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.

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. Agenda
PA Usage in Non-Marketing Areas
Different Arena, Different Approach
Success Factors and Case Studies
Q & A
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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 …
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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
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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
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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
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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
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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
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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
http://www.alexandergroup.com/blog/sales-analytics/preparing-data-for-insightful-sales-analytics/
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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
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11. Conclusion
• For every predictive analysis project, prepare the answers to three types of questions
– So what?
– Why?
– What if?
… In this order
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12. Q & A
izhao@ebay.com