Creating data-driven clarity to customer experience management

Vili Nieminen

Many organisations are interested in the improvement of customer experience, but its intricacies confuse how this could be achieved. The simple answer seems to be more data. This post 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.

Statements such as “If you can’t measure it, you can’t improve it” and “Knowledge is power” are valuable pieces of advice, but faulty interpretations have led to masses of unused data due to sheer volume or a lack of meaning.

In the information age, leaders have trouble identifying the important information among heaps of data[1]. Our working memory is very limited in terms of information processing and retention[2]. 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?

Identifying the right metrics

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 make it or fail 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 that are delivered 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 identifying 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 phase lead times, predict how things will evolve in the future, while lagging indicators follow future events[3]. Revenue and customer experience are results of other forces, and as such, belong to the latter category. Leading indicators enable a joint understanding of customer experience for different organisational functions and levels. Employees should have measurable objectives that connect their work to the end customer. Clear customer-oriented goals raise motivation by helping the personnel to understand how their efforts advance the organisation.

The Value Creation Model is an example of a nifty tool to further structure and share customer insight. With its roots in systems thinking, it is a visual management tool that presents the interrelated results of all activities from backend to customer satisfaction. One may find value paths, workflows through the system that create the most end-customer 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 “support” as these tools 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.

Understanding 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 comparable, benchmarks offer a nice view of the competition and positioning. Finally, metrics are not a means to an end. The challenge is to act on information and change. That is why you need to know how to create a sustainable customer-oriented culture and how management could change in the future.

This blog article has been published with permission from Qentinel. It has been republished at

Vili Nieminen is a customer-oriented business student from Aalto University. In addition to these topics, he is interested in IT and outdoors exercises. Vili writes on behalf of Qentinel, one of our CX Masterclass partners.




[2] Measures of Short-Term Memory: A Historical Review



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Ian Golding is a renowned CX specialist, who advises leading companies with their CX strategies, as well as measuring and developing CX and listening to your employees. He is the first one to have achieved the Certified Customer Experience Professional certificate and acts as CXPA UK Ambassador.


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