Everyone is talking about the importance of the customer journey, but few are talking about how the use of data informs it. Due to the complex nature of how customers purchase products in today’s digital world (in-store, real-time price comparisons, buying online), customer journey analyses are more complex than traditional marketing analyses. To evaluate a customer journey, it’s important to choose visualization techniques that can showcase customer behavior and track movement over time.
A customer journey analysis focuses on using data to step into the shoes of the customer to understand how that person is engaging with your brand. From initial interaction to purchasing, customers take various steps to becoming a loyal brand advocate. It’s critical to not only go out and talk to customers about their experiences, but also analyze the clues from their digital footprint with every touchpoint. A digital footprint is important because it provides essential puzzle pieces that are not often revealed in conversation.
Digital data comes in all shapes and sizes, but generally include:
- media (both outbound channel media that are served to your customers, and inbound channel media when someone searches for your brand),
- transactional purchases
- site activity
Customer journey analysis begins by storing all relevant data in a centralized location. Generally, data aggregation becomes a customer event stream, which is a comprehensive view of your customers where all the disparate data events — media, activity, and transactional — are collected, combined, and then stored in a centralized location. Customer event streams vary in complexity — they can be as simple as combining multiple data sources into one database table or very complex with multiple tables and look-ups. The differentiating component of an event stream is that the data are stored both at the customer and event level. It is critical that each event stream has a unique customer identifier and a timestamp so that you know what your customers did at a specific point in time, as well as when they did it.
What distinguishes customer journey data analyses from more traditional marketing data analyses is the importance of capturing the element of time. For example, line charts, transition matrices, and jump plots are great visualizations techniques for displaying changes over time. Below are examples of each:
1. A retail company runs a campaign in which a coupon is mailed out to customers following an initial purchase.
Surprisingly, if you plot when the coupon was redeemed, you find that many customers wait until the last possible day before the coupon expires to redeem it. Why is this happening and do people need an additional reminder or more time to make the subsequent purchase?
2. This transition matrix shows the order in which users are exposed to digital advertising, and if the user continued on to make a purchase.
Forty-six percent of users receive a display ad as their initial touch, followed by a social ad at 31%. Mobile display is a standalone channel meaning that someone who is exposed to a mobile ad is unlikely to have any other marketing touches. Following a paid search click, users have a 27% likelihood of seeing a display ad, so it’s critical to align the messaging in the served display ad with the same search keywords they used.
3. Jump plots are useful for showing movement between defined journey phases.
Unlike a typical sales funnel, jump plots showcase movement forward, backward, and even skipping phases as well as the amount of time spent in each phase.
The arcs on the top portion of this jump plot show forward movement, whereas the arcs at the bottom are backward movements. The arc height represents the number of days that the customers (in the same journey) take to move from one stage to another.
Here we can see that customers are moving backward at the same rate as they are moving forward, which indicates that there is a barrier in the journey that is preventing customers from proceeding in a linear progression. This is an indication that customers are getting stuck or the journey phases are not well defined.
No customer journey project is complete without analyzing data to identify customer behaviors. Using data visualization techniques that capture movement over time is key to understanding what people actually do versus what they say they do.
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