The Answers Data Can Provide
The following three use cases are based on data that is generated in the daily business of most organizations but is not used systematically.
Learning from Purchasing Behavior
Order data often contains at least the answers to the following questions: Who is buying? Which services? How often? When? Which contact or promotion triggers the purchase? Additionally, data on complaints and their content, returns, and repurchase rates provide insights into the satisfaction of individual customers and its causes, as well as the quality of services.
Together, this information can help estimate purchase probabilities for specific services, predict return rates, and identify and prevent churn and its causes at an early stage.
Controlling of Marketing Activities
A wealth of data on communication resonance makes marketing measurable, such as clicks, unsubscribes, product study, dwell time, reactions to mailings, or presence at personal events. This makes it possible to see which content works for which target groups. This information enables the reduction of wastage as well as the customization of content and frequency based on customer preferences.
If this data is combined on an individual basis with realistic contribution margin estimates, then Customer Lifetime Value analyses provide indications of which customers justify support at the planned intensity at all.
Optimization of Activities
The third point follows inevitably from the previous two: the use of data to specifically estimate potential. This allows organizations to predict for each customer how they would react to different services, to up-selling or cross-selling suggestions, or to other communication—and whether this reaction probability could be increased through individual adjustments.
This knowledge leads to consequences for sales, product management, or communication, such as the design of personalized offers, cost-benefit calculations for expensive measures like visits or incentives, as well as guidance for formulating personalized messages.
Of course, for all these questions, organizations require a sufficiently large database that must be prepared in a structured form before these analyses can be started. Once this framework is in place and the questions are answered using appropriate statistics, this investment continuously provides the desired answers with little additional effort, updated regularly, and thus forms the basis for both more effective and more efficient action.
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