We’ve all been at this analytics thing for a while now, and with all the advances in big data and algorithms and data visualization it’s sometimes too easy to lose sight of some of the bedrock disciplines that got us here.
Big companies are different from smaller ones and the stakes are higher where mistakes are made. Getting these three basics in good working order at the enterprise level makes positive change much more likely than not.
Who’s in charge here?
Many large organizations have content owners across the country or across the globe, and they are often in different stages of analytics maturity. Each team seems to think they have found a solution that works (more or less), but when it comes time to compare a Golden Delicious to a Granny Smith, often enough it turns out your team in Kalamazoo is using a pomegranate. You need to standardize on a platform and make it your benchmark. Each platform measures things in different ways and there is no reliable way to compare (for instance) what a “visit” is in one platform versus another. Most of the time, they won’t match.
The enterprise may also engage a fair number of content-creators, ranging from full-service agencies to bloggers to pure-play development shops. Too many of them also claim they can do the measurement as well as the creation. Avoid this trap by centralizing measurement inside one team that’s responsible for standards, governance, and measurement itself. It may require bringing in a digital analytics consulting team that can laser-focus on these issues. The alternative is a world of rogue sites, unhelpful self-measurement, and no way to roll up results beyond the market level.
2. Data Integrity
The notion of data collection is often remote and mysterious to marketers and decision-makers. They have little knowledge of the details and less control of it. Safety seems to require skepticism about accuracy – and this leads rapidly to nothing at all. Because if you cannot trust the integrity of the data, you won’t be taking any action based on it.
Taming this problem requires standardization (as noted above), but also trust in your analytics team.
Trust is a nice word, but how about verification?
A tag audit is a great place to start. Find someone neutral on content and measurement and have them look at whether the data is being collected properly. Too often, the answers will be less comforting than you’d hoped, but knowing is the first step toward fixing. Are tags firing more than once on a page? Are certain things not tagged at all? Are parameters set correctly so that the data maps to actual reports? Audit today.
3. Content Actionability
The main reason you measure user activity is to find out whether your content is working. This implies that if it turns out the content is weak, it will need to be changed. And how will that happen? Do you have a plan, a process, a regime that addresses this key element to success? Or do you end up making recommendations that never see the light of day? Are politics getting in the way? Is someone’s favorite campaign or favorite agency looking not-so-good after measurement? It’s tough, but this kind of thinking needs to be defeated before you can win at optimization.
Actionability requires human intervention. It requires putting aside personal preference and prejudice and looking at the data. Your mindset needs to be grounded in success metrics rather than aesthetics or alliances. If you want more customer activity and a healthier bottom line, ditch the so-called loyalty to any particular campaign or site design and look at its performance. The race goes to the swiftest car, not the prettiest and certainly not anyone’s subjective “favorite.”
Simply put, you need to have a plan to govern your data collection, then verify its accuracy, then be confident enough to make changes based on what you find out. If you are thinking this sounds pretty hard, you won’t be the first.
But making a success of it brings success to the business. Take immediate steps to fix these problems and you will be glad you did.
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