It goes without saying that in order to achieve one-to-one communications suited to each individual customer, companies will need sufficient data for customer insights. But when it comes to the question of what data is needed to deepen customer insight, we often see cases where companies push data-driven management while not having adequate policies to accomplish customer centric jouned with shared understanding across departments.
In terms of approaches companies can take to making effective use of data that contributes to customer experience (CX) transformation, we believe that the key points are setting targets that contribute to cross-organizational operations, establishing neutral CX driving organizations that do not create discord between departments to drive those targets, ongoing personnel development that is not limited to departments in charge of customer contact points, and carrying out internal promotional activities around these efforts, while also formulating a medium to long-term strategy (see Figure 5). Most crucial key point we believe is, to consider policies redefined to fulfil the customer journey that has common understanding across departments, and organizing data that contributes to improve the quality of customer service at each customer contact point.
Recently, all companies have put forward “data-driven management” as a strategy. However, a problem that companies can easily fall into such trap when their back-office departments, which tend to be physically away from the customer, are driving such discussion. The discussion tends to turn into introduction of systems and technology as a goal in and of itself. We feel that there are many cases where, well thought CX transformation shifts away from customer first perspective and becomes project that only to , integrat data management or enhance data extraction and analysis, through means such as building data warehousing (DWH) or customer data platform (CDP) /customer relationship management (CRM), or making use of cutting-edge technologies and tools such as generative AI The data and requirements truly need to contribute to improve customer service quality, however, in many cases those data and requirements remain ambiguously defined. Companies should ask if they are falling into the trap where building DWH or CDP/CRM and introducing solutions has become a goal for its own sake. Also, check data requirements and quality to see if they have ended up using data purely for show due to data quality (e.g., details or clarity of items) not meeting the needs of customer or user departments (i.e.,customer facing staff) , or if the solutions are turning into “mere boxes” where fragmented data is collected. It is also possible that, as a result, not only will these efforts not contribute to improving the customer experience, the astronomical increase in the volume of data needed to keep up with the development of technologies such as big data and AI may pose a greater risk of degradation in terms of maintainability.
Given all, it is necessary for companies to rethink their policies from the ground up, having redefined the customer journey such that there is a shared understanding across departments. From a data integration management perspective, it is also important for companies to acknowledge that they should foster data for deepening customer insight going forward, rather than trying to go from 0 to 100 with their systems all at once, As customer modes of behavior and customer needs are expected to diversify further and become still more complex as society changes, it is essential for companies to continue a regular process of review with the objective of improving customer service quality.