Download SAS Enterprise Miner templates for database marketing applications

Enterprise Miner® templates for database marketing applications
Authors:
Federico Ambrogi, Guido Cuzzocrea, Massimo Saputo
-
NUNATAC
Abstract
Treasure island, an enormous amount of data at your disposal and the best software environment to exploit it. Data
Mining is an opportunity to gain a competitive advantage and should supply the marketing decision-makers with
operative tools for the evaluation of client potential, the planning of an integrated offer and the selection of campaign
targets.
Nevertheless data and technology are necessary but not sufficient. To successfully apply Data Mining solutions to
Database Marketing activity you need to perform a process flow that starts from goal identification and then passes
through making data available, and applying the SEMMA methodology. But this is still not enough: you need to
implement the campaign in production, considering not only the new targeting rules, but traditional criteria, crosssections and control tests.
All this comparing budgets with expected break-even per segment and averaging market penetration goals with profit
maximisation ones.
Do you feel like Indiana Jones? What if you had a detailed treasure map to get you out of the jungle alive and smiling?
Introduction
Database Marketing means searching for
information wealth within the appropriately
organised data sources of companies, and
devising efficient and measurable rules to
enable you to make the best marketing
decisions.
In other words, Database Marketing means
moving the data analysis procedure to a
context which is centred on business
problems and which is directed towards
achieving competitive commercial strategies.
An important characteristic of Database
Marketing is the role played within the
company, which is now centred on the new
listen-and-answer approach rather than the
produce-and-sell one, in relation to their
clients and the market.
This role has the following prerequisites:
• the will of management to adopt this
approach and the necessary accompanying
cultural and organisational shift of the
company;
• the availability of a dedicated data structure
(a Marketing oriented Data Warehouse);
• the possibility of operating in an analytical
environment which favours the production of
decision-making rules on an industrial level
(a Data Mining environment).
In this culturally and technologically
innovative context, what was once the
estimate of a Scoring Model, now becomes
an opportunity to implement decision-making
processes for the optimisation not of a
campaign, but the ten or hundred campaigns
expected to come out of an ambitious
marketing operation.
1. Who we are
NUNATAC was formed at the beginning of
1994 in response to a specific need in the
market. A critical success factor in numerous
business sectors now means having an
efficient marketing strategy with a
particularly strong client focus: the ability to
develop a close relationship with your clients,
based on reciprocal knowledge and the
development
of
personalised
products/services, is an essential condition for
commercial development and consolidation.
The defining characteristic of NUNATAC is
the combination of specific abilities and a
well developed know-how in the field of
Database Marketing. Our approach to project
responsibility is centred on proposing
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solutions to needs identified by consultation
with management, systems development and
training of our clients in the methods we have
utilised.
NUNATAC is composed of a group of
professional consultants with statistical,
computing and marketing skills: since 1997,
NUNATAC is a European Quality Partner of
the SAS Institute.
2. Database Marketing Activity
2.1 Goals of the project
What are the guidelines that permit you to
usefully perform Database Marketing?
What are the necessary abilities required to
extract information wealth from the vast and
complex company databases?
To what degree can a sound methodology,
supported by an appropriate “tool box”
operating in a dedicated and easily accessible
data structure, give us consistent economic
advantages?
Finally, if the objective is to derive decisionmaking processes on an industrial level, is it
then possible to translate the successive
phases of this process into logical entities,
which are clearly identifiable and repeatable?
Nunatac’s opinion is that not only is this
approach possible, but it’s desired. Our
current activity of Database Marketing
consultants and Quality Partners of the SAS
Institute, is centred on the production of
“templates” for the realisation of Behavioural
Segmentation projects and the construction of
a Scoring System, in the Enterprise Miner™
environment.
Enterprise Miner templates
for Database Marketing applications
TM
2.2. Database Marketing cycle
Database Marketing activities are centred
around a purpose built database and follow a
precise, logical sequence of actions.
Database Marketing
Business
question
Analysis
goals
Evaluation
of results
Marketing
Database
Test and
Implementation
Data selection
and organisation
Analysis
In relation to this sequence, the Database
Marketing operation begins with the
identification of the business issue to which
an appropriate solution is required.
The business objective must therefore be
translated into analytical terms and into the
availability of the data needed to provide a
non-trivial answer to the problem.
When we possess a significant and
appropriately organised information base, we
then proceed to the actual mining phase.
The results of the analysis, after having been
validated, must subsequently be implemented
within the company’s productive process and
must be compared with traditional decisionmaking criteria.
The final and decisive phase involves the
measurement of the results obtained on the
market following the action just undertaken:
it is only through this procedure that we can
capitalise on the experience and feed the
information wealth of the database.
An example of the productive cycle
associated with the Database Marketing
operation is the construction of a Scoring
System to optimise the results of a campaign.
In this case, the objective is strictly geared
towards a specific marketing operation and
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the result we wish to obtain is redemption
maximisation rather than that of profit or
penetration of the campaign.
In the case of Behavioural Segmentation, the
desired business objective is definitely more
ambitious: we don’t limit ourselves to
wanting to optimise results associated with a
specific marketing action, but rather to
provide the elements for a marketing strategy
and commercial planning.
The fundamental aspect we want to underline
here, is the perfect matching, in relation to the
Database Marketing cycle discussed above, in
terms of the logical sequence of the actions to
be carried out.
In the context of the activities that marketing
must carry out and of the solutions that it
should provide to the company, Behavioural
Segmentation pursues strategic objectives. To
give a few significant examples, you might
consider: the planning of differentiated
marketing policies in relation to the identified
targets, the budget assignment to the sale
network in relation to cross-selling potentials,
the delimitation of the prospect universe to
which apply a Scoring System for a specific
campaign.
In this sense, the objective of constructing a
Scoring System is to support tactical
decisions, within a particular strategic plan.
From a conceptual point of view, a Scoring
System is the fine tuning in the search of a
radio
frequency
achieved
through
Segmentation.
If, however, we have to embark on the
Database Marketing process within a
company used to working traditionally, the
Behavioural Segmentation might be a too
expensive and resource intensive project to
start with. In this case, the construction of a
Scoring System becomes a project with
which we can more rapidly measure the
comparative advantage of the new approach.
2.3 Database marketing and data Mining:
what is necessary?
The prerequisites to carry out an efficient
Database Marketing activity are:
• Availability - or training - of dedicated and
competent human resources
• Technology to access and manage the data
sources
• Dedicated data structures and an analysis
environment
• An analytical methodology
• Implementation of processes
The following diagram depicts the logic
architecture
of
the
dedicated
data
environment, of the analytical processes to
implement and the operative aspects to face,
in order to carry out the Database Marketing
activity.
Logical structure
Internal
data
Ambiente DW
External
data
Personal data
manager
Customer Analysis
Data Mart
Segmentation
Scoring System
Product Analysis
Data Mart
Market Analysis
Data Mart
Basket Analysis
P.O.S. potential evaluation
Data Mining Processes
Commercial Proposal:
target selection and product mix.
R.O.I.
Campaign
manager
In a departmental Data Warehousing view,
the dedicated data structure provides for the
loading and the integration of internal and
external data, in addition to the traditional
decision-making rules that constitute the
business knowledge resources of the
company.
The Marketing Data Warehouse, or the
Marketing Database, must contain data at
various levels of detail in order to satisfy the
different analytical needs (OLAP and Data
Mining) of different reference units for the
business (Client, Products, Points of Sale).
The Analysis Data Marts, organised on the
basis of the desired objectives that need to be
followed up by the actual analysis, depend on
this structure.
The Enterprise Miner tool has to access to
these Data Marts, and the deliverables of the
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Data Mining step include the significant
reports necessary to make the best decisions
and the coded rules for the target selection.
2.4. Data Mining Process flow chart
Going back to the hypothesis of translating
into the Enterprise Miner repeatable process
flows for specific Database Marketing
projects, the general reference plan is the
Data Mining process subdivided into phases,
one of which is devoted to the analytical task
using the SEMMA methodology (Sample,
Explore, Modify, Model, Assess).
Data Mining process and
SEMMA methodology
Define business
problem
Evaluate
environment
Make Data
available
Mine data in cycles
Review
Explore
Sample
Implement in
production
Assess
3.1 The Behavioural Segmentation Project
A Behavioural Segmentation project involves
the following phases:
• Preparation of the elementary database for
the segmentation, considering an industry
oriented data model (e.g.: banking, insurance,
retail).
• The selection of a significant sample on
which to carry out the analysis and validate
the classification criteria.
• The analysis process to identify the
homogenous segments and, on this basis,
estimate a general classification rule to be
applied to all the clients.
• Finally, the estimated rule must be
implemented in a computing process. Such a
rule allows us to transform the chosen and
interpreted segments in a marketing vein, in a
classification
that
is
homogeneously
applicable for new clients and those acquired
in future.
Modify
Model
Developing analysis in the Enterprise Miner
environment means plotting a process flow
for the realisation of the successive phases of
a Data Mining project.
To render the Enterprise Miner Templates
usable means creating macro flows that act as
guidelines for the realisation of specific
Database Marketing projects: in other words,
the treasure map!
3. Enterprise Miner Templates
On the basis of previous logic plans, the idea
under discussion is that of providing support
for the carrying out of Database Marketing
projects on an industrial scale. Nunatac, in
accordance with the SAS Institute, has
produced, in the Enterprise Miner
environment, the first templates for
Behavioural
Segmentation
and
the
construction of a Scoring System.
3.2 The Design of a Scoring System
Even the construction of a Scoring System,
albeit with different applications and a greater
emphasis on the estimate and validation of a
model for the assignment of a score, can be
easily identified by clearly defined logical
phases.
3.2.1 Scoring System Template
Let’s look at one of our first template ideas:
an Enterprise Miner flow-chart for the
development of a Scoring System.
The template has been organised trying to
highlight the different SEMMA phases and
several points of the general data mining
methodology (Fig. 1).
Fig. 1: Scoring Template
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Coming back to the different steps of a Data
Mining project, an Enterprise Miner
workspace can incorporate what is shown in
grey in Fig.2.
It is possible to highlight the relationship
between each phase of the Methodology and
the template:
1. The business problem definition can have
an impact on the identification of the
target variable, but it is not the phase in
which the computation is effectively done
(this is more in the modify step).
2. The environment evaluation step is not
included, but might be simplified by the
use of the template: In fact the structure
will give a clear understanding of what
the project needs in order to be
implemented and which kind of data is
appropriate.
Fig. 2: Data Mining Methodology and Template
M ake Data Available - Step 1
Define business
problem
Evaluate
environment
M ake Data Available - Step 2
Review
M ine data in cycles
Explore
Implement in Production Step 2
Sample
Implement in Production - Step 1
3. Making data available requires a massive
use of the data step, especially when a
data warehouse does not exist. Generally
this activity is done outside of the
Enterprise Miner. Once the data is in
SAS format and a structure is created for
analysis purposes (one record per
statistical unit) it is possible to start
working with the E/M Template. At this
point the analyst must define the variables
to use, create some useful new variables
or transform the existing ones, evaluate
the probability distributions, and clean
missing values.
4. The mining in cycle step can be split into
two moments. The first concentrates on
sampling and exploring the data, the
M odify
Assess
M odel
second on building and assessing the
model.
5. The template’s last sub-diagram applies
the previously generated scoring code to a
prospect file, reports on, and explores the
results.
The operative step will be demanded to the
ERP software, and the review step will be
carried out when the results for evaluation are
available.
Conclusions
The use of data Mining techniques is difficult
for non-statistical experts.
Generally
analysing business data is not easy even for
an expert in statistics without a specific
experience.
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The SEMMA Methodology is a general
guideline for solving a Data Mining problem.
More, the data mining methodology is a
general help in setting up a complete project,
from the specification of the business
problem to the implementation in production.
The great need of standardisation, and the
integration of data mining processes in the
production cycles requires to have something
like a "data mining product" to have this
solution more easily accepted in the company
environment.
Furthermore to demonstrate that something
works is of vital importance for the diffusion
of data mining techniques.
From this the idea to analyse some real world
projects and to try to put them in an
Enterprise Miner template. The goal is to
build something in the middle between the
Data Mining and SEMMA Methodology on
one side and the real application on the other.
A template is not a ready-to-use solution for a
particular business problem. A template is a
more specific solution to a business problem
than SEMMA is.
Summarising, a template is the specification
of the Data Mining and SEMMA
Methodology for a typical business problem.*
*
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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