Download Enhance SAP step-by-step with Customer Relationship Management Functionality

Enhance SAP step-by-step with
Customer Relationship Management
Functionality
Patrick Schünemann, Predict AG
[email protected]
Starting Point: Big Investments in
ERP Systems
• Almost all companies invest in the automation of their
business processes
• Standard software helps in automating standard
processes in production, supply-chain, sales-force,
financial accounting etc.
• Cost saving is central
• Very significant investments with heavy influences on
business processes
Data processing is not equal to
information processing
• Data that had been processed on paper can now be
handled 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 data
processing
• Data management is optimized for speed and
consistency
→ not easy to understand
• Process workflow is optimized
→ no instruments for analysis
• Modeling is not possible
Corporate systems architecture
Management
Management
Decision
Decision
Support
Support
Systems
Systems
Data
Data
Mining
Mining
Knowledge
Knowledge
-Base
Base
Customers
Customers
Distribution
Distribution
Channels
Channels
e.g.
e.g. Branch,
Branch,
Web,
Call
Web, Call Center
Center
Execution
Execution
Legacy
Legacy Systems,
Systems, ERP
ERP Systems
Systems
Rules,
Rules,
Channel
Channel
Integration
Integration
To understand your business you
need various methods to generate
information and knowledge
Data Querying (1980)
Data Navigation (1990)
Data Mining (2000)
„Which are the 10’000
contracts with
the highest turnover?“
„Which are the top 5 % contracts
in terms of turnover in
eachbranch of the last 5 years?“
„Which customers will be the
top 5 % in terms of profitability
in the next year and why?“
deterministic, deductive,
one solution, retrospective,
one dimensional,
no model
deterministic, deductive,
one solution, retrospective,
several dimensions,
no model
probabilistic, inductive,
many solutions, Prediction,
multidimensional,
Computer Model necessary
relational database,
SQL,
statistical reporting,
list output
data warehouse,
OLAP,
dynamic reporting,
MIS / EIS
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
More
customers
•
Which customers?
•
Which product?
•
Who is profitable?
More revenue per
customer
•
•
•
Who will buy other
products?
Who buys more
often?
Who buys topproducts?
Retain
customers
longer
•
•
•
Who will leave?
When will he
leave?
Whom should
we keep?
Knowledge is basis for successful
CRM
•
Which customers?
•
Which product?
•
Who is profitable?
•
•
•
Who will buy other
products?
Who buys more
often?
Who buys topproducts?
•
•
•
Who will leave?
When will he
leave?
Whom should
we keep?
• 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)
Implementation driven by business
imperatives
Business imperatives require
Strategy drives
Tactics supported by
Decisions about projects, schedules, resources, requirements,
tools and data
The primary goal is not having a
CRM system
• Development of existing customer portfolio by cross
selling
• Customer selection for direct mail campaign to promote
citybikes (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 stepby-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, Navision
etc.)
Evolutionary prototyping assures
relevance 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 less
complex, more open and flexible
Any table in
SAP system
ODS for
SAP data
ODS for
external
data
Analytical dataset with
one record per customer
SAS/Warehouse Administrator™
SAP processes
Data mining environment
Enterprise Miner ™
New Table in SAP
Table with
scored
customers
External
tables
External
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 – find
relevant 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 of
customers who bought
a citybike last year
3. Enrich customer
data with analytic
variables (100 –
500 variables)
2. Sample of
customers who bought
no citybike last year
Data Preparation
4. Find variables
that discriminate
best between
buyers and
non-buyers
Explorative Data
Analysis
5. Buid the model:
calculate likelihood
to buy for all
customersin
analytical data set
Modeling and
Scoring
Model
Modelselection
selectionhelps
helpsto
to
identify
identifycustomers
customerswho
whowill
willbuy
buy
Results
• Prediction: 400 customers will buy a citybike
• Actual purchases: 360 customers
• Correctly classified: 330 customers
• Get tangible and intangible results
Volume
Cost (CHF 50 / Customer)
Purchase Rate
Purchases
Profit (CHF 310 / Customer)
ROI
Traditional Selection
1’200
CHF 60’000.30 %
360
CHF 111'600.CHF 51'600.-
Model Selection
400
CHF 20'000.82.5 %
330
CHF 102'300.CHF 82’300.-
Learnings from the project
• SAP systems are operative systems
• Collaboration with SAP specialists is simplyfied by
clear interfaces
• Next steps are simplified because of experience
• It is possible to build a reasonable CRM
environment with less than 3 people in less than 3
months and have first tangible results