Creating data-driven clarity to customer experience management
Many are interested in the formation and improvement of customer experience, but its intricacies perplex how this could be managed. The simple answer seems to be more data. Statements such as “If you can’t measure it, you can’t improve it” and “Knowledge is power” have valuable kernels of advice, but faulty interpretations have led to masses of unused data due to sheer volume or a lack of meaning. This second post follows on the first and concentrates on methods to find the right customer experience metrics, how to utilise those indicators to drive better business, and a concrete approach called Value Creation Model to make sense of customer experience.
In the information age, leaders can identify the important information among heaps of data. Our working memory is very limited in terms of information processing and retention. Furthermore, even detailed analyses with more resources tend to suffer if a larger range of variables is introduced. There must be a way to narrow down the interrelated and dynamic factors. The decisive question is: What information do you use to make decisions?
Finding the right metrics
The nature of the above question determines that each organisation and their decision-makers require tailored information. Before anything, you must know your customers. Who are they and what are their critical issues? While most industries have distinct characteristics and widely used KPIs, organisations still position themselves according to their customers’ preferences of goods and services. To put it simply, each organisation must identify their own specific customer experience metrics to use.
Reflect on a hypothetical example of a steel producer. Its typical clients need and value high quality customised steel components fast. Decomposing these requirements indicates the company’s offering must emphasize high quality, tailoring to specifications, and quick delivery. The first, high quality, could be tracked with standards and number of defects. Customisation is tricky. One option is to quantify how close requirements can be met balanced with appropriate costs. Finally, delivery times from the initial request to customer fulfilment should be examined.
The three high level metrics provide actionable comparison points to assess and adjust customer-oriented performance. Other indicators can also describe their development, such as working hours for customised products and demand forecasting accuracy. Using ever more precise metrics allows guiding of specific activities. But, aren’t we measuring everything again? Not necessarily. We are disaggregating the essential pieces that create customer experience as a whole to make data-driven decisions from bottom-up.
Leading with metrics
The fundamental distinction between our newly deduced operational metrics and holistic measures like the NPS is leading versus lagging. Leading indicators, such as production step lead times, signal how things will evolve in the future, while lagging indicators follow future events. Revenue and customer experience are results of other forces so belong to the latter category. Leading indicators enable a joint understanding of customer experience for different organisation functions and levels. Employees should have measurable objectives that connect their work to the end user, which leads to increased comprehension how their input advances the organisation and raises motivation.
The Value Creation Model is an example of a nifty tool to further structure and share customer insight. Its roots are in systems thinking. Organisations can be mapped as connected nodes, and the links can be reinforcing either in the same or opposite directions. A visual management tool designed from the organisation system presents the interrelated results of all activities from back-end coding to customer satisfaction. Finally, one may find value paths, work flows through the system that create the most end-user value, to prioritise the leading business drivers.
It is almost mandatory to have information systems and other tools support decision-making nowadays. Yet, the weight should be on “tools” and “support” as they only communicate states and their changes. Any performance management itself is not a silver bullet for results. Objectives should be set, and initiatives started to reach those goals. Most importantly, trends suggested by metrics must be judged objectively to polish final decisions with qualitative insight and experience.
Comprehending and quantifying customer experience is difficult. Customer-based leading indicators lay a foundation for data-driven customer experience management on all organisation levels, while aggregate scores are useful to analyse success in retrospect. Top management should also understand how their internal measures compare to rivals. Even if customer experience itself is not very comparative, benchmarks offer a nice view of competition and positioning. Lastly, metrics are not a means to an end. The challenge is to act on information and change. In our third and final blog we discuss how to create a sustainable customer-oriented culture and how management could change in the future.