Don’t Do This! 7 Pitfalls When Deploying Analytics (Part II)
Pitfall #5. Boiling the ocean
When deploying analytics, you should aim to reach as broad as possible across all your customer touchpoints. You need to if you want to be considered strategic. Your ability to optimize and understand the customer lifecycle is directly related to how comprehensive you can be with measurement.However, just because you go broad does not mean you should go deep. It is of much greater value to understand all your touchpoints at a minimum level that to understand a few touchpoints at a maximum level. I’m sure some people will disagree with this point vehemently – and I welcome the feedback. I’ve arrived at this conclusion based on my own experience and this has worked for me and the clients I’ve worked with.
In the world of analytics, it’s better to go wide and shallow, than narrow and deep.For example, I talk with many people who want to measure the daylights out of something like a shopping cart. They measure everything: when someone opens it, when someone closes it, when they add or remove a product, the product size and color, whether they open a pop-up…all this data may be interesting, but it provides them with a false sense of security because the granularity of this data fails to reflect their broader corporate strategy.For example, their focus becomes incredibly myopic and they become obsessed with minute observations like why Firefox visitors convert at a faster rate than those that come in via Internet Explorer. And because they are focused on the weeds, they can’t see that their conversion rate for the broader site has been falling for the past 3 months and they aren’t doing anything about it.In short, trying to “boil the ocean” keeps you from seeing the big picture and operating on your core business goals.
Pitfall #6. Multiple versions of the truth
Analytics success is all about building a baseline for performance (your KPI trend), and trying new things to improve on this baseline. That’s it! That’s why I think it’s easy. I know other bloggers have argued that analytics is hard, but I’ve done this for a living and I can tell you that it’s not. Sure, it can be hard – over time – to continue to improve on your baselines. I’ll grant you that. But that comes after you’ve picked all the low hanging fruit and innovation becomes more critical. But to be fair, I can’t say I’ve ever met with a company that has picked all the low hanging fruit. So from my perspective, it’s just not that hard.With that in mind, a critical pitfall occurs when customers try to use multiple systems to provide this baseline for performance. In other words, they have multiple versions of the truth. Most of you know what I mean.
Take a lead generation site for example, like an automotive manufacturer. They will often looks to total number of leads, among other KPI, to determine their success. But they will measure this with 5 different systems. They have leads as measured by Omniture, then leads as measured by their data warehouse, leads as measured by their email system, leads as measured by their ad server, and leads as measured by search. There may be even more systems than that.The problem with this approach is that you get multiple versions of the truth. And you waste your time trying to reconcile all these different systems rather than trying to improve on your baseline. Now – don’t get me wrong – you must make a best effort to understand why these systems report different results. I’ve spent countless hours doing this. And often times when it comes down to it, there are just fundamental differences in the measurement approach (as we talked about in our approach on Data Migration: Fools Erand). Another example is with clicks. A “click” as defined by an email provider will likely be different than one provided by Omniture which will be different than that provided by an ad server, which will be different than a “click” reported by a search engine. That’s the reality of the world we live in, so just accept it. You can spend your time trying to solve this academic challenge, or you can spend your time improving your business and beating your competition. You decide.
You must have a single version of the truth, because your ability to optimize your business is based on the relative difference between points on the customer lifecycle. It is not based on the absolute relationship.In other words, if a search engine like Yahoo! says you have 1000 clicks, and another analytics provider puts you at 900 clicks, that doesn’t matter as much as when you compare the 900 clicks as measured by your analytics provider for a search campaign against, say, 700 clicks as measured by the same provider for an email campaign. If you compare numbers from the same provider against each other, you can see how different campaigns are doing in relation to each other. When you compare results from different providers, you’re comparing things not based on the same absolute relationship. Therefore, your results are seriously skewed.
Pitfall #7 – Not Teaching how to fish
In my own analytics career, some of the biggest gains we made from analytics were actually from people outside my analytics team. They came from other business units that we had trained to use analytics. Why? Because they often understood their business questions better than anyone else, so they could innovate the most from the analytics data. In other words, they had the best context for the data.To that end a critical mistake that people often make is not training end users to be self-sufficient. Sure you can send users to programs like Omniture University and conduct internal training. That’s great and a critical first step. But once you’ve done this, you should seek out the people that “get it” – and bring them into your inner circle. When you identify these power users and nurture them, they can become your greatest ally and drive some of the most significant gains you’ll realize from analytics – all without any incremental effort from you and your team. This might sound like a fairy tale – but it works – trust me, I’ve done it and I’ve helped other companies like yours do it.
So there you have it. Seven critical pitfalls marketers often fall into when deploying analytics. Granted there are others – but if you go into your next deployment with eyes wide open to these common mistakes – I have every confidence you’ll be more successful than ever before. If you’d like to talk about your unique situation and requirements, we’d be happy to do so. Just give us a call and we can work with you to realign your analytics deployment with your strategic business requirements and industry best practices. It’s what we do best!