system is “self correcting”. John Quarto-vonTivadar: True or False? If you had 100% metaphysical certitude analytics coverage and could know anything you wanted to know, would some companies still be unable to increase their conversion rate? I depressingly suspect the answer is True. Remember I am not a rocket scientist. You need to dumb this down for me! :)...<br/><div align='right'>2009-05-04 10:07:46</div>

Standard Metrics Revisited: #6: Daily, Weekly, Monthly Unique Visitors.

Do you have a sneaking, yet unshakable, suspicion that your Web Analtyics Vendor is sometimes just trying to mess with you? Guess what? It’s true! All web analytics tools have a smattering of metrics and key performance indicators that were created just because someone decided it would be cute to add / subtract / multiply / divide some numbers. Many of these don’t pass the first sniff test and when if they do you are still left wondering: “What in God’s name and all that is holy in this world am I supposed to action based on this metric?” The answer? Nothing. With that gloriously upbeat set up let me tell you what we are going to cover today: Three metrics that are available in pretty much all “adult” web analytics tools. Daily, Weekly, Monthly Unique Visitors. They are so common yet most people don’t understand them well enough and fewer still realize how harmful these can be to your health even in day to day use. So in this post we try to understand the most basic of the web analtyics basics, the Unique Visitor computation. What’s a Unique Visitor? It is simple really. . . . Technical Definition: Count of all the Unique cookie_id?s during a given time period. English Definition: The first time someone visits your site a first party persistent cookie is set in their browser. This cookie lasts any where from several months to several years. Each time that person visits your site that cookie identifies them as the same browser. Notice I said browser, not person. It is likely, but not always true, that each a unique visitor is a unique person. You can learn a lot more about Visits and Unique Visitors in this post: Standard Metrics Revisited: #1: Visitors. Very predictably every 18 months or so the blogosphere goes wild with how accurate, or not, the Unique Visitor metric is. Much mud is thrown around. Indignations are foisted on the world. Name calling ensues. Regardless of that Unique Visitors remains a valuable metric that used correctly, in place of Visits, measures success of your online marketing efforts. Oh and your best weapon against ignorance? Education. See above post on Visitors. And this one: A Primer On Web Analytics Visitor Tracking Cookies. It covers cookies and deletion rates and other such yummy stuff. Read that and you have my word you’ll be the smartest cookie in the room. See what I did there? :) Daily, Weekly, Monthly Unique Visitors: In many web analytics tools (say Yahoo!...<br/><div align='right'>2009-04-20 11:54:45</div>

Google?s Search Based Keyword Tool: Monetize The Long Tail of Search

Every once in a long while you come across a tool that just gives you goose bumps, you are instantly infatuated. The Search Based Keyword Tool (SbKT) was that for me. The data it brings together and the transparency it brings is just so…. sexy. Let’s see if you feel that by the time you are done with this post. I can tell you know that you’ll never think of Paid Search the same way! One of the most common phenomenon is the long tail of search. Yet precious few people understand this and even fewer actually are able to use it to their advantage. A quick recap: The Search Long Tail. If you have not had a chance please give my long tail of search post a quick review. The essence of the phenomenon is that your website most likely has a ?short head? (say ten or fifteen keywords that drive massive number of Visits) and usually a ?long tail? (hundreds of thousands of key words and key phrases that drive five, ten, fifteen - few - Visits). The really cool thing about the tail is that the thousands of queries in the tail are generic (”category“) queries which tend to bring “impression virgins ” to your site. People who are new to your franchise, people who have not made up their mind. Find them first and you get first dibs on convincing them. My Long Tail: The Story in Numbers. As an example for the month of Feb this blog received traffic from a total of 32,138 search queries. Here’s a real example, data for this blog: 87,861: # of Visits in the month of March. 40,662: # of Visits from Search (!!). 26,137: # of distinct keywords and key phrases that drove those 41k Visits! Astonishing is that not? For a single topic blog! So what of the head and the tail? 13: # of search key words that brought 100 or more visits (the head). 26,124: # of search key words that brought 2 or 5 or 10 or a small number of visits each (the tail). If you are not yet confused by all the numbers, please stick with me, here is another way to think about it. 13 head keywords brought 5,128 Visits to the site. 26,124 tail keywords brought 35,534 Visits to the site! [This is the prime reason I tell Analysts, Marketers, CEO's, my children not to just obsess about the head!] Clearly even a one topic (Analytics) blog can have a verrrrry long tail. I can only imagine how long your tail is. You have a real business. You have thousands of pages. You have tons of products you sell, a diverse set of services and so on and so forth. The Problem?...<br/><div align='right'>2009-04-06 11:36:25</div>

Dear Avinash: Top Web Analytics Questions, Twitter Edition

When in doubt, ask your customers to help you. I am quite fond of saying that in the context of using methodologies like Surveys and Experimentation and Testing in service of improving web experiences. Today I used that idea in a different context, ask you what was top of your mind so I could try and answer some of them. My question was very open ended. Here’s my simple tweet : I got a boat load of questions on my twitter and facebook accounts. Some were a delight, others were like “what?” :), and there were some that made me pull my hair out. [Is that social media in a nutshell? :)] In this post I’ll cover all the questions I got on Twitter. I’ll answer the ones from Facebook in the next post (this one got too long!). But before I go on one important point of emphasis. It’s all about Outcomes baby! A lot of questions fell into this bucket “what should I do” / “where should I start” / “how can I convince xyz of something”. I am going to answer all those questions with this: Focus on what the Outcomes are that you / your company is driving towards. I know it is the lamest thing someone could say, it even sounds like a cop out. It is not. This game is all about Outcomes! Let me go out on a limb and say there are only three types of Outcomes any website delivers (except of corner cases - all you anal retentive folks take two steps back!): Increase Revenue Reduce Cost Improve Customer Loyalty / Satisfaction That’s it. When in doubt ask your self if what you are doing falls into one of those three buckets. If it does, keep going. If not then I suggest you revisit what you are doing. I know there might be others Outcomes, but for most “business” sites it will be about those three and their derivatives. [Business sites could be for-profits or non-profits, ecommerce or non-ecommerce, blogs or tech support etc.] Let’s get this puppy on the road. A selection of your questions on Twitter and my answers. . . . @JudithLewis : How do you convince people to look beyond page impressions for usable measurable metrics? I am not trying to be glib here, but you have to show them how pathetic those kinds of metrics are. If your HiPPO’s quite large then replace pathetic with primitive. : ) Recently I have started saying that the only metrics that truly help find actionable insights are those that measure / reflect customer behavior. Page impressions is a “aggregate...<br/><div align='right'>2009-03-25 10:52:56</div>

Analytics Career Advice:?I am an Analytics God, I want more $$. How??

Michael, politely, says in an email: “I have done web analytics for five years, I have mastered Omniture, WebTrends and Google Analytics, I provide analysis and not just reporting. I feel like am an Analytics God. What would be your advice for me in terms of next steps for my career? My goal is to climb the ranks and increase my salary.” Let me hasten to add two things. Michael is not his real name. Modesty aside, :), Michael is good at what he does. I get many emails in the spirit of this one and thought it was about time I wrote a proper post about it. Another reason for writing the post now is that it is always a good time to think about your career path, but never more so than the current economic circumstances. Some of you face tough times, some might get laid off [see end of this post], some might make opportunistic leaps. Either way good time to ponder, do some self reflection and make a conscious choice. Before we get going some assumptions I am making: 1) You are an “Analyst” (Senior, Junior whatever). Or atleast 40% of the time you are a true Analysis Ninja, even if 60% of the time you are a glorified Reporting Squirrel! 2) You might have some project / task management experience, your leadership experience is limited to that. 3) I am simply assuming you are good at tools and some technical stuff and some business stuff. When Michael says he is good at Analytics his stress is on his mastery of javascript tags, his rich understanding of evars and sprops and complex 60 kb Omniture tags. He can implement anything in his sleep. 4) You realize that there is more to life than creating reports and trying to explain KPI’s. It is ok to want more money and be aggressive about your career but know that it won’t happen unless you vastly expand your horizon on the work you’ll do (and how hard it will be). Update: 5) You are at a mid to large/bigger company. Please see this comment for context around why. Every Web Analyst (or really Business Analyst) of any sort finds themselves at that critical point. Have been doing analysis for a while, now where does my career lead me? The first and perhaps most important thing to realize that you have to make two very important very critical very life impacting choices: Choice 1: Business or Technical. Choice 2: Individual Contributor or Team Leader. Each choice will help propel your career in a different direction (slope and length). Typically we don’t think that you...<br/><div align='right'>2008-12-03 10:26:08</div>

Excellent Analytics Tip #14: Measuring Value of Ecommerce Sales Tools

An Analysis Ninja, let’s call him Philip Walford, asked a delightful question. Philip wanted to know if the impact of a faith based initiative in his company, product demo videos, could actually be measured using data. Hurray! Faith is good. Data is better. : ) [And before you flame me: know that I love my religion more than you love yours. Wait. That did not come out right. Let me rephrase that.] In this thanksgiving week 2008 post I’ll share Philip’s question about how to identify value of video product demos on an ecommerce site, and my answer about involving customers. Here’s Philip. . . . We are a large retailer with a lot of product on our site. In the past we have invested lots of dollars and time producing things like demo videos for our products, or adding other features and tools to our website to provide more information about a product. Our goal is to inspire customer confidence in their purchase (by giving them as much information is possible). The question is, what are the KPIs of things like a demo video. My recommendation was to measure conversion rate for the segment that views the video. If conversion is higher then the videos are bringing value. Others in my company have presented the hypothesis only customers that are a lot more invested in buying the product are likely to click on the video link and hence “pre qualified”, hence that segment would have had a higher conversion rate regardless. I understand their perspective but I feel they are reading too much into the situation but I don’t know how to argue this point. There are several directions we could go with this but I wanted to see if you could share some guidance on this issue. My answer to Philip. . . . This is a complex problem, more than might be apparent on the surface. It is also an example where it can be easy to jump into bed with your web analytics tool to get satisfaction but you wake up in the morning feeling. . . . well. . . . less than satisfied. But before we go there I have to give a ton of credit to Philip and his crew for being skeptical of reading too much into their own opinions or biases. I firmly believe that people who work for a company rarely (never!) represent customers. They are too close to the company and too different. Just because I work for Microsoft and use a Zune (yes I do!) does not mean I can be a effective customer representative of Microsoft Zune customers. Company employee opinions rarely reflect...<br/><div align='right'>2008-11-24 10:18:01</div>

Experiment or Die. Five Reasons And Awesome Testing Ideas.

“Experiment or die, there is no try.” That was my call to action, Yoda inspired, last week to a group of international C-level executives. And I meant every word of it. There is a tendency to think experimentation and testing is optional. Ouch! I fundamentally believe that is wrong. For a few simple reasons: # 1 It’s Not Expensive! You can start for free with a superb tool: Google’s Website Optimizer. It is packed with enough features that I have no qualms flogging it (even though I work closely with the team!). If you want to help our economy and pay for your tools then that is absolutely fabulous. Both Offermatica and Optimost are pretty nice options. [Just don't fall for their bashing of all other vendors or their silly claims, false, of "superiority" in terms of running 19 billion combinations of tests or the bonus feature of helping you into your underwear each morning. You'll be lucky if you can come up with 5 combinations, and it is not that hard to put on your underwear. Look for actionable uniqueness. For example I am quite fond of the fact that with Offermatica you can "trigger" tests based on behavior. That is nice, well worth paying for.] # 2 Six And A Half Minutes. That’s it! Tom has tried this with many many Marketers, and its so true: If you have two different pages you want to test, it takes six and a half minutes for you to configure, test (QA) and launch a A/B test. [Please read that literally, as it is written. You have two pages already. 6.5 mis to: Configure. QA. Launch.] You have six and half minutes right? I cannot recommend enough the wisdom of starting with a A/B test. You will start fast, you will find enough problems in your company, you can show easy wins. Aim to get to the thing vendors are selling, MVT, but start with A/B regardless of the tool you use. # 3 Show ‘em You Are Worth It. There is a lot of pressure on all of us to prove our worth and make significant improvements to our web business. ClickStream analysis with Omniture or Google Analytics or ClickTracks is well and good, testing will get you on the path of taking having a direct impact faster. By the nature of it Testing is action oriented, and what better way to show the HiPPO’s that you are awesome then by moving the dial on that conversion rate in two weeks? # 4 Big Bets, Low Risks, Happy Customers. Very few people appreciate this unique feature of testing: You have an ability to take “controlled risks”. ...<br/><div align='right'>2008-11-17 09:49:39</div>

The Ultimate Web Analytics Data Reconciliation Checklist

Ideally you should only have one web analytics tool on your website. If you have nothing and you are starting out then sure have a few different ones, stress test them, pick the one you love (just like in real life!), but then practice monogamy. At the heart of that recommendation is a painful lesson I have learned: It is a long hard slog to convert an organization to be truly data driven. And that’s with one tool. Having two tools just complicates life in many subtle and sub optimal ways. One “switch” commonly occurs is the shift from fighting the good fight of getting the organization to use data to bickering about data not matching, having to do multiple set of coding for campaigns (the page tagging work itself is trivial) and so on and so forth. In a nutshell the efforts become all about data and not the quest for insights. So if you can help it, have one tool. Bigamy atleast in this case is undesirable. [If that does not convince you remember the magnificent 10/90 rule from May 2006 when I was but a naïve web analytics Manager.] But. Pontification aside the reality is that many people run more than one tool on their website (though hopefully they are all on their way to picking the best of the lot). That means the bane of every Analyst’s existence: Data reconciliation! It is a thankless task, takes way more time then needed and the “game” is so rigged that 1] it is nearly impossible to get to a conclusion and 2] it is rarely rewarding - i.e. worth it. But reconcile we must. So in this post I want to share my personal checklist of things I look for when going through a data reconciliation exercise. Usually this helps get things to within 95% and then I give up. It is so totally not worth it to get the rest! * This post is a bit technical, but a Marketer should be able to understand it. And my goal is you smile three times while you read it, either from the inside jokes or from the sheer pain on display! * So if you are starting a data reconciliation project for your web analytics tools, make sure you check for these things: #1: Comparing Web Logs vs. JavaScript driven tools. Don’t. #2: First & Third Party Cookies. The gift that keeps giving! #3: Imprecise website tagging. #4: Torture Your Vendor: Check Definitions of Key Metrics. #5: Sessionization. One Tough Nut. #6: URL Parameter Configuration. The Permanent Tripwire. #7: Campaign Parameter Configuration. The Problem of the Big. #8: Data Sampling. The...<br/><div align='right'>2008-11-06 09:38:47</div>

Life, Liberty And The Pursuit Of Happiness

I am an immigrant to the United States. In the process of becoming a citizen there was one document, with a wonderful snippet, that continues to be deeply inspiring for me. It reflects the promise of America. The document was the United States Declaration of Independence [text here], and these words. . . . We hold these truths to be self-evident, that all people are created equal, that they are endowed by their Creator with certain unalienable Rights, that among these are Life, Liberty and the pursuit of Happiness. That last part forms for me the essence of what it means to be truly free. It is a commitment, to you and to me and to us, of the opportunity that we are blessed with. An opportunity to make choices that you and I would like to as we live our humble lives. One of those choices you and I can make is the choice of who we can marry. It is in support of that choice that I request you to consider voting No on prop 8. I have profound respect for the choice you make in whom to marry. I am asking that my right to marry the person of my choice be preserved. And the right of my friends. And my neighbours. And my fellow citizens. For Life, Liberty and the pursuit of Happiness. Thank you. Update: Nov 5th, 1400 hrs. I am deeply disappointed that Prop 8 passed. That we have now institutionalized and legalized civil discrimination against our own citizens in California. But. At the turn of the last century dogs and a specific race were asked to stay out public establishments. Less than a hundred years ago one human gender could not even vote. Less than fifty years ago one race in this country was asked to ride at the back of the bus, we still believed in “separate but equal“. Just eight years ago an American University ended a ban on interracial dating (something that would have prevented my beloved wife and I from ever being a couple). I am confident that in my lifetime this nation will allow any person who wants to marry to marry, and marry the one they choose to marry. I leave you with words that inspire me: Let us not wallow in the valley of despair, I say to you today, my friends. And so even though we face the difficulties of today and tomorrow, I still have a dream. It is a dream deeply rooted in the American dream. I have a dream that one day this nation will rise up and live out the true meaning of its creed: “We hold these truths to be self-evident, that all men are created equal.” I have a dream that one day on...<br/><div align='right'>2008-11-03 09:22:17</div>

Google Analytics Releases Advanced Segmentation: Now Be A Ninja!

The Google Analytics team announced the release of seven features today. The next stage in the metamorphosis of the popular web analytics tool. Without a doubt the feature that I am most excited about is Advanced Segmentation. This has been a long time coming (can you sense my pushiness!), and in this post I wanted to share with you all how to use this awesome feature. Along the way I’ll share three different segments that you must have in your web analytics tool. Regardless of why your website exists or what tool you use, Google Analytics or an alternative. I’ll close with a approach you can use to get answers to your ad-hoc questions / queries faster, in mere minutes rather than days. But before we go on here are all the features released today: 1. User Interface refresh. 2. AdSense now integrated into GA. 3. Advanced visualizations: Motion Charts! 4. Custom Reports! 5. Advanced Segmentation!! 6. The Google Analytics API. 7. Automatic importing of AdWords cost data into Urchin. AdSense and API are in Private Beta (access by invitation). Motion Charts, Custom Reports, Advanced Segmentation are all in Public Beta (being released starting today, gradually to everyone in the next few weeks). If you particularly can’t wait to use the Public Beta features then see the end of this post to get access sooner. Now to evolving from being Reporting Squirrels to being Analysis Ninjas! Why Segmentation? Analyzing data in aggregate is a crime. Bold statement, but the reality is that a “monolith” does not come to your website. Your site does not exist for a singular reason either. The core drivers of traffic are magnificently different for each core group of visitors. So your website’s really a mix of Visitor Sources, Visitor Behavior and your Desired Outcomes. When you look at all that in aggregate you get nothing. You think Average Time on Site means something. No! You think All Visits and Overall Conversion Rate gives you insights. Nyet! You think understanding Keywords without drilling down to each search engine will be awesome. Non! If you want to find actionable insights you need to segment your web analytics data. You need to separate out the various Sources, Behavior and Outcomes. Then you’ll understand behavior of micro-segments of your website visitors, which in turn will lead you to actionable insights because you are not focusing on a “glob” rather you are focused on a “specific”. Need...<br/><div align='right'>2008-10-22 20:05:40</div>

Aggregation of Marginal Gains: Recession Busting Analytics!

We are constantly on a quest to conquer the next big thing. Mountain. Ocean. Planet. “Conversion Buster.” The next million dollar opportunity. Not that there’s anything fundamentally wrong with that quest. The challenge is that frequently in that quest we ignore the immediately achievable. And that tradeoff is a crime. The title of this post comes from a blog post that my friend David Hughes had written in September 2008: What can Digital Marketers learn from Olympic Cyclists? He started that post with this wonderful quote: Back in the 1980’s Jan Carlzon was trying to breathe new life into an ailing Scandinavian Air Services. He was famous for saying “You cannot improve one thing by 1000% but you can improve 1000 little things by 1%”. Aggregation of marginal gains! Fantastic concept, loved it instantly, captured my heart. I have mentioned this issue in the past, though never quite as eloquently. Here’s my humble tweet from Aug 2008: This post is all about the low hanging fruits. Small and medium sized ideas for finding opportunities that collectively should add up to something remarkable for your website. This being the recession and what not, please share your own stories of simple every day things you do to find actionable insights for your company. Together we can! : ) So before your go boiling the ocean here are a few things you can and should do today to ensure your company is benefiting from immediately fixable things: 1) Figure out where you are making money, where you ain’t. 2) Cover all the bases in your email campaigns. 3) Funnels baby! 4) Stop The “Puking”: Fix Your Top Landing / Entry Pages. 5) Identify Paid Search Keyword Opportunities. 6) When In Doubt, Ask Your Customers. 7) Stop Doing “Dumb” Things. (Examples included!) Batten down the hatches, let’s deep dive. . . . 1) Figure out where you are making money, where you ain’t. I have covered this in the past: Pick One, Just One Web Analytics Report, Go! Why is it great? You come to work to: 1) Increase Revenue 2) Reduce Costs 3) Improve Customer Satisfaction (create Brand Evangelists). This analysis focuses on the first two of those things. It helps you identify which sources are working well (and hence need more love, resulting in increased revenue. It will help you identify sources that might be sucking wind, and help you reduce cost. Possible outcomes? Focus in your advertising, sales, marketing spend....<br/><div align='right'>2009-03-10 09:55:34</div>

Excellent Analytics Tip #15: Brand Evangelists Index

Often we present data without thinking about it too much. We might actively think about the metrics we are computing (and avoid rookie analysis mistakes). But it is rare that we, “Web Analysts”, actually think, I mean think, about the story we are telling. I think that’s because that is not our job (I mean that in all seriousness). Our job is to report data. On good days it is to understand and segment and morph and present analysis. But we don’t think about the implications of the data in a grander context and we don’t think about the role we can play in connecting with the Business, the Marketers and be so bold as to try and change behavior of decision makers. Change company cultures. This blog post is a short story about my small attempt at changing the culture and setting a higher bar for everyone. Using data. The Use Case: The data in question was survey data. This one was specifically about a day long conference / training / marketing event for current and prospective customers. On a five point scale for each Presenter the Attendees were asked to rate “how satisfied were you with the presentation and content“. Quite straightforward. Here are the results: But you can also imagine getting this kind of data from your free website survey, like 4Q from iPerceptions ["Based on today's visit, how would you rate your site experience overall?"]. Or if you use free page level surveys from Get Satisfaction or Kampyle ["Please share your ratings for this page."] In those cases you would analyze performance of content or the website. The Data Analysis: On surface this is not that difficult a problem to analyze. Here is a common path I have seen people take in reporting this data, JAI! Just Average It! :) The actual formula is to take the average of the last three columns (satisfied through extremely satisfied). This is ok I suppose. I find people have a hard time with smaller numbers and then you throw in the decimals and you might as well call it quits. Your boss, Bruce, eyeballs this and says: “Looks like everyone performed well today, let’s uncork the champagne.” Meh! Those a bit more experienced amongst you know this and what we might see from you is not averaging but rather a more traditional Satisfaction computation. The formula is to add the three ratings (satisfied through extremely satisfied) and divide that by the total number of responses. For Jonny: (6+12+0)/18 A bit better from a...<br/><div align='right'>2009-03-02 10:17:13</div>

Actively Avoid Insights: 4 Useful KPI Measurement Techniques

Yes. I noticed the slightest hint of sarcasm in the title of this post. This post covers four commonly used measurement techniques that 9 times out of 10 work against the evolution of Reporting Squirrels into Analysis Ninjas. I’ll also admit that most of the times when I encounter them I might think slightly less of you (especially if you present the aggregate version to me rather, presenting the segmented view atleast gets you time to explain :)). If I am being slightly tough minded here it is only because I am hugely upset by the fact that analytics on the web is deeply under leveraged, though the good lord knows we try and pump out KPI’s by the minute. One root cause of this under leveraging it our dashboards that are crammed full of metrics that use these four measurement techniques. The end results: Data pukeing and not insights revelation. So who are the four amigos? Averages. Percentage. Ratios. Compound Metrics. Each a technique that when used “as normal” actively hinder your ability to communicate effectively the insights that your data contains. Only one caveat: I am not saying these techniques are evil. What I am saying is don’t be “default” when using them, be smart (or don’t). Before we get going here’s my definition of what a Key Performance Indicator is: Measures that help you understand how you are doing against your objectives. Note the stress on Measures. And Objectives. It it doesn’t meet Both criteria its not a KPI. With that out of the way lets understand why Averages, Percentages, Ratios and Compound Metrics are four usually disappointing measurement techniques. #1. Averages. Raise your hand if you are average? Ok just Ray? No one else? Raise your had if your visit on any website reflects an average visit? Just you Kristen? No one is “average” and no user experience is “average”. But Averages are everywhere because: 1) well they are everywhere, which feeds the cycle and 2) they are an easy way to aggregate (roll up) information so that others can see it more easily. Sadly seeing it more easily does not mean we actually understand and can identify insights. Take a look at the number above. 51 seconds. Ok you know something. Now what? Are you any wiser? Do you know any better what to do next? Any brilliant insights? No. It is likely that the Average Time on Site number for your website has been essentially unchanged for a year (and yet, yes sirrie...<br/><div align='right'>2009-02-18 10:51:54</div>

?Dear Avinash?: Bounces, Abandonment, Visitor Ratios & Data Drops!

One of the fun part of my professional life is all the email I get from you. Yes it is an insignificant amount of work but it means I learn a lot about what’s on your mind, what you find hard, what you find easy, what challenges bedevil you. At the moment the “could you help me” / “what do you think I should do” emails are around 50 a day. [My one small request is that you do a search on www.google.com before you email me, sometimes that also works pretty well.] Here are some questions from the last couple weeks that were interesting enough to share with you. #1: Bounces on a “non-bounceable” Page. How Come? #2: Bounce Rate in Omniture. Why Not? #3: Decent Funnel Abandonment Rate. How Much? #4: Rock Steady New vs. Returning Visitors Ratio. Why? #5: Sudden Data Drop! Help Me!?! The questions and answers are below, I hope you find them to be of value. #1: Bounces on a “non-bounceable” Page. How Come? How can an a page that is not accessible without coming from a previous page can have a “bounce rate”? It should only have an exit rate, isn’t it? That page is not a landing page. This is a good one, don’t cha love riddles? Many people wonder how their page deep in the site can have bounce rates. Or say their shopping cart page, that’s usually not even indexed by the search engine! Here are some reasons I have found for this problem in my experience: It turns out that people will bookmark pages on your site during their visit and then visit during bookmarks and then bounce! The session already started so you get a “single page view session” and you have a bounce. More than once the culprit was that people in the company had bookmarked pages and were visiting it as there were changes happening to see if the changes went live or looked good. They see one page and bounce (damn!). Another time it was that someone had bookmarked it and sent it around on email, all those clickthrus showed up as “direct” and bounce. Ditto for this tweet crazy world (if they short url the wrong page!). Of course other times you think the page is “unindexable” but not all robots will respect your robots.txt file instructions. And once, just once for me, this delightful person had set up a program (javascript executing program!) to go ping a deep page in the site to make sure the site was up! Of course that showed up as bounces, but by segmenting that page we found a...<br/><div align='right'>2009-02-10 10:50:31</div>

Paid Search Analytics: Measuring Value of ?Upper Funnel? Keywords

“How do I measure the value of my “upper funnel” keywords?” People ask me this all the time. Upper funnel keywords are typically those that are used by Customers who are early in their consideration life cycle, they have not made up their mind. They are keywords that introduce people to you. They don’t convert, that happens later in the life cycle (say as they move from “curious” to “interested” to “consideration” to…). So how to measure value of those keywords? [You can also apply this to targeted email campaigns or other marketing adventures.] Before I answer that question let me take you down memory lane and give you some critical context, then we’ll answer the question. A while back I had written a post about the long tail of search. You can read that post (it is a good one :) for more details, but the essence of the phenomenon is that your website most likely has a “thick head” (say ten or fifteen keywords that drive massive number of Visits) and usually a “long tail” (hundreds of thousands of key words and key phrases that drive five, ten, fifteen - few - Visits). As an example for the month of Jan ‘09 for my blog: the “head” is 11 keywords (100 or more Visits) and the “tail” is, and I am always astonished by this, 22,181 key words! Can you believe that? For such a deeply focused blog on one topic there were a total of 22k key phrases that delivered traffic from search engines. That’s both a reflection of all the love/work I put into SEO and a real living breathing example of the long tail. My long tail post also outlines how the head and the tail have very unique characteristics when it comes to the kinds of: key words that are present in the head (usually brand keywords) and the tail (usually category / generic / ecosystem) and people / visitors that use who come with head (people who know you already / have made up their mind) and tail (people new to your franchisee) key words and key phrases Read the post on the search head and long tail to see how to uniquely use Organic Search (SEO) and Paid Search (SEM / PPC) strategically to plan your world domination. My recommendation there was that most businesses should have a very focused and efficient long tail search strategy to ensure they keep attracting newbies (prospects) to their company / product / franchise. These people have not made up their mind. Find...<br/><div align='right'>2009-02-02 10:12:00</div>

Excellent Analytics Tip #15: Measure Latent Conversions & Visitor Behavior

Here is an astonishingly brilliant, yet simple idea (if I may say so myself!): Why just measure conversions as one purchase or conversions just as a submission of a lead or opening of an account on facebook / twitter / what ever? Why not measure Visitor behavior after that first purchase / lead / membership sign up (or the first super poke)? Why obsess about the “quickie” that is opening an account? Why not the Visitor behavior in the 30 days after sign up? For your Search campaigns. Or your Display ads. Or Affiliate programs? If you do that, you are not just measuring the “one night stand quickie” what you are measuring is something more of value: Was there a second date? Perhaps a proposal in five months? Maybe a marriage in nine months (because that’s when the baby showed up). : ) We do this so rarely. Success for campaigns (Search, Social Media, Display, TV and what not) is always measured based on that one visit or what happens when the campaign runs. Yet it is likely that you are measuring incomplete success. Let’s do an example. Facebook, in its quest for world domination, runs a world wide campaign (of whatever sort, really, make one up). Success is to increase the membership on Facebook and thus making it a more “valuable” property (so that even in a weaker economy they can raise another billion dollars of cash). So they get another 500k memberships. Success? How about this…. Rather than measuring membership sign ups why not wait 30 days and look at the Recency report for Visitors that came to the site as a result of that campaign? Did that just blow your mind? : ) No? Ok, what you are measuring in the case of Recency is not simply people who signed up BUT rather the behavior of the visitors who signed up. You are measuring if people you acquired from this campaign are visiting the site frequently lot in this 30 day time period after the campaign launched. My hypothesis being that simply signing up is not enough. That’s not enough success (not in this economy baby!). Real monetizable success is: 1) Do those people visit facebook again? And this is key… 2) They visit every day, or every other day, or every few days. The more frequently people visit facebook.com, the more valuable they are to facebook. They see more ads, they super poke more, they friend more, they add more apps, and so on and so forth. All activities (sexy translation: “visitor behavior”) that is...<br/><div align='right'>2009-01-21 10:35:32</div>

Google Analytics Maximized: Deeper Analysis, Higher ROI & You

I am sure you have a new year’s resolution. Perhaps it is that you’ll maximize the value you get out of your web analytics implementation (or should I say from your implemented web analytics tool since for many tools just implementing the tool is a multi year project!). For all Analysis Ninjas this special post outlines how to get maximum value from Google Analytics. Tips, best practices, pictures, pointers, guidance. Most people barely scratch the surface of their web analytics tools. My hope with this post is to get you beyond the Dashboard and the Search Keyword reports and the Referring Websites data. [While this post is uniquely focused on GA these are things that you should do with any web analytics tool, be it Omniture or WebTrends or CoreMetrics or the one your mother-in-law gave you for Christmas. Many of these features are, IMHO, easier to use in Google Analytics but they are available in other paid analytics tools. In fact with paid analytics it is quite likely that you are also getting additional options, add-ons, drill-anywheres, etc that should exploit a lot more to get exponentially higher ROI - if you are not call your vendor and do ask for guidance to show ROI. Net, net this post should help everyone, that's my goal.] This a simple ten nine point checklist of Analytics Awesomeness. If you do all of these ten nine things consider yourself an Analytics Empress/Emperor [aka Maximizer]. And if you do them well, ask for a raise, even in this economy. If you do five consider yourself an Analytics King/Queen. If you do three or less, consider yourself an Analytics Princess/Prince (aka the King of Newbies!). Of course any less than that and you are a Newbie (not that there’s anything wrong with that!). Ok that was, partly, just for fun. Let’s go. . . . #9: Get External Context to you Performance. If you look at your own web analytics data you know how well you are doing. 70 miles per hour. If you have access to your competitive ecosystem then you know that while you are happy to move from 60 miles per hour to 70 miles per hour in 12 months, they have moved from 170 miles per hour to 190 miles per hour! By enabling benchmarking in Google Analytics, you can view metrics for similar sites within your category. These benchmarks enable you to identify areas of opportunity relative to the performance of your competitors. Benchmarks are available for Visits, Bounce Rates, Page Views, Average Time on Site, Page Views Per Visit...<br/><div align='right'>2009-01-06 09:45:45</div>

Excellent Analytics Tip #15: Measure Latent Conversions Visitor Behavior

Here is an astonishingly brilliant, yet simple idea (if I may say so myself!): Why just measure conversions as one purchase or conversions just as a submission of a lead or opening of an account on facebook / twitter / what ever? Why not measure Visitor behavior after that first purchase / lead / membership sign up (or the first super poke)? Why obsess about the “quickie” that is opening an account? Why not the Visitor behavior in the 30 days after sign up? For your Search campaigns. Or your Display ads. Or Affiliate programs? If you do that, you are not just measuring the “one night stand quickie” what you are measuring is something more of value: Was there a second date? Perhaps a proposal in five months? Maybe a marriage in nine months (because that’s when the baby showed up). : ) We do this so rarely. Success for campaigns (Search, Social Media, Display, TV and what not) is always measured based on that one visit or what happens when the campaign runs. Yet it is likely that you are measuring incomplete success. Let’s do an example. Facebook, in its quest for world domination, runs a world wide campaign (of whatever sort, really, make one up). Success is to increase the membership on Facebook and thus making it a more “valuable” property (so that even in a weaker economy they can raise another billion dollars of cash). So they get another 500k memberships. Success? How about this…. Rather than measuring membership sign ups why not wait 30 days and look at the Recency report for Visitors that came to the site as a result of that campaign? Did that just blow your mind? : ) No? Ok, what you are measuring in the case of Recency is not simply people who signed up BUT rather the behavior of the visitors who signed up. You are measuring if people you acquired from this campaign are visiting the site frequently lot in this 30 day time period after the campaign launched. My hypothesis being that simply signing up is not enough. That’s not enough success (not in this economy baby!). Real monetizable success is: 1) Do those people visit facebook again? And this is key… 2) They visit every day, or every other day, or every few days. The more frequently people visit facebook.com, the more valuable they are to facebook. They see more ads, they super poke more, they friend more, they add more apps, and so on and so forth. All activities (sexy translation: “visitor behavior”) that is...<br/><div align='right'>2009-01-21 10:35:32</div>

Videos: Actionable Web Analytics Tips

Through the years Dr. Stephen Turner and “not Dr only Mr.” John Marshall have had a deep influence on my evolution as a Web Analytics professional. My first foray, all those years ago, into a “big boy” web analytics tool was using ClickTracks (after having dabbled in build your own log parsing ecosystem that dumps data in a massive Data Warehouse that fed a business intelligence tool to do web analytics!!). Over the years that early experience with ClickTracks and conversations with Dr. Turner and John helped shape a lot of my thinking. John Marshall as you all know was the ex-CEO of ClickTracks, and now along with me is the co-founder of MarketMotive Inc which focus on online marketing education and certification. Dr. Stephen Turner wrote what is perhaps still the most widely used logfile web analytics application in the world: Analog (in 1995!). He is also the ex-CTO of ClickTracks. Dr. Turner was in the Silicon Valley recently and I could not resist having him and John come over to do a quick video. Two reasons actually. One they have spent a very long number of years in the field contributing in so many ways, true web analtyics legends to the rest of us. Two, as you’ll see soon, we just have so much fun when we hang out. The question was simple: “If you could reach deep into your experience and share one awesome actionable tip with Analysts & Marketers what would it be?” Here are three answers that you’ll surely find helpful. . . . A bit of history + Guru Dr. Turner shares his tip: Segmentation! (This video is 7 mins long.) Key lessons you’ll learn. . . . Specific examples of what segments you could start with. How Segmentation could help you understand visitor persona. Learn how you can customize landing pages for higher conversions. Dr. Turner’s top secret tip! [Ok, ok you got it out of me: "spend" time with your data!] There are some links at the bottom of this post if you want to learn more about how to use segmentation. In particular I recommend the post about making your reports “connectable”, it is segmentation and also data communication. Let me know what you think of it. Guru Marshall shares his actionable analytics tip: Internal Site Search! (This video is 5 mins long.) Key lessons you’ll learn. . . . How SEO influences what Visitors search on your site. Why gods gift to humanity (ok Analysts :)) is intent. Why it makes sense to mix Guru...<br/><div align='right'>2009-01-13 09:16:55</div>

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