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How to lead successful predictive analytics projects in sales and hr


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Analytics functions in B2B sales and HR usually start small. How to lead an impactful predictive analytics project is key to success and future growth.

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How to lead successful predictive analytics projects in sales and hr

  1. 1. How to Lead a Successful Predictive Analytics Project Practical Cases in Sales and HR Feb. 13, 2015 Ian Zhao Director, Comp Market Analytics eBay, Inc.
  2. 2. Agenda PA Usage in Non-Marketing Areas Different Arena, Different Approach Success Factors and Case Studies Q & A 1
  3. 3. Analytics – A Widening Field in Non-Marketing Areas • More corporate functions are building analytics teams • Endless applications for predictive analytics Marketing Sales HR … sales forecasting personnel turnover sales cost estimation compensation market customer retention talent acquisition sales force planning … 2
  4. 4. Different Arena, Different Approach • Predictive analytics in non-marketing areas tends to have: – Less data – More scattered data sources – Fuzzier objectives – Shorter time expectancy to insights – Team leaders have more executive exposure Different environments call for different approaches to problems 3
  5. 5. Success Factor 1: Beware of Different Analytical Methods • Problems can be resolved with different methods • Plan for contingencies before finding the default approach is not working • Case Study: Predicting Personnel Attrition Overall Attrition Survival Model Panel Data Cluster Analysis Time Series Analysis ARIMA with eReg 4
  6. 6. Success Factor 2: Access Three Types of Talents Business Consultants IT StaffData Scientists • Deploy ex-consultants to take care of the business • Lead data scientists to test hypotheses • Leverage IT staff to access data 5
  7. 7. Success Factor 2 (Continued): Key Skill Sets • PA team differs from the data reporting team Presentation, Interview, Primary Research Database and Big Data Processing Statistics and Data Modeling Data Reporting 6
  8. 8. Success Factor 3: Employ Lean Analytics Method • Identify the most important measure for business • Establish a “Minimum Viable Model” • Modify MVM based on feedbacks • Frequent milestones and status updates • Be prepared to ditch the model and start over • Case Study: High-Performer Compensation – Key question: How to compensate high performers? – Minimum viable model: how to compensate high performers in one job in the Bay Area? – Measure: High performer turnover rate – Be prepared to change High performers High performers 10th 25th median 75th 90th 7
  9. 9. Success Factor 4: Leave Ample Time for Data Preparation • Always start with data exploration • Confirm data availability for the hypotheses • Be prepared to impute data • Validate summary statistics with business community • Case Study: Modeling customer spending potential for a software company 8
  10. 10. Success Factor 5: Dedicate Time/Resource for Communication • Ask a “so what” question once reached insights • Communicate with executive at a higher (summary) level • Tell a story with data • Prepare answers the “What if” question 1. What drives seasonality? 2. What determines long-term trend? 3. What can we do (to reduce turnover and increase productivity)? 4. What’s the ROI? 1. Statistical Model 2. Business Questions 3. Executive Briefing Case Study: predicting attrition 9
  11. 11. Conclusion • For every predictive analysis project, prepare the answers to three types of questions – So what? – Why? – What if? … In this order 10
  12. 12. Q & A