Data for greater returns from customer journey optimization
You can optimize a customer journey faster and better through smart use of data. In this article: How do you make data work for you in improving the customer journey? And how do you start if your name isn’t Google or Amazon?
Customer experience = the new differentiator, data = the new oil
In many markets, the product is increasingly becoming a commodity, and companies need to differentiate themselves primarily on the experience they offer customers. So many companies start improvement programs with keywords: customer journey and customer experience. What is striking is that in most cases these programs are largely based on gut feelings rather than hard data on customer behavior and experience. And that’s crazy, because there are many studies that show faster-growing companies are basing their decisions more often on data. Where does it go wrong?
We see two challenges that many organizations struggle with in effectively leveraging (customer) data:
- The structured capturing of data and the accessibility of data. For more information on this one, read our article on data capturing and accessibility requirements on the road to a 360-degree customer view.
- Taking a clear approach to making improvements based on data.
In this article we address the second challenge. With which approach do you make data work for you in improving customer experience and how do you circumvent common pitfalls?
Data analysis to determine most promising directions for improvement
You want to improve the customer journey, so you just get started immediately with ideas or you talk to some customers? Sounds logical. Yet you can waste a lot of time on things that will contribute little to real improvement. Data analysis provides direction here by offering insight into the most important impact factors for the organization’s goals. Think about: Where do most customers drop out? Which customer profiles exhibit the undesirable behavior? Which contact channel performs worse than others? With these data insights, you come up with more focused ideas in the design phase, and any in-depth customer interviews also gain a lot of momentum. After all, you then know which customers to talk to about which topics.
Analyze at 3 levels; touchpoint, service level, context
The analyses you can perform of course depend on the data maturity of the organization. For example, in order to deploy analysis techniques such as process mining, it is necessary to be able to link data from different source systems. Thanks to this linkage, insight can be gained down to the customer level into which touchpoints customers actually go through, in what order and in what time frame. Besides the bottle necks in the process that become visible, it almost always turns out that many customers go through a different customer journey than the organization thinks.
But other, more pragmatic analyses can also lead to sharp insights. Regardless of the technique used, analysis generally focuses on three levels:
Level 1 – touchpoint: what is the impact of (not) experiencing a touchpoint?
First, the analysis focuses on which touchpoints (e.g., an email or phone call) have the most impact on the organization’s goals. Here we look at whether (not) going through a touchpoint significantly affects customer experience and behavior. How does (not) receiving an after-service call affect customer satisfaction and churn? Or downloading a product information brochure for conversion and cost to serve (due to possibly fewer customers requesting the same information by phone)?
Level 2 – service elements: what is the impact of the type of service we provide within a touchpoint?
A touchpoint can be completed in different ways. Take for example the touchpoint ‘customer calls with a question about X’. Some relevant service elements here: response speed, duration of contact, first time fix, employee expertise (general helpdesk vs. specialist), etc. An analysis at this level provides insight into each service element’s impact on business objectives and where the main opportunities for improvement lie.
Level 3 – contextual factors: what impact does the customer’s context have during the contact?
Often factors outside the organization also influence customer perception and behavior. Think of the weather, season, time of day, age, family situation of the customer, etc. These are often factors over which you have little or no control, but which you may be able to anticipate. Consider the insight that a service call early in the evening is better appreciated than a phone call during business hours.
Several pitfalls lurk during the analyses described above, which we see many organizations struggle with in practice. Read how to avoid them in this article.