1. Dr. Manu CARRICANO
Director Pricing Center, EADA Business School
Director Research, Pricing Solutions Europe

2. THE RULE OF ALL RULES:
“PRICING IS HIGHLY REWARDING”
Jan’00/Jun’11 analysis by
+ 90% (2000 – 2011)
+50% (2008 – 2011)
S&P 500 vs Pricing Virtuosos Weighted Index
Source: Compustat, F500 & S&P 500

5. Pluralitas non est ponenda sine
necessitate
Plurality must never be posited
without necessity
William of Ockham

6. What is a rule?

8. SET
CONNECT
LEARN

9. SET
CONNECT
LEARN

10. Rule #1:
“Garbage in,
Garbage out”

11.  Transactional Data
 Customer Data
 Product Data
Transactional Must Have Data
• Invoice Number/ Lines – Unique
identification Number
• Time (Year, month, day, time, etc.)
• Volume (Quantity)
• Price
• Levers
Customer Must Have Data
• Customer number - Unique number
for each Customer
• Customer Name
• Segments (transactional)
• Geography (Country, Region, City, etc.)
• Related KPIs
Product Must Have Data
• Product Number - Unique number for
each Product
• Product Name
• Segments (transactional)
• Brand
• Category
Start With Must Have Data…

12. … But do not forget scalability
ERP
Research
Socio-
Market
Data
CRM
+
+
Data from Pricing Optimization:
• Predefined Calibrated Scenarios
• Optimal SO & Net Price points
• Competitive & Market Disruption
Intelligence
Transactional Pricing Analytics
Data from Value-Based Analytics:
• Behavioral Segmentation
• Value Perception / Utilities
• Premium/ Elasticity / Market Response

13. Rule #2:
“Granularity+Segmentation
= Visibility”
.
.
. . .
.
.
.
.
.
.
.
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14. Segmentation is Key

15. Rule #3 :
“Describe patterns to create awareness”

16. What do you see?
$
%

17. Different segments,
different conditions
$
%

18. $
%

19. What should we see?

20. What should we see?
$
%
Corridor Sell-out /
Mini Net Price
Sell-out vs
Mini Net Price
Trade-offs

22. Elasticity vs demand response curve accuracy
Optimal Price
Polynomial Regression (degree 6)
Q
P

23. SET
CONNECT
LEARN

25. Calibration:
linking estimated price points to oberved price points
Premium
Premium
%VolumeatRisk%VolumeatRisk

26. Scenarios:
Relate an optimal value at a certain stage
with dependent actions to be taken
Optimal
Condition 2
Optimal
Cost of Sales
Decided Sell-Out
Optimal
Condition 1
Optimal
Net Price
COGS
Gross Margin

27. Z(Profitability)
Optimization:
Identification of winning scenario among mutually
exclusive / conjointly exhaustive options available

28. SET
CONNECT
LEARN

29. Rule #6 :
“You can’t manage what you don’t measure”

30. Learn from controlled experiments:
Execution as a source of data for optimization

31. Learn how to track & document disruptions:
Variance analysis is at the heart of Pricing
-20%
-15%
-10%
-5%
0%
5%
10%
15%
20%
25%
30%
35%
40%
45%
50%
-250
-125
0
125
250
375
500
625
750
875
1000
Consolidated Crop Commodity Price Index ($/ton) BR Price variance without Gly (%)
SYN Price variance without BR and Gly (%)
t
Price Commodity Index
Price Variance BU 1
Price Variance BU 2

32. Learn about your own Pricing Power:
Relate Environmental disruptions to predefined scenarios
Pricing Power is a copyrighted tool from

33. Learn how to Track Performance:
Pricing Dashboards as a key element of change
ProfitabilityVolume
Gross Profit Margin
Net Profit
Margin
YTD Profit
Gross Profit Margin
Net Profit
Margin
$
Sales
Variance
Gross Sales
Variance
Net Sales Variance
Price
Variance
Gross Price
Variance
Net Price Variance
Pocket
Margin
Variance
Other
MS
Mix
Variance
SoW
Sales Effectiveness
Premium
Price
Volume
Elasticity
Efficiency
ROI
Pricing
Power
Brand Equity
Competitiveness
Pricing
Leadership
Relative COGS

34. As a Wrap Up…

35. SET
LEARN
CONNECT
#3
Patterns
#6 Control

36. mcarricano@eada.edu
mcarricano@pricingsolutions-eu.com
@mcarricano