Download Database marketing simplified through data mining

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.
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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.
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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
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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.
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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.
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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.
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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.
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