Manu carricano gpes berlin 2013 ppt

Dr. Manu CARRICANODirector Pricing Center, EADA Business School

Director Research, Pricing Solutions Europe

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

Pluralitas non est ponenda sine necessitate

Plurality must never be posited

without necessityWilliam of Ockham

What is a rule?

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Rule #1:“Garbage in, Garbage out”

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…

… 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 DisruptionIntelligence

Transactional Pricing Analytics

Data from Value-Based Analytics:• Behavioral Segmentation

• Value Perception / Utilities

• Premium/ Elasticity / Market Response

Rule #2:“Granularity+Segmentation

= Visibility”.

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Segmentation is Key

Rule #3 :“Describe patterns to create awareness”

What do you see?

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%

Different segments, different conditions

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%

$

%

What should we see?

What should we see?

$

%

Corridor Sell-out /

Mini Net Price

Sell-out vs

Mini Net Price

Trade-offs

Elasticity vs demand response curve accuracy

Optimal Price

Polynomial Regression (degree 6)

Q

P

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Calibration: linking estimated price points to oberved price points

Premium

Premium

% V

olu

me

at

Ris

k%

Vo

lum

ea

t R

isk

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

Z (

Pro

fita

bili

ty)

Optimization: Identification of winning scenario among mutuallyexclusive / conjointly exhaustive options available

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

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

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

Learn about your own Pricing Power: Relate Environmental disruptions to predefined scenarios

Pricing Power is a copyrighted tool from

Learn how to Track Performance: Pricing Dashboards as a key element of change

ProfitabilityVolume

Gross Profit Margin

Net ProfitMargin

YTD Profit

Gross Profit Margin

Net ProfitMargin

$

SalesVariance

Gross SalesVariance

Net Sales Variance

PriceVariance

Gross PriceVariance

Net Price Variance

Pocket Margin

Variance

Other

MS

MixVariance

SoWSales Effectiveness

Premium

Price Volume

Elasticity

EfficiencyROI

PricingPower

Brand Equity

Competitiveness

PricingLeadership

Relative COGS

As a Wrap Up…

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#3

Patterns

#6 Control

[email protected]

[email protected]

@mcarricano