How does Customer Intelligence change customer service in retail banking?

Traditional approaches to customer service and the role of sales must be reconsidered and adapted. The supporting use of the gained customer data through Customer Intelligence (CI) in sales to recognize potentials not only enables efficiency gains but also ensures support by the right consultant for the right situation. The consultant is increasingly evolving from a farmer to a hunter. Such changes need to be anchored sustainably and consistently in the process and organisational structure.

Market changes

The market environment in retail banking is changing. Traditional sources of income are eroding, and measures to increase efficiency are indispensable. Individual customer advice is cost-intensive and requires a very targeted approach. Digitization is simultaneously changing customer behavior and – most importantly – customer needs. Customers increasingly handle their financial transactions independently online and visit branches less frequently. Product standardization eliminates the need for a personal consultation in most cases, and the contact center gains acceptance even among wealthy customers.

Customer advisors can, therefore, focus primarily on complex customer issues. These are precisely the concerns for which today’s customers expect highly professional and personalized advice. The ability to emotionalize the consultation, as well as the specialized knowledge, move into the foreground, and displace the importance of building up a long-term relationship. However, due to the lower level of customer intimacy, it is essential to intelligently evaluate the available customer data and thus anticipate customer needs. This allows for a personal consultation with the customer at the right moment through the right channel.

Potential orientation in sales

Traditional static service models, in which customers are divided or serviced according to their assets, are no longer appropriate in retail banking. It involves managing clients in portfolios, i. e., assigning them to an advisor without clients explicitly expecting this. In such a support model, the assigned advisor is responsible for all customer concerns and portfolio analysis and invests a great deal of time in maintaining the customer relationship.

In dynamic customer care models, the efficiency of customer care can be significantly increased by using Customer Intelligence (CI). Data-driven potential recognition covers the entire customer base and is no longer limited to those customers with whom the bank is currently in contact. Leads generated by CI become the central sales instrument in potential-oriented, dynamic customer care.

In line with the strategic orientation, data evaluation campaigns are programmed, which – according to defined criteria – supply leads to the appropriate customer consultant and thus provide him with information on a possible sales opportunity. This allows the client-advisor to target clients with potential and to focus his advice on topics that are most likely to result in transactions. The client advisor can thus ensure that the client receives the best possible advice regarding his individual needs.

The flexibility in cross-channel customer allocation is of great importance for the success of such a dynamic support model. The interaction between the various channels and organisational units must be clearly regulated and anchored in the processes. The role of the consultant and the skills required are changing. Instead of traditional customer relationship management on a limited customer base (farming), consultants must address a much larger number of customers based on leads and process them without a relationship history (hunting).

Successful incorporation into the organisation

To successfully implement a dynamic care model and a CI-supported working method, it is necessary to create the following prerequisites:

• Orientation of customer advisory services towards the exploitation of identified potential (“hunting”) and incorporation in the sales business process model
• Alignment of sales channels and their responsibilities based on the new business process model and creation of a continuous data information flow
• Changed sales management – away from fixed sales targets to more dynamic, lead-based targets
• Initiating a mindset shift among customer advisors as well as strengthening their ability to quickly inspire customers and emotionalize sales conversations, even without the basis of a personal customer relationship built up over many years
• Structured preparation of business and customer data as a high-quality data basis and development of a data analytics competence
• Further development of the appropriate CRM and consulting tools to optimally support the customer advisors in their new way of working