This document discusses predicting sales team turnover. It notes that annual turnover is 27% with an average tenure of less than 2 years, costing the company billions in training and incentives. Benefits of predicting turnover include intervening to increase appreciation and motivation, making counteroffers, and substantial cost savings. The document proposes a quantitative model to identify high turnover risk individuals and why, using performance metrics, peer effects, and meeting targets. The model found middling performers most likely to turnover. Peer performance variation and turnover influence each other. Implications are identifying predictors from big data and considering peer effects.

1. Prediction of sales team turnover
Tahmina Khatoon
ID: 10414
ACMP Phase-2, Batch 4
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2. Objectives
● Effect of turnover
● Benefits of prediction of turnover
● Quantitative model for prediction
and it’s outcomes
● Implication of prediction model
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3. Effect of
turnover
● Annual turnover - 27%
● Avg. tenure - less than 2 years
● Company faces a number of
direct & indirect costs
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Of sales team

4. Direct & Indirect Cost for turnover
1. $15 billion for training
2. $800 billion for incentives
3. Reduces the Return On Investment
4. Empty position may hurts sales
5. Learning curve for new employees to rebuild client
relationships
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5. Benefits of
prediction
“If I Know Before They Have an
Offer, That’s a Big Plus”
Jay Mincks,
Executive vice president, Insperity
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Of turnover
Intervention
Appreciation
& motivation
Counter offer
Substantial
savings

6. Quantitative
model
For turnover prediction
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1. Who’s Your Most
Valuable Salesperson?
2. Who is at high risk of
leaving & why?
● Performance Measurement
● Peer effect

7. Sales Person’s Performance Measurement
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Past performance
according to revenue
Customer satisfaction Monthly target fulfilment

8. Expected vs Actual outcomes
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Performance
TurnoverRisk
Expected result
Actual result
It is the
‘middling’
salespersons
who are likely to
turn over

9. Peer Effect
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● Variation in performance among
coworkers
○ Less performance variation -> Less challenge -
> High turnover
○ High turnover -> Lass faith & strength -> More
turnover
● Voluntary and involuntary attrition

10. Implication of prediction model
● Identify turnover prediction variables using big data
● Pay careful attention to peer effects
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