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Data Mining in Industry: Putting T heory into Practice. Bhavani Raskutti. Agenda. What do analysts in industry actually do? Analytics in Australian Industry Case studies Telecommunications Wholesale Take-home Points. Business understanding of complex trends - PowerPoint PPT Presentation
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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
• What do analysts in industry actually do?
• Analytics in Australian Industry
• Case studies
–Telecommunications
–Wholesale
• Take-home Points
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
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
• What do analysts in industry actually do?
• Analytics in Australian Industry
• Case studies
–Telecommunications
–Wholesale
• Take-home Points
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
• What do analysts in industry actually do?
• Analytics in Australian Industry
• Case studies
–Telecommunications
–Wholesale
• Take-home Points
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?