Customer Journey Framework

Over the past few weeks, I’ve been looking at the variety of sources, in addition to the data that comes from your site, available for evaluating e-business performance. These additional sources include audience panels, surveys, and focus groups. I’ve also been making the point that purely focusing on Web analytics data rarely provides the full picture.

Let’s talk about this on a practical level. Multiple data sources can be used together to examine a specific business issue, such as optimizing site conversion rates. The simple premise is if you know who’s coming to your site, why they’re there, and what they’re trying to do, you can develop the site to optimize these key customer journeys.

To help digital property owners understand how visitors interact with their site, my company uses the Customer Journey Framework. This is an approach to understanding which visitors are trying to use the site, how they’re using it, and whether they’re successful in their goals. There isn’t a single data source that can answer all these questions. You must draw the answers from several different places.

The Customer Journey Framework has three key components:

  • Understanding the different types of visitors (audience segments)

  • Understanding why people visit the site (intentions)
  • Understanding site use and the consumption of different content

People come to your site for different reasons; there are bound to be different visitor segments. The challenge is to work out the most meaningful business segments, then use them for your marketing and site development activities. This is something we’ll take a look at in the future.

Working out who’s coming to your site is where you might use audience panel data, surveys, or internal data from customer registration databases. You may need all three to build a true profile of the different user types you may have. Audience panels can provide a demographic profile (if your site is large enough), but they may not help you segment your audience in a meaningful way.

Surveys can help you understand if different types of visitors come to your site for different reasons. We call these “intention modes.” What’s the visitor’s intention when she arrives on the site? What’s her goal? To use an e-commerce example, a visitor may come to a site in one of these modes:

  • To browse for something and buy if she likes it

  • To do research for price comparison purposes
  • To buy a specific product she already researched
  • To browse with no intention of buying

Visitors in each mode have different goals and exhibit different site behaviors.

By linking intentions to visitor segments, you may find some modes are more pronounced in certain groups. In some work for a main street U.K. retailer, for example, we found distinct differences in these modes when we examined age and gender. In this particular case, older females arrived at the site with higher levels of purchase intent than younger females. The younger women needed to be inspired by the site to purchase, whereas the older women were more likely to already know what they wanted to buy.

The final link is to layer these visitor segments and their modes onto the site’s actual content. This is where Web analytics data is important. Link site behaviors to what you know about visitors and what they’re trying to do. In the above example, are the younger women looking at different product types than their older counterparts? Should those products be merchandized differently on the site to maximize conversion?

Linking behavioral and profiling data can be tricky. It’s certainly easier if you can identify at least some of the site’s visitors through a registration process or transaction. You can match profiling data captured in that process with actual behavior on the site and use that information to generalize for all traffic. It’s also possible to link survey response and site behavior data, though certainly here in Europe you must be mindful of privacy concerns around identifying individuals.

This framework is one example of bringing data from different sources together for a holistic view of what’s happening on a site. It’s also just that — a framework. It can be adapted to suit your site’s and information sources’ own circumstances.

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