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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 CyclesTrust 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 AttributesObserved Attributes
Predicted OutcomePredicted Outcome
BusinessDecisionBusinessDecision
Outcomes afterBusiness Decision
Outcomes afterBusiness Decision
CustomersEquipmentDevicesNetwork nodes Products
InstancesInstances
UpgradeReplacePromoteMarket
DecisionsDecisions
Nex
t bus
ines
s cy
cle
For each instance
Observed AttributesObserved Attributes
PredictedOutcomePredictedOutcome
Score Outcome
Score Outcome
Observed AttributesObserved Attributes
Real Outcome on control group
Real Outcome on control group
Learn Outcome
Learn Outcome Business Decision bd1Business Decision bd1
Real Outcome after bd1Real Outcome after bd1
Learn impact of Business Decision bd1Learn impact of Business Decision bd1
bd1
bd2
bdt
Experimental Groups (EG)
EG1
EG2
EGt
***
***
Two Stage Prediction
ROI(ia→bdb) : ROI in applying business decision bdb to instance ia
ROI(ia1→bdb1) ROI(ia2→bdb2)ROI(ia3→bdb3)ROI(ia3→bdb3) * * *
Business DecisionsBudget
Decreasing orderof ROI
Business Milestone. Ex.•Out of budget•ROI target reached
Instance skipped
“Quantitative” ROI-driven decision making
Determining ROI for a micro-business decision
ROI in applying business decision bdb on instance ia : CΔ(ia → bdb)
ROI in applying business decision bdb on instance ia : CΔ(ia → bdb)
Expected cost savings from change in outcome
Expected cost savings from change in outcome - Cost of business decision bdb on
instance CBD(bdb)Cost of business decision bdb on
instance CBD(bdb)
Expected cost of outcome prior to business decision
Expected cost of outcome prior to business decision
Expected cost of outcome after business decision
Expected cost of outcome after business decision
Cost of outcome yk on instance ia
CY(ia,yk)
Cost of outcome yk on instance ia
CY(ia,yk)X
Probability of outcome yk
for instance ia prior to bdb : P(ia,yk,,bdΦ)
Probability of outcome yk
for instance ia prior to bdb : P(ia,yk,,bdΦ)
Σ Over possible outcomes yk
-Cost of outcome yk on instance ia
CY(ia,yk)
Cost of outcome yk on instance ia
CY(ia,yk)X
Probability of outcome yk
for instance ia after bdb : P(ia,yk,, bdb)
Probability of outcome yk
for instance ia after bdb : P(ia,yk,, bdb)
Σ Over possible outcomes yk
Maturing predictions over business cycles
Num
. of I
nsta
nces
Business Cycles and Prediction maturity
ExperimentationExperimentation Business IntuitionBusiness Intuition Predictive Optimization
Predictive Optimization
Control Group
Experimental Group 1
Experimental Group 2
Experimental Group 3
Today
1. Pre-paid subscribers Churn analytics2. Marketing Campaign management1. Pre-paid subscribers Churn analytics2. Marketing Campaign management
Whitepaper: http://goo.gl/Xw4XFL