Situation
Rabobank is a cooperative in which all banks of the Rabobank Group, the largest financial services provider in the Netherlands, have combined their forces. Headquartered in Utrecht, Rabobank functions as the general and technical services company of all 328 affiliated Rabobank offices.

Critical Issue
Rabobank aims to strengthen its position as a market leader in financial services by further developing and expanding its relationship with its private and corporate customers. Information about and knowledge of each individual customer and market segment is essential for achieving maximum return on customer contacts and marketing efforts. Within the Marketing Intelligence department, a team of database marketers and market analysts continuously strives to optimize the market and customer information to generate qualified leads that will support the local branches in effective cross-, up- and deep-selling initiatives.

In doing so, the emphasis is on combining three kinds of information: information related to planned outbound interactions such as mailings and call center campaigns, “event-driven” customer contacts (based on events that could change customer behavior and needs), and “incoming customer signals” (in which case, the local bank is informed by the central CRM system about possible leads with existing customers). Combining the details of all interaction forms results in a complete picture of the customer, his or her needs, and the best channels to approach him or her with new offers—a truly effective marketing plan.

Solution
Rabobank uses predictive analytics software to create and execute targeted direct marketing and lead generation campaigns. Predictive analytics enables Rabobank to accurately target its customers and considerably increase the success rate of its actions. Acquisition, cross-selling and retention campaigns are created for various financial products, such as savings, loans, mortgages, and investment funds. The software can indicate, for example, the likelihood that a customer who now holds a savings account will also invest in stocks or shares if he or she receives a specific offer to this end. In order to do so, the software analyzes customer events. For example, when a customer transfers a large sum of money to his account, that could be an indication that he or she is interested in an investment product. However, Rabobank must respond quickly; already within a week the customer may have transferred the amount to a competitor. Predictive analytics enables Rabobank to give a timely response by quickly sending a lead to the responsible financial advisor.

Rapid Start
Every month, the Rabobank Group initiates two or three large marketing campaigns and tens of sub-campaigns for private and corporate customers. In addition, five large seasonal campaigns are launched each year. To ensure that actions can be quickly set up and carried out, predictive models have been developed, which automatically link the right target groups to the most suitable channels and result in a highly effective selection. Predictive analytics enables marketers to deploy these models immediately, without having to perform complex analyses over and over again.

High Quality Leads
"Eventually, as a financial services provider, we aim to have more frequent customer interactions, though we will only focus on those actions that really meet the customer's needs," comments Paul Groenland, project manager database marketing at Rabobank. Optimizing the profitability of our interactions with private and corporate customers is key for the future development of the group. By synchronizing customer data with the analytical environment in real time, the result is high-quality and up-to-date information serving as the basis for marketing actions. The offices are informed on a daily basis which customer should be approached with which offer and via which channel. As a result, marketing initiatives will become much more profitable and costs can be reduced significantly."