Anticipating customer preferences

Record Bank: SAS analytics improves campaign returns

Record Bank is a rapidly growing Belgian retail bank. They recently launched a project to optimize their marketing campaigns addressing newly acquired customers. By using SAS analytics on the early customer history, they determined the type of offers these customers appreciate most. They also identified the most appropriate moment to send out these offers. Initial results show that by sending the right offers at precisely the right time, Record Bank is improving both campaign perception and return.

The combined SAS analytics enabled us to develop specific roadmaps for each customer group that stimulated their purchasing behavior
Christian Colot

Christian Colot
Head of Customer Intelligence, Record Bank

Record Bank: SAS analytics improves campaign returns

Record Bank is a subsidiary of ING Banking and Insurance. It has grown out of the merger of four small banks in 2001 and acquisitions of several other banks in subsequent years. It is currently the third largest retail bank in Belgium, employing approximately 750 people in offices in Brussels, Ghent and Liège. Commercial activities operate through a network of external points of sales and offer the classic portfolio of current accounts, savings accounts, time deposits, bonds, pension funds, mortgages, personal loans, and debit and credit cards.

Follow-up of newly acquired customers

Christian Colot is the Head of the bank's Customer Intelligence team. One of his tasks is analyzing the effectiveness of the past and present marketing and sales campaigns. In 2012, the Marketing Department asked him to investigate whether the marketing campaigns addressing newly acquired customers could be optimized. "Our Marketing Department helps improve the performance of the points of sales," explains Mr. Colot. "They regularly send out mailings to existing customers offering additional products from our portfolio. However, they must be rather prudent in sending such offers, and not just for budgetary reasons. Customers tend to find it obtrusive when they receive too many mailings. So increasing the number of mailings was absolutely out of the question."

Analyzing purchase behavior

Instead of increasing the number of mailings, Marketing and Customer Intelligence agreed to focus on the content of the offers and on the timing of the mailings. Mr. Colot and his team then began an analysis of the purchasing behavior of newly acquired customers in the recent past. "We sorted customers into groups based on the product they first purchased," he explains. "The first product is often a savings account, but sometimes it is a current account with a credit card, a pension fund, or some other product. We then made a statistical analysis of each group's purchasing behavior in the twelve months following their initial purchase, taking into account all the offers they had been receiving along the way."

Combined analytics help build roadmaps

Statistical analysis of the different customer groups meant combining various data sets in the bank's CRM database. "The analytics in SAS® Enterprise Guide and SAS® Enterprise Miner proved to be a great help in that task," says Mr. Colot. "We used the Sequence Discovery technique to analyze the purchase trajectories of each customer group. For example, product A typically leads to purchasing D or E and then maybe H. We also used the Association Discovery technique to analyze the customer's entire product portfolio after one year. This enabled us to uncover and emphasize the sometimes hidden links that exist between our different products. The combined analytics enabled us to develop specific roadmaps for each customer group."

Stimulating the natural purchasing behavior

A second analysis focused on the timing of the mailings. "We wanted to find out at what point it is best to send additional offers to fresh customers," notes Mr. Colot. "Would it be useful to send offers two months after the initial purchase? After five months? Nine months? Using SAS analytics, we in fact discovered that in the first few weeks and months after the initial purchase, many new customers had already decided on their own to purchase additional products. However, this natural purchasing behavior virtually comes to a halt after three months. Based on such insights we developed a smart mailing strategy, ensuring that each customer receives offers at the most appropriate moment, stimulating his or her natural inclination and leading to a richer total product portfolio."

Approaching customers in a differentiated way

The first set of customer roadmaps and associated mailing strategies have now been in place at Record Bank for twelve months. Additional roadmaps are gradually being added. The initial results are even more promising than what was hoped. "I'm hardly surprised by that," observes Mr. Colot. "After all, what we do is not so different from a baker's or a butcher's commercial approach. They know their customer's preferences well and will suggest new products based on this insight. We are doing the same thing, except of course on a much vaster scale, dealing with tens of thousands of customers each year. We have learned that it is particularly beneficial to approach our customers in a differentiated manner."

Record Bank

Challenge

Keeping newly acquired customers actively onboard.

Solution

SAS® Marketing Automation

Benefits

  • Improved campaign perception and return
  • A specific roadmap for each customer group can be developed

Lessons learned

  • Customers do not necessarily purchase the most popular products next. Analytics made it possible to discover what products they find most interesting.
  • It is wise to develop a differentiated mailing strategy that enables you to take into account the natural purchasing behavior of the customer.
  • Intensive cross team collaboration, including Marketing, Sales and Customer Intelligence, was of paramount importance in this project
The results illustrated in this article are specific to the particular situations, business models, data input, and computing environments described herein. Each SAS customer’s experience is unique based on business and technical variables and all statements must be considered non-typical. Actual savings, results, and performance characteristics will vary depending on individual customer configurations and conditions. SAS does not guarantee or represent that every customer will achieve similar results. The only warranties for SAS products and services are those that are set forth in the express warranty statements in the written agreement for such products and services. Nothing herein should be construed as constituting an additional warranty. Customers have shared their successes with SAS as part of an agreed-upon contractual exchange or project success summarization following a successful implementation of SAS software. Brand and product names are trademarks of their respective companies.

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