Non-Line Blogging

Avinash Kaushik has been de-mistyfying the world of web analytics for years through his excellent blog Occam's Razor.  He took us deeper into the murky world of Java-script tagging and standard reports with his comprehensive "Web Analytics:  An Hour a Day" in 2007.  Now he has written a book that all digital marketers should buy, read and leave on their desk to refer back to on a regular basis. 

Web Analytics 2.0 shows us how to move from shovelling  buckets of meaningless "clickstream" data around our organisations to developing a love for true insight.

In short he encourages us to move towards adding qualitative data to our limitless supply of quantitative data in order to really understand what people are doing on our sites.  We must learn to use our hearts as well as our minds.

Lets take a simple example - that old favourite of "Engagement".  Marketers run so many analytics reports to get a fix on engagement that the lights in most offices regularly dim.  And the bad news according to Avinash is that you will NEVER be able to measure how much people are enjoying themselves on your site just with the click-stream data.  For instance, to paraphrase Avinash, 2 people visit your site and spend 10 minutes looking at 12 pages.  Both  happy right?  One loved your site, but the other was frantically trying to find some content and gave up after 10 fruitless minutes - you will never ever ever know this just from your data.  By adding some qualitative data (an on-site survey?) we have more chance of finding out how satisfied, not engaged, these 2 visitors were.

So Avinash takes us on a journey to show us where we should be using our hearts to make sense of data.  On our way we look at the need to move away from once a week reports to continuous streams of  meaningful data;  we are constantly reminded that customers, not marketers, are the best people to inform us what our site should look and feel like; and we are taken on a guided tour of the mountainous areas of competitive insight and told how to mine it profitably.

But this book does so much more than just change the way you think, critical though that is.  It shows you what buttons to press to make your reports more actionable, tells you what sites to look at when considering additional solutions and gives clarity to virtually all the web analytics jargon terms.  Some of the content will be familiar to regular readers of his blog (like the excellent explanation of multiple-tab time on site calculations!) but that makes this even more of a reference book for all our analytics needs.

As you may know I am a huge fan of testing everything that we do in digital marketing and so the chapter titled "Failing Faster:  Unleashing the Power of Testing and Experimentation" took me around all my favourite sites in the digital marketing landscape:  A/B testing, Multi-variate testing and some really sound advice about where to start and a few quick wins to get you in the mood!  Here is my favourite slide that I use to introduce the issue of testing in my courses...I'm sure Avinash would not disagree!

Avinash shows us that web analytics is woven into all our digital marketing activity - from search to site usability and email campaign analysis to off-line integration.  I even spent a rewarding few minutes simply reading the sub-heads and being reminded of things we ought to be doing all the time: 

• Segment or go home
• Five Rules for creating a Data-Driven Boss
• The Key to Glory - Measuring Success 
• Context is Queen
• Failing faster - unleashing the power of Testing and Experimentation

So there we are.  Web Analytics 2.0 is a digital marketing book that takes you from thinking differently to doing better, packed with explanations about the things we ought to know about (or showing us how wrong we have been!).  It comes with a CD brimming with Podcasts, Video and Powerpoint material as well as lists of additional resources.  He even finds time on page 400 to mention Non-line Blogging as a resource people may want to use!  It's taken me 2 weeks to work from the start of the book to the end but it's been a fantastic journey...and at over 450 pages you may want to pack a lunch before you set off! 

We've come a long way with on-line analytics in a short period of time.  A couple of years ago we were all relatively happy with the "last click wins" referrer model and merrily shovelled money into Google's bank account.  Today we are more likely to obsess about attributing a fair percentage of a sale to the efforts of a number of digital activities;  as we always knew, somebody may have seen a banner and clicked on an affiliate link BEFORE using a search engine to find your site, so we'd better juggle our advertising spend accordingly.  But how far should we go down this attribution path, and if you give up too soon what effect will this have on how we judge success? 

Let's walk through the process.  Using a simple analytics funnel we can see what source generates a "successful outcome" - could be a sales lead for a BtoB organisation or a booking for a holiday company.  I've not included any time scales in these examples as the period from trigger to successful outcome will vary from one product to the next.

As I've already mentioned, we've probably come to terms with the over-simple view of the "last click wins" attribution model.  We may even be able to link together all our on-line advertising activity and identify dates of key events like "viewing one of our display ads on-line" or "clicking on an advert on a partner site".  We can then get a feel for the on-line customer journey (as we feel we have influenced it)

Most marketers seem to be at this point and we can get really excited about deciding what is a fair way to atribute the relative importance of all this channel activity.  However, let's not forget the importance of social media at this point - does somebody who goes to a LinkedIn discussion forum or plays with a Facebook widget mean we should give them some credit?  If we're not careful we overlook some of the digital experiences people may have, simply because we may not have tracked them in the past.

One that is relatively easy to track is the presence of email in the overall journey, but as this is often not included as part of the "advertising tools" we may under-represent the role email plays.  It does not usually sit with the "acquisition tool" family and is often overlooked when implementing multi-source tracking like DoubleClick's Floodlight or the newer offering from TagMan.   Matching an email file to a list of "successful outcome" email addresses may yield this multi-channel impact, but this is a manual, somewhat "clunky" production process. 

However, the big problem with our lovely "closed" view of our customers' mind is that it is frequently polluted by mucky, grubby off-line advertising.  Maybe it was a print ad that stimulated the click on a banner, or perhaps a direct mail pack thumping onto somebody's door mat that promped a branded search, as we can see below..

So the conclusion is a bit worrying.  No matter how much we obsess about our digital customer journey and no matter how clever we get with our attribution algorithm, we may have got the "demand generator" completely wrong.  As there is no way to isolate all off-line noise from our customers' minds, and whilst it is still rare (but not impossible!) to track off-to-on-line conversions we are happy to pretend that we have got a really accurate fix on how we get our sales.  Maybe we are not making as many strides in the world of attribution analytics as we think, and are happy to stumble on with our tracking tags.

And if you think I'm being a bit harsh, ask yourself this question:  

If one of your prospects is on the cusp of becoming a customer, will sending them a direct mail pack or making an out-bound phone call help to nudge them towards a branded search on Google?  

If your answer is, at worst, a reluctant "probably", maybe we need to re-think how much value single-channel attribution modeling really delivers and worry less about divvying up our on-line budget between a few suppliers.  Perhaps we should even be trying to invest more in reaching people off-line at the right stage of the digital process? So there.