Predictive analytics for“Quantitative” ROI-driven decision making

Sai DevulapalliDirector, Technology Strategy Ericsson North America

Machines vs. Management

Score = 0.65

Training

Scoring

Machines Management

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

Machines and ManagementScore= 0.65

Score= 0.75

Training

Scoring

Training

Scoring

Prediction vs. Intuition vs. Reality

Score= 0.85

Training

Scoring

Prediction vs. Intuition vs. Reality

BusinessCycle 1

BusinessCycle 2

BusinessCycle n

Machines Management

Micro Decisions:Few decisions → Millions of instances

Observed Attributes

Predicted Outcome

BusinessDecision

Outcomes afterBusiness Decision

CustomersEquipmentDevicesNetwork nodes Products

Instances

UpgradeReplacePromoteMarket

Decisions

Nex

t bus

ines

s cyc

le

For each instance

Observed Attributes

PredictedOutcome

Score Outcome

Observed Attributes

Real Outcome on control group

Learn Outcome Business Decision bd1

Real Outcome after bd1

Learn impact of Business Decision bd1

bd1bd2

bdt

Experimental Groups (EG)

EG1

EG2

EGt

***

***

Two Stage Prediction

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

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

Business DecisionsBudget

Decreasing orderof ROI

Out of budget

Instance skipped

“Quantitative” ROI-driven decision making

Maturing predictions over business cycles

Num

. of I

nsta

nces

Business Cycles and Prediction maturity

Experimentation Business Intuition Predictive Optimization

Control Group

Experimental Group 1

Experimental Group 2

Experimental Group 3

Today

1. Pre-paid subscribers Churn analytics2. Marketing Campaign management

Whitepaper: http://goo.gl/Xw4XFL