3. Which is better?
They said they
And I
need more…
Clearly I have
I understand
understand
competitive
room to
my risk vs
What will it
maneuver…
tactics…
?
reward…
take to win?
5. Challenges in Tracking Win/Loss Data
“I don’t have time to
update this”
• Challenges
– Lack of methodology and tools to systematically capture
win/loss information or track the life cycle of a deal
– Data entry requirements and reporting complexity can limit
compliance
– Hiding or burying lost bids
– Behavioral and organizational inertia
– Many existing CRM systems lack value for sales people, so
hard to get them to buy in
– Reasons for gathering of won/loss data not readily apparent
9. Which example would you choose?
Ex. 1 Ex. 2 Ex. 3
• Great success • Great success • Great success
• Relies on: • Relies on: • Relies on:
– Savant – Team – Dumb luck
– Process – PED
– Discipline
– Information
10. Key Question to Ask in Tracking Win/Loss
• Did I win or lose?
• What constitutes a win or loss?
• Who did I win or lose against?
• What context did I win or lose?
11. Win/Loss Tracking as a Pricing Best Practice
Tracking Win/Loss information is best practice that helps pricing teams:
Identify areas of competitive advantage
Understand customer/product price sensitivity
Develop price-driven segments
Set price levels and guidelines to balance volume/share and margin objectives
Capturing and analyzing win/loss data lets executives answer key business questions:
Where/how do we win/lose deals?
What price wins for specific opportunities?
Where do we have pricing power?
It also enables more granular guidance to front-line sales people
What is my chance of winning this proposal?
I think we’ll win. What can I do to maximize the expected margin?
I want to increase our chance of winning. Can I do it without sacrificing margin?
12. Challenges in Tracking Win/Loss Data
Quote Order Situation Your Choice
Big Hat No Cattle: Win? Loss?
Product Qtty Price Product Qtty Price Overpromising volume to
A-100 100 $ 1.25 A-100 5 $ 1.25 extract lower price.
Price leakage.
Customer switches to Win? Loss?
Product Qtty Price Product Qtty Price lower value product.
A-101 100 $ 1.35 A-100 100 $ 1.25 Mix impact/ Margin
Leakage.
Product Expected Product Qtty Price Customer buys 5/6 of Win? Loss?
Family Volume Discount A-100 250 $ 1.25 expected unit volume but
A1 300 10% A-101 0 $ 1.35 at a different mix.
A-102 0 $ 1.45
Is this 1 win, or 1 win and
2 losses?
Product Qtty Price No buying activity on a Win? Loss?
A-100 100 $ 1.25 quote for 90 days.
End-User = Bob's MVNO One End use job, one Win? Loss?
Price $x Quoted to: VAR 1 VAR wins, did the other
VAR 2 three “lose”?
VAR 3 Or is job divided across
VAR 4 all 4 distributors?
13. Pricing Best Practice: Automate Win Loss Capture
Vendavo has a standard definition for wins and losses and the
ability to automate win loss attribution and tracking
14. Defining Wins and Losses in Vendavo
A WIN is defined as a line item or deal that has sufficient
activity
measured as cumulative volume and/or revenue during defined
period
Upon approval, deal/line item win loss status is ‘Pending’
To determine the win status, the committed revenue of the
line item and deal is compared with sum of all associated
transactions that match against it.
Client determines win loss parameters.
Activity win vs. volume win vs. mix win.
Upon reaching activity hurdle, line item/deal automatically
deemed a win.
Deemed a LOSS after X days ff no/insufficient activity
Override to win if activity after this date
15. An Example: Win Loss Tracking
Consider a Distributor Quote where
X (volume threshold to mark as won) =10%
Y (number of days to mark as won) = 5 days
Committed revenue (sum of supported price for this line item) is $2000 over 20 days (i.e.,
$100/day)
Revenue
Cumulative Cumulative
Day Commitment Win Status (reason)
Commitment Transaction
%
1 $100 $0 0 Pending (<5 days, so not Loss)
2 $200 $25 12.5% Win (12.5% > 10%)
Pending (8.3% < 10%, <5 days so
3 $300 $25 8.3%
not “loss”)
4 $400 $45 11.3% Win (11.3% > 10%)
Loss (9% < 10%, >5 days so not
5 $500 $45 9.0%
“pending”)
6 $600 $65 10.8% Win (10.8% > 10%)
Win (a “win” on/after day 5 stays a
7 $700 $65 9.3%
“win”)
16. Metrics for Tracking Win/Loss
Deal Metrics Dependent on Win/Loss Status of Deal
A “win” is when transacted revenue against a deal line item is > X% of quoted
Win Status
revenue. If within Y days of the deal line item valid from date the transacted
revenue is < X%, the deal line item is “pending.” If after Y days transacted
revenue is <X%, the deal line item is “loss.” After Y days, a deal line item
deemed a “win” will always be a “win.”
Where X = % of quoted
X is defined in the Win Threshold policy table. Y is defined by deal type as
revenue
part of the calculation.
And Y = Number of days
NOTE: Y value is not loaded from policy table but is hard coded in the system
after transaction
for every deal type
Percentage of transaction lines marked with a win as a percentage of
Win %
transaction count
Categorize approved deal line items as loss, pending, low compliance, or high
Rev Compliance Band
compliance based on revenue
% of realized Revenue (from “won” line items) relative to committed
Revenue Compliance %
revenues
Volume Compliance % % of realized Quantity (from “won” line items) relative to committed quantity
17. A Balanced Scorecard for Interpreting Win/Loss
Win loss metrics must be considered in a balanced scorecard In order to get a complete
picture of customer behavior
Complementary Metrics include:
• Volume and revenue commitment compliance
– So that you don’t misinterpret activity wins with low volume compliance
• Cherry Picking
– Winning only a few line items/materials on a much larger quote
– Indicates that you are likely lower than competitor on these line items
– Customer is taking your price back to primary supplier
– Or cannot find that material elsewhere
• Quote cycle time
– Sometimes a fast response can win even at high prices
– But the fastest turnaround may also reflect insufficient push back on some competitive
situations
• N. B. When analyzing wins and losses, remember the distinction between correlation
and causation
22. Power & Risk together determine Pricing guidance
High
High Power,
Pricing Power
Low Risk
Segment
Low Power,
High Risk Low
Segment High Low
Pricing Risk
Dir Approval
Floor
VP Approval
Dir Approval
VP Approval
Target
Floor
Target
23. Logistic Regression model
• Functional form of the logistic regression model
1
( p) ( a bp )
1 e
– a,b are parameters to be estimated from the data set
– p = price, Φ(p) = win probability given price p
• Parameter estimation using maximum likelihood estimation
– Method maximizes the probability that the model fits the patterns
seen in the data set
n a bp i
Wi (1 W i ) e
max ln( L ( a , b )) ln a bp i a bp i
a ,b
i 1 1 e 1 e
– Wi = 1 for win and Wi = 0 for loss
