data mining in industry: putting t heory into practice

Data Mining in Industry:Putting Theory into Practice

Bhavani Raskutti

Agenda

• What do analysts in industry actually do?

• Analytics in Australian Industry

• Case studies

–Telecommunications

–Wholesale

• Take-home Points

What do analysts in industry actually do?

Business understanding

of complextrends

To make strategic & operational decisions

Business Problem

Data Acquisition & Preparation

DAP Problem Definition

PD

D

Deployment

Presentation

P

Mathematical Modelling

(Algorithms)

Data Matrix

MM

Initial Development• Iterative• 90% DAP

Decision-making by users• Insights via GUI• Automation• Training• Documentation• IT Support

Agenda



• Case studies


–Wholesale


Analytics in Australian IndustryIndustry Clustering /

SegmentationClassification /

ScoringOther

• Customer/market segmentation

• Survey analysis• Sentiment analysis• …

• Upsell/Cross-sell• Fraud detection• Credit scoring• Location services• Churn modelling• …

• Marketing effectiveness• Market share understanding• Next best offer• Asset management• …

Telecom

Finance

Wholesale

Retail

Bio-informatics

Agenda



• Case studies


–Wholesale


Win-back? Stop churn?

Upsell?

DAP

PD

DP

MM

- Winning back customers is hard

- Churn is hard to identify and harder to prevent

- Upsell to existing customers increases retention & revenue

Increasing Revenue for Telstra Business Customers

Increase revenue from

business customers

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

- 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

Excel spread sheet with potential customer list

- Take-up likelihood for all modelled products

- Last quarter revenue for all products

- 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

Create models to predict customers likely to take up a

product sooni-5 i-4 i-3 i-2

i-4 i-3 i-2 i-1

i-3 i-2 i-1 i

i-1 i i+1 i+2PredictorsPrediction

LabelsTRAIN

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

Agenda



• Case studies


–Wholesale


DAP

PD

DP

MM

- Sales demand - Similar products @

similar outlets have similar demand to sales relationship

- Anomaly may be due to lack of stock

Wholesale Sales Opportunities at Retailers

Increase wholesale sales

into major retailers

- Quantify demand - Define normalised

sell-rate - Define a long term

in-stock measure - Define products &

outlets that are similar

- Weekly SOH & sales for each store & SKU

- SKU master - Store master

Simple univariate regression in SQL

Perform comparisons & find anomalies

with stock issues

- 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

Wholesale Sales Opportunities at Retailers (Cont’d)

Demand

In-s

tock

%

· R1· R2

Demand

Sell

Rate

DAP

PD

DP

MM

- Sales demand - Similar products @

similar outlets have similar demand to sales relationship

- Anomaly may be due to lack of stock

Wholesale Sales Opportunities at Retailers

Increase wholesale sales

into major retailers

- Quantify demand - Define normalised

sell-rate - Define a long term

in-stock measure - Define products &

outlets that are similar

- Weekly SOH & sales for each store & SKU

- SKU master - Store master

Simple univariate regression in SQL

- 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

- Implementation in SQL & Cognos

- DataMarts for reports updated weekly

- Documentation on intranet wiki

- Training by corporate training team

- Support from IT helpdesk

Perform comparisons & find anomalies

with stock issues

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

Questions?

data mining in industry: putting t heory into practice

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