Understanding key customer journeys

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

To talk about this on a practical level let’s take a look at how multiple data sources can be used together to look at a specific business issue such as optimising conversion rates on the site. The simple premise is that if you know who is coming to your site, why they are there and what they are trying to do then you can develop the site to optimise these key customer journeys.

To help digital property owners understand how visitors are interacting with their site we use something called a Customer Journey Framework. This framework is an approach to understanding which visitors are trying to use the site, how they are using it and whether they are being successful in their goals or not. There isn’t a single source of data that will give you the answer to these questions. You need to draw the answers from a suite of different places.

The Customer Journey Framework comprises of three key components:

  • Understanding the different types of visitors (Audience segments)
  • Understanding why people visit the site (Intentions)
  • Understanding usage of the site and the consumption of different content (Content)

Different people come to your site for different reasons and there are bound to be different segments of visitors. The challenge is to work out what the most meaningful segments are for your business that you can use for your marketing and site development activities. This is something we’ll take a look at in the future.

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

Surveys can help you understand if different types of visitors are coming to your site for different reasons. We call these “intention modes”. What is the visitor’s intention when they arrive on the site? What is their 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 it they find something they like
  • To do research for price comparison purposes
  • To buy a specific product that they have already researched
  • To browse around with no intention of buying anything

Visitors in each of these modes will have different goals and will also exhibit different behaviours on the site.

By linking intentions to visitor segments you may find that some modes are more pronounced in certain groups of visitors. For example, in some work for a high street retailer in the UK we found distinct differences in these modes were evident when we looked at it along age and gender lines. In this particular case, older females were tending to arrive at the site with higher levels of purchase intent than younger females. The younger females were looking to be inspired by the site to make a purchase whereas the older females were more likely to already have in their mind what they wanted to buy.

The final link is then to layer these visitor segments and their modes onto the actual content of the site. This is where web analytics data is important and the linking the behaviours that you see on the site back to what you know about visitors and what they are trying to do. So, in our example above are the younger females looking at different types of products than the older females and so do those products need to be merchandised differently on the site to maximise conversion?

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

The framework we’ve looked at here is one example of bringing together data from different sources to get a holistic view of what is happening on the site. It’s also just that - a framework, which can be adapted to suit the circumstances of your own site and information sources.

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