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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