Enhance SAP step-by-step withCustomer Relationship ManagementFunctionality

Patrick Schünemann, Predict AGpatrick.schuenemann@predict.ch

Starting Point: Big Investments inERP Systems• Almost all companies invest in the automation of their

business processes

• Standard software helps in automating standardprocesses in production, supply-chain, sales-force,financial accounting etc.

• Cost saving is central

• Very significant investments with heavy influences onbusiness processes

Data processing is not equal toinformation processing

• Data that had been processed on paper can now behandled electronically quicker and in huge amounts → cost savings

• To make information out of data we need• Metadata• Analytical instruments• Models

• Information brings cost savings and revenue

ERP systems are good in dataprocessing

• Data management is optimized for speed andconsistency→ not easy to understand

• Process workflow is optimized→ no instruments for analysis

• Modeling is not possible

Corporate systems architecture

Legacy Systems, ERP SystemsLegacy Systems, ERP Systems

Rules,Channel

Integration

Rules,Channel

Integration

DistributionChannels

e.g. Branch,Web, Call Center

DistributionChannels

e.g. Branch,Web, Call Center

CustomersCustomers

DataMiningDataMining

Knowledge-

Base

Knowledge-

BaseExecutionExecution

DecisionSupportSystems

DecisionSupportSystems

ManagementManagement

To understand your business youneed various methods to generateinformation and knowledge

Data Querying (1980)

„Which are the 10’000 contracts with

the highest turnover?“

deterministic, deductive,one solution, retrospective,

one dimensional,no model

relational database,SQL,

statistical reporting,list output

Data Navigation (1990)

„Which are the top 5 % contracts in terms of turnover in

eachbranch of the last 5 years?“

deterministic, deductive,one solution, retrospective,

several dimensions,no model

data warehouse,OLAP,

dynamic reporting,MIS / EIS

Data Mining (2000)

„Which customers will be the top 5 % in terms of profitability

in the next year and why?“

probabilistic, inductive,many solutions, Prediction,

multidimensional,Computer Model necessary

statistics,artificial intelligence,classifier, generalizer

scoring (with model-function)

The bicycle retailer

• SAP test system

• 10 branches in Switzerland

• „Happy Biker“ customer card

• 6 product groups

• 1‘200 customers with a card

Three ways to increase profitability

Morecustomers

More revenue percustomer

Retaincustomers

longer

• Who will leave?

• When will heleave?

• Whom shouldwe keep?

• Which customers?

• Which product?

• Who is profitable?

• Who will buy otherproducts?

• Who buys moreoften?

• Who buys top-products?

Knowledge is basis for successfulCRM

• To answer these questions, you have to make predictions• Predictions are better, when they are based on relevant

experience• Models are mathematical expressions of past experience

! e.g.: older people buy more (likelihood to buy = 1.353 × age)

• Which customers?

• Which product?

• Who is profitable?

• Who will leave?

• When will heleave?

• Whom shouldwe keep?

• Who will buy otherproducts?

• Who buys moreoften?

• Who buys top-products?

Implementation driven by businessimperatives

Business imperatives require

Strategy drives

Tactics supported by

Decisions about projects, schedules, resources, requirements,tools and data

The primary goal is not having aCRM system

• Development of existing customer portfolio by crossselling

• Customer selection for direct mail campaign to promotecitybikes (product group 3) because of high margin

• Data mining is essential for target group selection

! This is not CRM! This is not ERP! This is not Warehousing

The solution – build the system step-by-step

• Driven by business needs

• User learns with the project – learning organization –results and facts

• Small project team (2 analysts, 1 SAP specialist)

• Generic approach (SAP, Baan, Peoplesoft, Navisionetc.)

Evolutionary prototyping assuresrelevance of investments• Strategy

• Tactics

• Proof-of-Concept• Identify data• Get data and massage it• Analyze and model• Execute with a measurable result

• Next prototype

System with clear interfaces is lesscomplex, more open and flexible

Analytical dataset withone record per customer

SAS/Warehouse Administrator™

Data mining environmentEnterprise Miner ™

Table withscored

customers

New Table in SAP Externaltables

Externaldata

SAP processes

Any table inSAP system

ODS forSAP data

ODS forexternal

data

The right CRM system• 1 record per customer – build a customer view

• Data Rollup from 54‘000 (!) tables and 850‘500 (!!!)fields with 200 Mbyte metadata – manage metadata

• Flat data model similar to the business model – findrelevant data

• Data cleaning, data quality assessment

• Feed selection to call center and laser-letter-shop –campaign management

• Repeat loading one months after campaign –measure

Building the predictive model

1. Sample ofcustomers who boughta citybike last year

2. Sample ofcustomers who boughtno citybike last year

3. Enrich customer data with analytic variables (100 – 500 variables)

4. Find variables that discriminate best between buyers and non-buyers

5. Buid the model: calculate likelihood to buy for all customersin analytical data set

Data Preparation Explorative DataAnalysis

Modeling andScoring

Model selection helps toidentify customers who will buy

Model selection helps toidentify customers who will buy

Results• Prediction: 400 customers will buy a citybike• Actual purchases: 360 customers• Correctly classified: 330 customers

• Get tangible and intangible results

Traditional Selection Model Selection Volume 1’200 400 Cost (CHF 50 / Customer) CHF 60’000.- CHF 20'000.- Purchase Rate 30 % 82.5 % Purchases 360 330 Profit (CHF 310 / Customer) CHF 111'600.- CHF 102'300.- ROI CHF 51'600.- CHF 82’300.-

Learnings from the project

• SAP systems are operative systems

• Collaboration with SAP specialists is simplyfied byclear interfaces

• Next steps are simplified because of experience

• It is possible to build a reasonable CRMenvironment with less than 3 people in less than 3months and have first tangible results