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Predictive analytics for
“Quantitative” ROI-driven decision making

Sai Devulapalli
Director, Technology Strategy
Ericsson North America
Machines vs. Management
Training

Machines Management

Scoring

Score = 0.65

Gut Feeling !
“Crossing the Chasm” in
Predictive analytics
• Align business intuition with predictions
• Is it the data or is it the inference ?
• Quantitative ROI metrics

Trust takes time, i.e. Business Cycles
Trust takes time, i.e. Business Cycles
Machines and Management
Score= 0.65
Business
Cycle 1

Training
Scoring

Score= 0.75
Business
Cycle 2

Training

Prediction vs.
Intuition vs. Reality

Scoring

Score= 0.85
Business
Cycle n

Machines Management

Training
Scoring

Prediction vs.
Intuition vs. Reality
Micro Decisions:
Few decisions → Millions of instances
For each instance

Upgrade
Replace
Promote
Market

Customers
Equipment
Devices
Network nodes
Products

Observed
Observed
Attributes
Attributes

Instances
Instances

Next business cycle

Decisions
Decisions

Predicted
Predicted
Outcome
Outcome

Business
Business
Decision
Decision
Outcomes after
Outcomes after
Business
Business
Decision
Decision
Two Stage Prediction
Score
Score
Outcome
Outcome
Observed
Observed
Attributes
Attributes
Learn
Learn
Outcome
Outcome
Real Outcome on
Real Outcome on
control group
control group

Predicted
Predicted
Outcome
Outcome

Observed
Observed
Attributes
Attributes

bd1

*
**
bd2

bdt

*
**

Business Decision bd11
Business Decision bd
Learn impact of Business Decision bd11
Learn impact of Business Decision bd
Real Outcome after bd11
Real Outcome after bd
Experimental Groups (EG)

EG1

EGt
EG2
“Quantitative” ROI-driven decision making
ROI(in→bda) :
ROI in applying business decision bda to instance in =
cost savings from change in outcome for in –
cost of business decision bda
Decreasing order
of ROI

Business Decisions
Budget

ROI(ia1→bda1)
ROI(ia2→bda2)
ROI(ia3→bda3)
ROI(ia3→bda3)
*
*
*

Instance skipped

Out of budget
Maturing predictions over
business cycles
Today

Num. of Instances

Experimentation
Experimentation

Business Intuition
Business Intuition

Predictive
Predictive
Optimization
Optimization

Control Group

Experimental Group 1
Experimental Group 2
Experimental Group 3
Business Cycles and Prediction maturity

1. Pre-paid subscribers Churn analytics
1. Pre-paid subscribers Churn analytics
2. Marketing Campaign management
2. Marketing Campaign management
Whitepaper: http://goo.gl/Xw4XFL
Whitepaper: http://goo.gl/Xw4XFL

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Dallas digital summit 2013 - Big Data Session Talk

  • 1. Predictive analytics for “Quantitative” ROI-driven decision making Sai Devulapalli Director, Technology Strategy Ericsson North America
  • 2. Machines vs. Management Training Machines Management Scoring Score = 0.65 Gut Feeling !
  • 3. “Crossing the Chasm” in Predictive analytics • Align business intuition with predictions • Is it the data or is it the inference ? • Quantitative ROI metrics Trust takes time, i.e. Business Cycles Trust takes time, i.e. Business Cycles
  • 4. Machines and Management Score= 0.65 Business Cycle 1 Training Scoring Score= 0.75 Business Cycle 2 Training Prediction vs. Intuition vs. Reality Scoring Score= 0.85 Business Cycle n Machines Management Training Scoring Prediction vs. Intuition vs. Reality
  • 5. Micro Decisions: Few decisions → Millions of instances For each instance Upgrade Replace Promote Market Customers Equipment Devices Network nodes Products Observed Observed Attributes Attributes Instances Instances Next business cycle Decisions Decisions Predicted Predicted Outcome Outcome Business Business Decision Decision Outcomes after Outcomes after Business Business Decision Decision
  • 6. Two Stage Prediction Score Score Outcome Outcome Observed Observed Attributes Attributes Learn Learn Outcome Outcome Real Outcome on Real Outcome on control group control group Predicted Predicted Outcome Outcome Observed Observed Attributes Attributes bd1 * ** bd2 bdt * ** Business Decision bd11 Business Decision bd Learn impact of Business Decision bd11 Learn impact of Business Decision bd Real Outcome after bd11 Real Outcome after bd Experimental Groups (EG) EG1 EGt EG2
  • 7. “Quantitative” ROI-driven decision making ROI(in→bda) : ROI in applying business decision bda to instance in = cost savings from change in outcome for in – cost of business decision bda Decreasing order of ROI Business Decisions Budget ROI(ia1→bda1) ROI(ia2→bda2) ROI(ia3→bda3) ROI(ia3→bda3) * * * Instance skipped Out of budget
  • 8. Maturing predictions over business cycles Today Num. of Instances Experimentation Experimentation Business Intuition Business Intuition Predictive Predictive Optimization Optimization Control Group Experimental Group 1 Experimental Group 2 Experimental Group 3 Business Cycles and Prediction maturity 1. Pre-paid subscribers Churn analytics 1. Pre-paid subscribers Churn analytics 2. Marketing Campaign management 2. Marketing Campaign management