Data Mining in Industry: Putting T heory into Practice

1. Data Mining in Industry:Putting Theory into Practice Bhavani Raskutti

2. Agenda • What do analysts in industry actually do? • Analytics in Australian Industry • Case studies • Telecommunications • Wholesale • Take-home Points

3. Business understanding of complex trends To make strategic & operational decisions • Data • Acquisition & Preparation • Presentation Data Matrix • Deployment • DAP • Problem • Definition • Mathematical • Modelling • (Algorithms) What do analysts in industry actually do? • Decision-making by users • Insights via GUI • Automation • Training • Documentation • IT Support • Business Problem • PD • MM • P • Initial Development • Iterative • 90% DAP • D

4. Agenda • What do analysts in industry actually do? • Analytics in Australian Industry • Case studies • Telecommunications • Wholesale • Take-home Points

5. Analytics in Australian Industry

6. Analytics in Australian Industry

7. Agenda • What do analysts in industry actually do? • Analytics in Australian Industry • Case studies • Telecommunications • Wholesale • Take-home Points

8. Increasing Revenue for Telstra Business Customers • - Satisfaction survey • - Service assurance • - Demographics • - Quarterly revenue from different products for each customer • - SVMs to score with likelihood of take-up • - Weighting by value of take-up to find high value take-up • - Winning back customers is hard • - Churn is hard to identify and harder to prevent • - Upsell to existing customers increases retention & revenue • - Implementation in Matlab & C • - Different predictive models for over 50 products in 4 segments • - Automatic updates every quarter • - Used by sales consultants to re-negotiate contracts • Excel spread sheet with potential customer list • - Take-up likelihood for all modelled products • - Last quarter revenue for all products • Create models to predict customers likely to take up a product soon • Increase revenue from business customers • Win-back? • Stop churn? • Upsell? • DAP • Imbalanced data – too few examples of take-up for most products • - Data aggregation & Interleaving • Comparable predictors from revenue • - Raw, change from previous, projected • - Use values as is & normalised • - Binarise using 10 equi-size bins • PD Labels i-5 i-4 i-3 i-2 T R A I N • MM i-4 i-3 i-2 i-1 • P i-3 i-2 i-1 i Prediction Predictors i-1 i i+1 i+2 • D

9. Increasing Revenue for Telstra Business Customers (Cont’d) • Evaluation: Piloted predictive modelling in 2 different regions • Region 1: 9 new opportunities from just 5 products with an increase in revenue of ~400K A$ • Region 2: Opportunities identified were already being processed by sales consultants • Conclusion: Predictive modelling better than previous manual process • Identifies more opportunities • Spreads techniques of good sales teams across the whole organisation • Deployed in 2004 & still operational • For more details, refer to “Predicting Product Purchase Patterns for Corporate Customers” by Bhavani Raskutti & Alan Herschtal in Proceedings of KDD’05, Chicago, Illinois, USA

10. Agenda • What do analysts in industry actually do? • Analytics in Australian Industry • Case studies • Telecommunications • Wholesale • Take-home Points

11. Wholesale Sales Opportunities at Retailers • Simple univariate regression in SQL • - Sales  demand • - Similar products @ similar outlets have similar demand to sales relationship • - Anomaly may be due to lack of stock • - Weekly SOH & sales for each store & SKU • - SKU master • - Store master • - Self-serve report in Cognos for each sales rep • - Presents list of products with opportunities • - Opportunities click through to detailed graphs showing demand, sales & stock position of the two products compared • Perform comparisons & find anomalies with stock issues • Increase wholesale sales into major retailers • DAP • - Quantify demand • - Define normalised sell-rate • - Define a long term in-stock measure • - Define products & outlets that are similar • PD • MM • P • D

12. Wholesale Sales Opportunities at Retailers (Cont’d) • R1 • R2 Sell Rate Demand In-stock % Demand

13. Wholesale Sales Opportunities at Retailers • Simple univariate regression in SQL • - Sales  demand • - Similar products @ similar outlets have similar demand to sales relationship • - Anomaly may be due to lack of stock • - Weekly SOH & sales for each store & SKU • - SKU master • - Store master • - Implementation in SQL & Cognos • - DataMartsfor reports updated weekly • - Documentation on intranet wiki • - Training by corporate training team • - Support from IT helpdesk • - Self-serve report in Cognos for each sales rep • - Presents list of products with opportunities • - Opportunities click through to detailed graphs showing demand, sales & stock position of the two products compared • Perform comparisons & find anomalies with stock issues • Increase wholesale sales into major retailers • DAP • - Quantify demand • - Define normalised sell-rate • - Define a long term in-stock measure • - Define products & outlets that are similar • PD • MM • P • D

14. Take-home points • Data acquisition & processing phase forms 80-90% of any analytics project • Business users are tool agnostic • R, SAS, Matlab, SPSS, … for statistical analysis • Tableau, Cognos, Excel, VB, … for presentation • Business adoption of analytics driven by • Utility of application • Ease of decision-making from insights • Ability to explain insights

15. Questions?