The use of machine learning techniques allowed IBM to provide its sales force with market intelligence that identified sales prospects.

Transcription

1 Robert Palinic 1

2 Executive Summary The use of machine learning techniques allowed IBM to provide its sales force with market intelligence that identified sales prospects. A tool was provided to sales personnel which could answer the question: How much product could be sold if a sale is made? Increased sales force productivity amounted to savings of over $10 million and attributed sales of $100 million. 2

3 Presentation Overview Background Project Goal Key Issues Data Sources Components of OR Solutions Predictive Relationships Conceptual Framework Data Mining Techniques Deployment Tool Evaluation of Success Positive Points Criticisms 3

4 Background Marketing has traditionally been a qualitative, soft discipline Mass Marketing versus Direct Marketing Half my marketing budget is wasted. If only I knew which half.

5 Background Mass marketing depends upon a small group of positive respondents to be profitable Many large marketing campaigns are not undertaken due to their negative profitability

6 Background Direct marketing aims to focus marketing efforts against a smaller subset of customers which has a higher response rate The challenge is identifying this subset population

7 Example: Mass Marketing Mass marketing campaign to 100,000 people at $1 per person, for a cost of $100,000 Each potential sale has a profit of $10 With a response rate of 4%, 4,000 customers respond. At $10 profit on each sale, the total profit is $40,000 The total campaign has therefore lost $60,000

8 Example: Direct Marketing Using data mining techniques, a subset population has been identified with a 11% response rate. This subset contains 10,000 people. The cost to reach this group is $10,000 With 1,100 respondents at a profit of $10 each, for a total profit of $11,000 The overall campaign is profitable by $1,000

9 Background of IBM Requirements At the end of 2007, IBM had approximately 40,000 employees in sales-related roles Several families of products sold 650,000 potential customers 9

10 Project Goal To build a system that computes opportunity for sales to a customer. 10

11 Market Space Current Customers (37,433) Whitespace Potential Customers (612,677) 11

12 Key Issues Predictions for targets that cannot be verified Explanatory capabilities Capability to incorporate external knowledge Parallel computation of multiple targets and other efficiencies Capability to calibrate optimism in the predictions Method stability and ease of maintenance for incorporating new examples 12

13 Components of Solution Quantifying Business Impact Solution Delivery OR Models Data Models Transactional History Other Internal Data External Firmographics 13

14 Data Sources (a) IBM Sales Database (internal data) Dun and Bradstreet (external data) 14

15 Data Sources (b) Total inputs 16 Numericals 14 Codes 2 Number of code 3 and 26 values Outputs 6 Total Records 650,110 Non-Customers 612,677 Customers 37,433 15

16 Conceptual Framework Opportunity=Expected(sales sales>0) Opportunity=Average(sale sale>m) Optimism=Opportunity/ Current Sales 16

17 Data Mining Techniques Objectives for Similarity Measures Estimate Sales Opportunity Order Similar Companies Present k most similar companies with complete sales histories K Nearest Neighbour Used 17

18 Predictive Relationships Firmographics State is CA Historical Total Sales Historical Lotus Sales IBM Relationship Company is HQ Sector is IT Historical System p Sales Historical System x Sales Historical System z Sales New Rational Sales 18

19 User Interface 19

20 Evaluation of Success (a) Executives estimate that over $100 million of sales are traceable to the system A user survey identified the average productivity gain for sales personnel as two hours per week High rate of usage by sales personnel 20

21 Evaluation of Success (b) 21

22 Calculation of Savings With 6,454 users in 2008 saving 2 hours each, over 52 weeks in the year, enhanced productivity results in savings of $10 million a year 22

23 Positives Improved productivity of sales personnel More focused efforts on high opportunity leads The tool meets requirements of usability, usefulness and robustness 23

24 Criticisms Low rigour in evaluation criteria Does it actually improve IBM sales? Success seems to be anecdotal Does not fully exploit customer data Further processing of financial data is possible Competitor information missing 24

25 Questions? 25