Business Intelligence, Analytics, and Predictive Modeling Kim Gaddy

Transcription

1 Business Intelligence, Analytics, and Predictive Modeling Kim Gaddy

2 Business intelligence and analytics C O M P E T I T I V E A D V A N T A G E Optimization Predictive Modeling Forecasting/ extrapolation Statistical analysis Alerts Query/drill down Ad hoc reports Standard reports Degree of Intelligence What s the best that can happen? What will happen next? What if trends continue? Why is this happening? What actions are needed? Where is the problem? How many, how often, where? What happened? Source: Davenport, Thomas H. and Harris, Jeanne G. Competing on Analytics, The New Science of Winning A N A L Y T I C S R E P O R T I N G

3 Data warehousing, data mining and predictive modeling Data Warehousing Query and Reporting (SQL) Static Perspective Describe Past and Present Assume Hypothesis Classic Data Mining Statistical Analysis Continuous Changes Predict the Past Validate Hypothesis Predictive Analytics Prescriptive Algorithms + Discontinuous Changes Predict the Future Invent & Validate Hypothesis Source: Agosta, Lou. The Future of Data Mining Predictive Analytics, DM Review., August Online. (21 March 2007).

4 The killer app Predictive modeling is definitely not the killer app for BI, but the use of predictive models, especially embedded in operational analysis and Business Process Management, definitely is. Source: Raden, Neil. Models Take the Danger Out of Prediction. Online Posting. 28 Feb. 2007, Intelligence Enterprise. (4 Apr. 2007).

5 One perspective Knowledge Discovery is best left to the experts, those with PhDs, years of experience or both New tools may be easy to navigate and explore a Kohonen Network or association algorithms Unless you understand the whole process, the likelihood of producing reasonable looking, but spurious results, is high Deep understanding of data mining mathematics, training set design, and results analysis is key Source: Raden, Neil. Models Take the Danger Out of Prediction. Online Posting. 28 Feb. 2007, Intelligence Enterprise. (4 Apr. 2007).

6 Predictive modeling possibilities Delinquency? Write-offs? Reconnection? Fraud? Cadence? Payment Plans? Portfolio Sales? Agency Management? Roll Rates? Recovery? Acquisition? Up-selling? Cross-selling? Response? Conversion? Channels? Revenue? Price elasticity? Product mix? Demand? Messaging? Consumption? Customer Value? Preferences? Attrition? Lifetime value? Profitability? Cost to Serve? Decisions? Call volumes? Outages? Staffing?

7 Collections risk modeling can inform Collection policies Champion/challenger strategies Communications cadence/tone Deposit strategy Shutoff prioritization/ reconnection fees Rate plans promoted ACH/CC/DC targets Payment plans offered Skip tracing Agency strategies Portfolio debt sales Write-offs

8 Risk scorecards vs. predictive models Point systems seek to predict risk Credit score, payments made, account age, late payments, NSF checks and other factors drive weighted point assignment Much better than a one size fits all approach May describe the past, but perhaps not predict the future Reflects intuition, judgment and assumptions Hard to discern risk drivers

9 Apply predictive modeling as part of an integrated business process *ID what works *Update/rebuild models *Strategic plan input *Monitoring *Control *Triggers *Production processes Review Optimize Repeat Rollout Business Assessment Optimizing Business Value *Current situation vs. vision *Measurable objectives *Limitations/Constraints Analysis & Design In-market testing *Market analysis *Data mining *Scenario planning *Experimental design *Validate assumptions *Measurement Source: Epsilon, a subsidiary of Alliance Data

10 Phase 1: Baseline analysis Acquire data Summarize and prepare for analysis Analyze behavior by variables (e.g. age/ size of debt, weather, geography, season, tenure, demographics) Create baseline metrics and model strategy Source: Epsilon, a subsidia

11 Phase 2: Build the model Step 1 Universe Step 2 Create Analytic Datasets Step 3 Build/Test Step 4 Model Develop Model Build Validate 100s of variables 5-20 Predictors Pre- Modeling Implement Model Data DNA 100 Candidates Scoring Highest risk Lowest Risk High Moderate Low Source: Epsilon, a subsidiary of Alliance Data

12 Phase 3: Deploying risk scores Use of the resulting scores will help determine the best means of managing risk High risk customers, otherwise in good standing, can be steered toward rate plans that avoid summer surprises $ Preserve Standard/ Use testing Proactive/ Aggressive Proactive Write-off Risk Source: Epsilon, a subsidiary of Alliance Data

13 % Bad Debt Evaluate and monitor model performance Use holdout population for instant validation Back test against a different time period Monitor in market performance vs. baseline and control groups Bad Debt Rate - Predicted vs. Actual 70% 60% 50% 40% 30% 20% 10% 0% Deciles Learn Validate Average rate: 9.9% Cumulative % of write-offs 100% 80% 60% 40% 20% 0% Model Random Model Decile Lift Represents higher-than average write-off potential identified before it occurs Savings Represents people we don t need to treat in a special way Source: Epsilon, a subsidiary of Alliance Data

14 Thank you! Questions?