Download A data model for database marketing activity

Data Model for Database Marketing Activity
Alberto Saccardi, Frank Fiocca - NUNATAC
Abstract
What kind of data do we have collect to optimise C.R.M.? Which data do we need to understand the
market and our competitors? Which logical subjects should we consider in a marketing database?
How detailed and, how summarised should our data be? How should it be organised?
These are a few questions that have to be dealt with to design a marketing database for decision
support. We’ll illustrate the basic guidelines to define the correct data model for a powerful database
marketing activity. In particular we’ll present some cases we have encountered and tried to resolve.
Introduction.
Nunatac is a consulting firm, which is made up
of specialists in statistics, marketing and
information technology. Its current business
solutions fall into the following categories:
data warehousing, data mining, business
reporting and campaign management. In
particular we are experts in database marketing
activities, comprising data warehousing and
data mining to do business.
Nunatac focuses on supplying tailor made
business solutions for its clients, using the SAS
SYSTEM and is certified as a SAS Quality
Partner.
The most recent projects are in the publishing,
manufacturing, banking and insurance sectors.
In the first part of this paper we’ll introduce
the business problem and what’s necessary for
a database marketing activity. In the second
part we will discuss the main points which
characterise a marketing database model. At
the end some concluding remarks.
1. Database Marketing Activity.
Some of the most important business questions
a marketing manager must answer are:
· Who are our customers?
· What are they like?
· How many behavioural groups are there
among them?
· Which are the most profitable?
· How can we maximise customer lifetime
value?
Answering these questions in various projects
has allowed Nunatac to refine its approach to
Database Marketing Activity (DBMA).
The DBMA puts the customer at the centre of
interest and the approach may be summarised
in the following steps.
Business question;
Defines the goal of the analysis.
Data Organisation;
Once the business question has been defined,
the second step is to collect and to arrange the
data needed to solve the problem. In this phase
it’s necessary to:
· define the logical units
· census the data present in the Information
System
· Transform and aggregate data in to data
marts for analysis.
Data Analysis;
The third step is the analysis of the data using,
for example, data mining techniques to find
similar groups of customers considering their
behaviour not related to a specific
phenomenon or to identify the most important
attributes
which
explain
a
specific
phenomenon: e.g. a policy subscription.
Test & Implementation;
The fourth step is to test the models, which
have been estimated, and to implement the rule
on the marketing database.
Results;
The fifth and last step is to measure the results
that are obtained using this rule. When
possible, we should compare these results with
the results obtained from a control sample
selected using traditional criteria.
What do we need to implement this approach?
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First; an organised work group with diverse
expertise:
Marketing,
Statistics,
and
Information Technology.
Second; data access and management
technology.
Third; a data model by which it is possible to
organise and to prepare data for data analysis
activities: reporting, OLAP and data mining.
2. Marketing Database Model.
In this paragraph we’ll list the main elements
involved in the construction of the data model.
Logical Subject definition;
Generally speaking the logical subjects that
characterise a marketing database are:
· Client
· Product
· Sales Force
· Territory
Internal and External data;
Once the logical subjects have been identified
it is possible to associate:
· Information gathered from operational
data sources; invoices, payments, claims,
etc. that are related to each of the logical
subjects.
· Relevant
external
information;
differentiated by the level of detail and
quality.
Physical organisation of data;
The internal and external sources supply the
environment prepared for the DBMA. This
environment is composed of fact tables
(detailed and certified data) and subject tables
(summarised data related to the logical
subjects, e.g. the customer).
The main characteristic of the fact tables is
that they contain data at the maximum detail:
at least one record per variation of the fact
considered. In this phase data are not
summarised and they are most of all used for
extraction or for reporting activities: standard
tabulate, OLAP and so on.
The main characteristic of the subject tables is
that they contain summarised data regarding:
· The
analysis
variables,
in
data
warehousing terminology the facts, e.g.
the number of products purchased;
· The
classification
variables,
the
dimensions, e.g. product type;
· The interaction between facts and
dimensions;
· The fixed time lag for summarising the
data.
The data at this level are organised for data
mining activity, where e.g. the customer is the
logical dimension of interest and the data are
prepared in data marts where one customer
corresponds to one record.
This is the standard checklist that should be
adhered-to to build a marketing database, but
what does all this mean? Our presentation will
attempt to highlight how we have put this
theory into practice.
Alberto Saccardi - Frank Fiocca
NUNATAC
Via Crocefisso 5,
20122 Milano
Italy
Tel.
+39 02 86996848
Fax.
+39 02 89012074
E-mail:
[email protected]
[email protected]
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