Predictive Analytics in Action







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Analytics in Action

San Diego County


• 4,083 sq. miles – 8th largest county by size • 3 million residents – 5th largest by population • 725,000 children

• 13.9% living below poverty level

• Unemployment rate 5.9% (as of 10/14)


Our mission is to enhance our children’s futures by obtaining support for families today

73,000 cases

Over 113,000 children served

FFY 2013 - $178.7M

6% of California’s total caseload

FPM Performance FFY14 : #1 – 99.8%, #2 – 88.2%,

#3 – 70%, #4 – 69.8%, #5 - $3.85 (FFY2013)

County of San Diego







Predictive Analytics


Child Support Payment Predictor

Scope of Study:

1,033 cases opened 10/2011 through 5/2013

No prior support order


To establish a “payer” classification predictive tool that

prognosticates the NCPs who are potentially at risk to fall behind on their child support payments.

Provide staff with a tool to more effectively perform


Child Support Payment Predictor

Data Elements

466 data elements from the case and participants 68 field variables were created as candidates for

exploratory data analysis

29 variables were selected for model building 8 variables remain in the final model


Child Support Payment Predictor

Model data points

NCP’s Employment stability

Incarceration history

Unemployment in previous year

Living status in previous year Default history on first order NCP’s email information

NCP’s employment status at initial order establishment


Child Support Payment Predictor

Payment Predictor Details

Multinomial logistics regression


4 payer groups created:

Extremely rare payer (0%-30%)

Rare payer (31% to 50%)

Attempt payer (51 to 80%)

Constant payer (81% or greater)

Distribution of 6 month average payment rate (N=1033)


Child Support Payment Predictor

By payment class

Extremely rare payor (0%-30%)

Rare payor (31% to 50%)

Attempt payor (51 to 80%)

Constant payor (81% or greater)

Total due vs. Total payment for each payor class


Child Support Payment Predictor

Time to order or “waiting time”

Shortest - 2months Longest- 19 months


Child Support Payment Predictor

Variable – NCP e-mail address

54% had e-mail addresses Strong impact on payer class


Child Support Payment Predictor


Prediction accuracy of the model 62.5%

Percentage of the caseload more accurately monitored 14%

Able to target/focus on at risk cases before they


What’s Next or in the works?

Business Intelligence – “Smarter Planning” New analytics tools

Greater flexibility and detailed analysis

Review “payment predictor” for areas of further


Legal Paperless System – customer behavior Performance-based dashboard



What is Business Intelligence?

Set of theories, methodologies, processes and technologies that

transform raw data into meaningful and useful information.

Why do we need Business Intelligence?

 Reaching limit of producing NEW reporting and analytical results.  Paradigm shift from reactive reporting (the past) to proactive

reporting (looking into the future).

Who is likely to participate in the process? Who is likely to pay their child support? What will our caseload look like in 5 years?




Planning – A new way to plan

Smarter Strategic Planning

Strategic Planning Tool – predict future case loads, staffing needs, budget and


Smarter Case/Knowledge Worker

 Case Stratification Tool - Based on certain case data predictors, will be able to predict case outcomes with high probability.

 “Reduce” Case Loads: Focus on cases that need ‘personal touch’ – automate others.

Change Individual Case and Family Outcomes  Proactive Case Management

 Targeted Early Intervention




Planning – How do we get there?

4 Phases

 Phase I: DCSS Executive Dashboard

 Phase II: DCSS Smarter Planning

 Phase III: County-wide Data Sharing




Planning – Phase I

Phase I: DCSS Executive Dashboard

 The Past and Present

 Data Visualization of Current Performance Measures using Key Performance Indicators (KPI)*

 Summary of Case Load Performance  General Health of Department

 Central Reporting Portal

*A set of quantifiable measures that a company or industry uses to gauge or compare performance in terms of meeting their strategic and operational goals.




Planning – Phase II

Phase II: DCSS Data Mining and Predictive Analytics  Looking at the Future

 Predict future case performance, participant and staff behavior

 Correlate Staff Demographics to Case Data  Smarter Planning Tool

 Case Stratification Tool  Alerts




Planning – Phase III

Phase III: County-wide Data Sharing

Current limits to Business Intelligence based on only Child Support Data (silo effect)

Partner with other Departments including IV-A to Pilot  Richer Business Intelligence and Predictive Analytics




Planning – Phase IV

Phase IV: SmarterGoverning

 Executive Dashboard, KPIs and Reporting Portal Rollup for PSG


 Smarter, Integrated Knowledge Workers

 Probation Officer helping with Child Support Case


 Early Case Intervention

 Reduce recurring public sector demands

 Central Case Management Customer Service Portal  Predicting Future Budget, Facilities and Staffing


Credit Score


Jeff Grissom Director

San Diego County





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