When updating and optimizing analytics functions the final product is only as good as the preparation. These insights can help you execute an effective plan prior to code implementation.

It's exciting to know there are tools that can deliver useful insights about your digital properties. They're more sophisticated than ever and the possibilities are enticing - even hypnotic. You've probably heard all about how you can optimize content based on visitor history, how you can retarget based on engagement scoring, and how so-called "predictive" analytics can deliver more valuable customers.

At some point, you may decide to improve your analytics and take advantage of these advanced features. Of course, you will need your technologists and developers to be heavily involved. Here is where you encounter your first major challenge: you need to hold them off a bit. If they're like most developers, they're really smart and want to get right down to work. However, you must resist their enthusiasm and inform them that you need to do some studies and make a plan first.

In other words, you need to stay out of the weeds - at least in the beginning.

Key Performance Indicators

We've heard lots about key performance indicators (KPIs) by now. KPIs are the measurable items that tell you how well you're progressing against your goals. KPIs are also part of every digital analytics plan.

But proper planning goes well beyond just deciding what you want to know. The
problem is that the analytics tool doesn't know what you want to know, and there are no buttons that enable you to make it to tell you this. Instead, you will need to work with measureable data to determine approximations and proxies that infer the information you seek. For instance, if you want to know how visitors from (Read more...) particular campaign are engaging with your content, you have to plan these measurements well before data collection.

The key to useful KPIs is the planning and execution of data-collection protocols, visitor categories, and metrics in a coherent manner. Planning has to come first. Otherwise, you will be too prone to error, inaccuracy, and irrelevancy if you get down into implementation without a business context.

In addition to context, KPIs need data support. Using the above example - wanting to know the characteristics of a visitor arriving from a particular campaign - you will need to match that business objective with a list of campaigns, followed by a segment of visitors sourced out of that campaign or many campaigns.

Then, match this objective to a variety of measures, including one-page visits (bounces) and content engagement. Content engagement is often based on an amount of pages, time spent on site, or content groups. It's also likely you will have to measure with some form of funnel analysis in order to show how certain visitor types or all visits proceed through some of your expected pathways. In this instance, you should plan out what the expected pathways are and allow for some variables: visits can vary widely in behavior while still showing some adherence to a pattern.

How Planning Effort Equals Insight Reward

If all this seems complex and even daunting, it's probably because it is. The most valuable results from analytics exercises are often based on complex planning. That said, you need to listen to your key business stakeholders and hear the often- straightforward things they want to find out.

They may say things like, "attract more customers" and "sell more stuff." Your job is to channel these demands into something that an analytics tool can actually measure - and I don't just mean shopping cart metrics. The next step is to create a document that shows the correlation between your high-level requirements and the specific dimensions or segments, with the metrics that populate those categories. You can only go so far in this exercise without knowing what's really possible in your analytics tool, so if you're not knowledgeable about that already, you need to work with someone who is.

Here is a simple plan for planning:

  1. Ask questions of your stakeholders. 
  2. Translate that information into something measureable. 
  3. Match those measureable items with actual measures in your tool. 
  4. Document this carefully. This "requirements document" will drive your entire analytics project.

And if you plan well, you'll find yourself much closer to understanding what's going on and perhaps even be able to improve it. Only good planning can yield results that deliver on the "predictive" and "optimization" promises made in analytics. Without it, you're lost in an open field of data just scraping around in the weeds.

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