Database Marketing simplified through Data Mining Author*: Dr. Ing. Arnfried Ossen, Head of the Data Mining/Marketing Analysis Competence Center, Private Banking Division, Deutsche Bank, Frankfurt, Germany Preface: With assets of DM 1,200 billion (1998), Deutsche Bank is one of the world s leading financial institutions. It offers a comprehensive portfolio of products and services. Business activities are divided between five divisions, all with international operations: Retail & Private Banking, Corporate and Institutional, Investment Banking, Asset Management and Transaction Services. The merger with Bankers Trust of America has created a truly global player with a transatlantic platform. Bankers Trust has enabled Deutsche Bank to strengthen its expertise in the area of investment banking. February 1, 1999, heralded a new era in Deutsche Bank s Private Banking Division. Germany s leading financial services provider has developed a marketing communications concept designed to set new standards in terms of the quality of advice and service given to customers. The corresponding information technology (IT) support will be implemented in the course of the INCCOM (Integrated Customer Communications Management) project. A key role is played by data mining: the aim is to develop models for retaining existing customers, winning new ones, for cross- and up-selling, and for the simulation of the impact of marketing campaigns. The ultimate goal is to increase the return on investment (ROI) of marketing activities in the Private Banking Division. Introduction: Competition in banking has never been fiercer, and like all other financial institutions, Deutsche Bank must pay greater attention to customer service. The bank needs to attract new, high-income and high-net-value customers, to cater to the individual needs of customers, to offer them precisely tailored products and services, to identify customers likely to close their accounts and to dissuade them from doing so. 07/28/99, 5:18 Uhr Ossen.doc/jobnummer Seite 1 von 7
In particular, the new Private Banking concept targets some 300,000 high-net-value customers with accounts at Deutsche Bank. Responsibility for optimizing and co-ordinating marketing activities in this area lies with the Marketing Department of the Private Banking Division. This department operates its own competence center for data mining and marketing analysis. The center not only maintains its own data and analysis tools, but also has access to all relevant information in the central Deutsche Bank IT environment. In particular, it has access to operational and transactional data, which it can then combine with its own marketingdriven planning and decision-making data. The central IT environment is very different to the system operated by the Marketing Department in terms of both structure and platform. It is based on IBM mainframes, UNIX servers and OS/2 PCs, with many terabytes of data stored in DB2 and Oracle databases and in a SAS data warehouse. The Marketing Department employs scalable UNIX servers and runs Sun Solaris on a Windows NT client/server architecture. The Marketing Department, too, employs an Oracle database, together with a number of SAS data marts. The volume of data employed by the Marketing Department totals some 500 gigabytes. In the past, the department was forced to seek the support of database specialists in order to acquire the information needed on target groups for customer mailings for new products. These specialists then employed mainframe emulation and mainframe SAS techniques to access the central data warehouse. Selection of target customer data and the design of marketing campaigns therefore entailed time-consuming ad-hoc programming. This tied down vital human and technical resources. What s more, marketing analysts were unable to formulate their queries themselves and were unable to access and manipulate data without expert assistance. For practically each and every query, data had to be extracted from DB2 tables and from the central data warehouse using custom-developed programs written by Deutsche Bank s IT specialists. The need to channel queries through programmers in this way meant a loss of valuable time. In many cases, it also failed to deliver the right results due to frequent misunderstandings and communication shortfalls between IT specialists and marketing staff. 07/28/99, 5:18 Uhr Ossen.doc/jobnummer Seite 2 von 7
As a result, the data extracted and aggregated was often not geared to the actual needs of users, and marketing staff were unable to make full use of the information potentially available to them. The highly complex calculations were performed on a mainframe, and despite the scale of the resources used the results were often imprecise. All in all, the information process chain was too long, to inefficient and had far too many links. There are several reasons why this process does not always deliver the right results: the IT specialists in the Database Marketing Group may, theoretically at least, have access to all relevant data in the mainframe-based IT environment and the Marketing Department, but the data landscape is very heterogeneous in character. It comprises databases of varying structure, plus a data warehouse and multiple data marts. This is not the ideal environment for ensuring data consistency. Target group segmentation was made in accordance with criteria such as age or assets, and was primarily based on descriptive statistics. Purely data-driven information and insight played no part in the segmentation process. In other words, it was not possible to develop hypotheses or analysis models in order to test rules of customer behavior or to confirm their validity in order to perform task-oriented segmentation for example, to determine the conversion rate for a given product. The Database Marketing Group also lacked sophisticated statistics tools of the type needed for forecast modeling, hypothesis development and testing. Precise customer scoring and the calculation of cross-selling potential were also not feasible. A further problem was the inability to access information on customer contacts at individual branches and to data relating to customer responses to specific marketing campaigns information which could have been used to systematically refine target group selection. In conclusion, the Marketing Department needed an improved, more consistent pool of data and more powerful statistical analysis tools. Against this background, the Database Marketing Group and the IT department began looking around for a suitable data mining solution. The aim of the project, which commenced in August 1998, was to improve the bank s knowledge of both existing customers and prospects, and to raise efficiency and the return on investment within marketing as a whole through better response rates and more precise target group selection. The Project 07/28/99, 5:18 Uhr Ossen.doc/jobnummer Seite 3 von 7
The proposed data mining system needed to provide the basis for an analytical approach which would support quantitative analysis, hypothesis testing and model development, for instance for forecast and scoring. In particular, the Marketing Department wanted to access to precise information for cross-selling and up-selling, for segmentation of existing customers and prospects, for sales and ROI forecasts, and for the simulation of marketing campaigns. The data mining solution had to support a variety of statistical methods: classic statistics (descriptive and inference, for instance for testing hypotheses), cluster analysis, state-of-the-art statistics, decision-trees and non-linear methods such as neural networks. In addition, interfaces to OLAP and other reporting tools were required. The data mining project is just part of a more wide-reaching private banking project known as INCCOM (Integrated Customer Communications Management); There are five sub-projects in all: data warehousing, data mobilization, campaign management, data mining and OLAP/reporting. The objective is to create a fully integrated, end-toend marketing process from the formulation of the business objective, to the development of tailored marketing campaigns and the definition of individual tasks within those campaigns, to analysis of the results and of return on investment. Decisionmakers need to be aware of how much is invested in marketing, the impact it has, what products are sold and at what cost. It is also a matter of integrating all marketing channels, such as the private banking centers, the Internet, call centers and direct mail organizations, and of leveraging existing sales support systems in the area of marketing. The INCCOM project was developed and planned with the involvement of all relevant departments: database marketing, Deutsche Bank IT, product management, direct sales and the sales force. Subsidiaries with their own marketing activities were also given the opportunity to contribute to project preparations. An advertising agency, a management consultancy and a systems house provided additional external expertise. But back to the data mining project: Deutsche Bank carefully considered eleven data mining product vendors. They shortlisted IBM Intelligent Miner, SPSS and SAS Enterprise Miner. These three were invited to make presentations and to demonstrate their products benefits by installing them on a test basis. The SAS Institute solution came out on top, primarily because it offered better integration of various statistical techniques and supported a broader variety of such techniques. 07/28/99, 5:18 Uhr Ossen.doc/jobnummer Seite 4 von 7
Cooperation between Deutsche Bank and SAS Institute was also well established, both for mainframe and desktop solutions. The key argument in favor of the SAS solution was, however, the existence of SAS skills at Deutsche Bank in a variety of areas, in particular for database marketing, data warehousing and for extracting and staging data from operational systems. With the exception of a brief introduction to the use of Enterprise Miner, there was practically no need for training. One of the earliest and most important steps was to consolidate the existing data landscape. In particular, there was a need to check the quality and consistency of data and to ensure that operational data was aggregated in alignment with marketing objectives and tasks. The data in the mainframe environment had to be translated into meaningful information of a kind that could be employed by marketing personal: marketing experts do not wish to do battle with cryptic code, they need user-friendly product names such as SparCard or TopInvest together with statistics they can understand at a glance - and not just row upon row of figures and symbols. To provide consistent, accurate and up-to-date facts and figures of a kind that could easily be understood, a number of fields had to be redefined and restructured. The tasks of consolidating existing data sources, and ensuring the quality and coherence of data, proved to be highly complex and time-consuming. After all, data had to be extracted from three different sources, from financial controlling, from the existing data warehouse and from operational systems, and then combined before data mining and analysis were feasible. The initial tests and checks performed by SAS Institute revealed a variety of inconsistencies. Nevertheless, this work did not hold up project progress. A further important task was the integration of the data mining solution into the existing IT structure of the Marketing Department in particular and of Deutsche Bank in general: the solution needed to access operational systems, the existing data warehouse and planning and decision-making data used by the Marketing Department. Marketing database and analysis tools, including data mining tools, are resident on the Marketing Department s IT system, while operational data and the data warehouse form part of Deutsche Bank s central IT environment; information from sales channels also had to pass through the bank s central IT structure, calling for links to be created to OPUS/NOS, i.e. the internal communications platform for data exchange with branch offices. 07/28/99, 5:18 Uhr Ossen.doc/jobnummer Seite 5 von 7
During the course of the project, it was decided in many cases to access operational systems directly rather than drawing on the data warehouse. This approach guaranteed that data was of greater accuracy and quality, and more up to date. Combining the various information needs of marketing staff with the structure of and IT support for work processes in branch offices and the establishment of a solution for customer contact management also proved to be more difficult than expected. The aim was for employees at the bank s branches to enter data directly into the marketing database by means of Deutsche Bank's intranet, raising the usefulness of the intranet to an entirely new level. The data mining project kicked off with the development of two concrete models; one for customer retention and one for cross-selling - this latter model being ad-hoc in nature: the prototype was completed in March 1999. At the present early stage, these two models are operating with customer master data and product data only. Customer contact information will be incorporated by means of a pilot system that is expected to go live in June 1999 and which will, to a limited degree, be used for ongoing marketing operations. The fully-fledged production system is expected to go live in September 1999. The customer retention model is designed to identify customers likely to switch to another bank and to dissuade them from doing so. Analysts wish to gain answers to questions such as What is the probability of us retaining this customer?, What is the probability of him switching to a competitor bank?, What are the indicators underlying the trend towards changing to a competitor?. The analysts wish to identify at risk customer segments and their specific characteristics. This is followed by the development of tailored marketing campaigns and products. For instance, analysts have been able to establish five key characteristics indicative of a customer likely to switch to another bank as a result, they are now able to spot these customers much more precisely and, above all, at a far earlier stage, and to take appropriate counter-action. Cross-selling models are designed to reveal affinity between various products: if a customer has already made use of products A and B, what is the probability of an interest in product C? Or, conversely, customers who have already purchased product F are practically never interested in product C. 07/28/99, 5:18 Uhr Ossen.doc/jobnummer Seite 6 von 7
These models support highly precise segmentation of customer types, allowing them to be addressed more effectively and therefore offering a better return on marketing investment. Further models are already planed for identifying potential customers and winning them over to Deutsche Bank. The key benefit of the new data mining solution for the Database Marketing Group is the wide variety of ways in which it can put the various models to the test faster and with far less programming than was the case to date. The time and resources that had been previously expended on programming can now be used to develop new models. The simplification of modeling has, for instance, allowed the bank to validate the entire area of customer retention scoring, leading to entirely new insight into the model and the importance of the various parameters employed. The system offers IT specialists in the Marketing Department far greater flexibility, as they can test and simulate a variety of alternatives and options. The marketing staff also profit from improved customer segmentation and more precise information on the probability of certain types of customer behavior, and the correlation between one type of behavior and another. Last but not least, the customer himself stands to benefit: he will be provided with precisely the information he needs and not barraged with marketing materials of no relevance to his financial situation or goals. * The author holds a doctorate in Information Technology and is a specialist for neural networks and database marketing. He was an assistant professor at the Technical University of Berlin before joining Deutsche Bank in 1998 where he works on projects in the area of investment banking. 07/28/99, 5:18 Uhr Ossen.doc/jobnummer Seite 7 von 7