Truths & Misperceptions about Win Loss

Analysis© Primary Intelligence, Inc. 2015

June 10, 2015

Connie Schlosberg

Digital Marketing SpecialistPrimary Intelligence

Housekeeping

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Carolyn Galvin

Director of Industry InsightsPrimary Intelligence

The State of Win LossHow Organizations are Leveraging Win Loss Programs to Increase Buyer Understanding, Develop Competitive Strategies, and Improve Company Performance

Today’s Agenda• Brief Overview of Primary Intelligence

• What is Win Loss?

• Background & Methodology

• Selected Key Findings

• Conclusions & Recommendations

• Q&A

• Upcoming Research

• Prize Winners

Ken AllredCEO & Founder

We analyze more than 3,000 deals and customer engagements annually

We evaluate more than $20 billion worth of purchase decisions annually

We manage 568 win loss and customer experience programs

We support more than 6,000 sales, marketing, product, CI & customer leaders globally

We work with 6 of the Fortune 20

What is Win Loss Analysis?

• Explains buyer choice

• Transformative

Win Loss in Practice

• Differences in program maturity

• Differences in organizational approach

• Primary Intelligence:• Solution Performance• Sales Impact• Company Attitude• Pricing

State of Win Loss Research Background

• Goals: • Understand how organizations

are performing Win Loss Analysis and using Win Loss data

• Understand impact of Win Loss programs on company results

State of Win Loss Research Methodology

• 60-question online survey

• Outreach timing: March – October 2014

• Outreach to 5,400 individuals

• Feedback from 175 respondents

76%performing Win Loss

Unsure

All sales wins are analyzed

All large and/or strategic sales wins are analyzed

A random sample of sales wins are analyzed

A small number of wins are analyzed infrequently

Sales wins are never analyzed

0% 5% 10% 15% 20% 25% 30% 35% 40%

13%

15%

34%

20%

16%

2%

Overall Win Analysis Frequency

n = 126

Unsure

All sales losses are analyzed

All large and/or strategic sales losses are analyzed

A random sample of sales losses are analyzed

A small number of losses are analyzed infrequently

Sales losses are never analyzed

0% 5% 10% 15% 20% 25% 30% 35% 40%

15%

17%

33%

19%

15%

0%

Overall Loss Analysis Frequency

n = 126

3x – 7xhigher win

rate

Yes, we analyze why we win and lose sales opportunities

No, we do not currently analyze why we win and lose sales opportunities

I don't know if we analyze why we win and lose sales opportunities

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

67%

24%

10%

74%

22%

4%

83%

11%6%

Win Loss Program Participation and Win Rates

0-20% Win Rate (n = 21) 21-50% Win Rate (n = 77) 50%+ Win Rate (n = 66)n = 164

“Specific feedback on opportunities lost that

assists to increase future win rates.”

In the last six months, what was the most significant thing that your organization’s Win Loss Analysis program helped you with?

“Improve sales process and measurement

techniques…engagement model for sales reps to clients.”

“It enables me to ask for best practices and I

also learn what customer’s value.”

“Competitive forces.”

Real-time access (whenever I

need it)

Monthly Quarterly Every six months

Once a year I don't want or need access

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

58%

24%

12%

4% 1% 2%

Desired Frequency of Win Loss Data

n = 165

Win Loss Data FrequencyFitbit Primary Intelligence

“Accurate update on what competitors are doing in real time instead of six months later.” – Account Manager, Workforce Management Vendor

In the last six months, what was the most significant thing that your organization’s Win Loss Analysis program helped you with?

Real-t

ime

acce

ss (w

hene

ver I n

eed

it)

Mon

thly

Qua

rter

ly

Ever

y six

mon

ths

Onc

e a

year

I don

't wan

t or

nee

d ac

cess

0%

20%

40%

60%

80%

100%

62%

24%10%

0% 5% 0%

55%

29%

12%4% 0% 1%

58%

20%12%

5% 3% 3%

Win Loss Data Frequency and Win Rates

0-20% Win Rate 21-50% Win Rate 50%+ Win Rate n = 162

0

1

2

3

4

5

6

7

8

9

10

6.3 6.25.9 6.0 6.16.0

5.3 5.2 5.4

6.05.9

5.24.9

5.45.96.0

5.7 5.8 5.76.0

Win Loss Data Frequency and Company Performance

Real-time access (n = 95) Monthly (n = 40) Quarterly (n = 19) Bi-annually (n = 6)

n = 160

10

= S

ign

ifica

ntl

y b

ett

er,

0 =

Sig

nifi

can

tly w

ors

e

Feedback is collected from the buyer only

Feedback is collected from the responsible sales reps

only

Feedback is collected from both the buyer and the responsible sales reps

Unsure0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

19% 20%

57%

4%

Win Loss Collection Methodology

n = 126

Win Loss Collection Methodology

Potential customer

contacts PI about WL ProgramNo response from

PI in over 2 weeks

Prospect goes to competitor

PI conducts Loss Review;

interviews lost prospect

“No response for over 2 weeks”

SVP Sales -> Sales Rep: “Why

not?”

Sales Rep says he contacted

prospect day he got lead

Rep got lead 2 weeks after submitted

Breakdown: Emails going to

unmonitored inbox

STARTFIX

0

1

2

3

4

5

6

7

8

9

10

5.5

6.56.0

5.5 5.3

4.44.6

5.04.5

4.9

5.8

6.7 6.5

5.85.5

Win Loss Collection and Program Success

Feedback collected from buyer only (n = 24) Feedback collected from sales reps only (n = 25)Feedback collected from buyers and sales reps (n = 72)

10

= F

ar

exceed

s exp

ecta

tion

s,

0 =

Far

sh

ort

of

exp

ec

-ta

tion

s

n = 121

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

55%

22%

4%

10%

0%

9%

Sentiment about Amount of Win Loss Data

n = 165

0

1

2

3

4

5

6

7

8

9

10

5.0

5.75.5

5.2 5.0

6.7

7.6 7.6

6.5 6.3

Amount of Win Loss Data and Program Success

I need more Win Loss Data (n = 78) I have enough Win Loss Data (n = 34)

10

= F

ar

exceed

s e

xp

ecta

tion

s,

0 =

Far

sh

ort

of

exp

ecta

tion

s

n = 112

0

1

2

3

4

5

6

7

8

9

10

7.16.7 6.5

7.1

6.16.6

7.6 7.4 7.4 7.5

6.77.2

Amount of Win Loss Data and Win Loss IQ

I need more Win Loss Data (n = 91) I have enough Win Loss Data (n = 36)

n = 127

0

1

2

3

4

5

6

7

8

9

10

6.35.9

5.76.0 6.1

6.4 6.3 6.45.9

6.4

Amount of Win Loss Data and Company Performance

I need more Win Loss Data (n = 91) I have enough Win Loss Data (n = 36)n = 127

10

= S

ign

ifica

ntl

y b

ett

er,

0 =

Sig

nifi

can

tly

wors

e

Conclusions & Recommendations

• If no Win Loss program in place, start today

• Determine ways to implement real-time access

for users

• Collect information from buyers and sales reps

• Watch your game tape

• More detailed results in full report

• Participate in 2015 State of Win Loss research

MORE INFORMATION• 2014 State of Win Loss Report:

http://bit.ly/1IqsNDw

• Participate in 2015 State of Win Loss

http://bit.ly/1HjD8lO

• Industry Insights at Primary Intelligence:http://bit.ly/1QG2uLB

• Industry Insights Mailing List:http://bit.ly/1Mj3VhD

• Primary Intelligence:http://www.primary-intel.com/

Questions?

Upcoming Research

• 2015 State of Win Loss/State of Customer Experience

• Sales Intelligence analysis

• Buyer persona research

And the Prize Winners are…..

Connect with us!

linkedin.com/company/primary-intelligence

@PrimaryIntel primary-intel.com/blog

© 2015 Primary Intelligence, Inc.

37

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