Dynamic Price

Sensitivity Measurement

Joe Sakach

Joy Joseph

Vice President - Modeling & Analytical

Solutions, IRI

Director - Next Generation Analytics &

Practices, Campbell Soup Company

Case for Change

• Pricing continues to be critical concern …

especially given the heightened consumer

focus on “value”

• Price elasticity is still a critical metric … even

after 100+ years

• Tendency to treat price elasticity as a static

metric…despite the evidence that it is dynamic

Solution

• Dynamic Pricing … a new approach for

measuring price elasticity

• Provides a timely indicator to changes in price

sensitivity and what is driving them

• Is consistent with current price elasticity

measurement approaches

Why Dynamic Pricing?

• A rapidly evolving economic environment demands

the most current pricing metrics

• Understanding price elasticity drivers will allow better

management of brands for long term health

• Predicting future price elasticity is key to improving

the ability to plan future pricing actions

Maximize

EBIT

Measure Price Sensitivity Trend

Anticipate Price

Sensitivity Shift

Time Pricing

Change Maximize

Profits

Why Does Elasticity Evolve?

Marketing driven

differentiation

communicates

volume driving

positioning benefits

Innovation

differentiates the

product on

functional &

emotional benefits

Value proposition

offered by your

product vs.

competitive

choices, impact

consumer price

sensitivity

Elasticity evolves as these drivers evolve

Elasticity

Drivers

Economic-

Income

Competitive

Relevance

Economic-

Inflation

Marketing

& Innovation

Methodology Overview

Moving Window Model & Outlier

Smoothing

Qualitative Judgment &

Principal Components

Predictive Model

(VARMAX)

Forecast Model

• Estimate price elasticity trend over latest 4 years

4 Year Elasticity Trend

• Identify and validate potential drivers of price elasticity

• Product Drivers

• Category Drivers

• Macro Economic Drivers

Identify Drivers • Develop model that explains changes in price elasticity

Develop Model

• Develop forecasts by projecting anticipated values of drivers in the model

Forecast Elasticity

Elasticity Trend Analysis

• Short spans of overlapping periods to model short-term

fluctuations in elasticity

-1.8

-1.6

-1.4

-1.2

-1.0

-0.8

-0.6

-0.4

-0.2

0.0

Fe

b-0

5

Se

p-0

5

Ma

r-0

6

Oct-

06

Ap

r-0

7

No

v-0

7

Ju

n-0

8

De

c-0

8

Ju

l-0

9

Elasticity Loess Filter Smooth

1.2

1.0

Y Ago CY

26-Week

Moving

Window Model

48 Iterations of 26-

Week Models yield a 4-

year (48-Month)

Elasticity Trend

Remove noise from

trend using a custom

filter

Identifying Drivers of Elasticity

Classify potential drivers into logical clusters

Trip Frequency

Gas Prices

Shopping Trips

Category Competition

Promo Volume/

Item

Price Index vs. Category

Purchasing Power

Prime Rate

Consumer Revolving

Debt

Income

Personal/

Disposable Income

Non-Farm Payrolls

Promotional Intensity

Display & Feature

Execution

Promoted Price Index

Inflation

Total CPI

Food CPI

Predictive Model Selection

Selection Criteria:

• Hold-out Sample Forecast Accuracy

• Explanatory Power of Causal Drivers

Causal Time Series Forecasting

Models

Auto Regressive Error Model

Distributed Lag Model

VARMAX Model

Predictive Time Series Model

• VARMAX model (SAS® VARMAX Procedure)

– Vector Auto Regressive Moving Average Model with Exogenous

variables

Generating Elasticity Forecasts

-1.6

-1.4

-1.2

-1

-0.8

-0.6

-0.4

-0.2

0

CNS 10 oz (Smooth) Forecast Actual

Model Calibration Validation

Actual Forecast

Business Applications

• Tracking - Indicator of shifting consumer

price sensitivities

• Strategic Pricing - Guide the management

and planning of pricing actions

• Marketing Effectiveness - Measure the

long-term impact of marketing mix

Price Elasticity Tracking

• Price sensitivity was trending more negative but

leveled off

• What drove this change?

More Price

Sensitive

Less Price

Sensitive

BRAND A

-0.2

-0.15

-0.1

-0.05

0

0.05

0.1

0 1 2 3 4 5 6 7 8 9 10

Imp

act

Lag (Months)

Impulse Response Function (IRF)- Exogenous Factors (Series in Descending Order)

Avg Items Per Store

%of Base Volume on Feature Only

Shopping Trips

%of Base Volume on Display Only

Base Price Index

PPG %Vol Any Merch

Unemployment

CPI (Food)

Driver Analysis

• Inflation is by far the biggest driver of price sensitivity

Facto

rs In

cre

asin

g E

lastic

ity

Fa

cto

rs d

ec

rea

sin

g E

las

tic

ity

BRAND B

(374)

(88)

(52)

(42)

2

42

77

123

Impact Index*

* Impact Index measures the relative importance of the drivers to each other

() indicates a negative impact

0.010.080.020.020.01

0.260.11

0.06 0.04 2.2

1.6

Sta

rtin

g

Un

em

plo

ym

en

t

CP

I (F

oo

d)

Bra

nd

% V

ol

An

y M

erc

h

Bra

nd

% V

ol

Fe

at

Bra

nd

% V

ol

Dis

p

Sh

op

pin

g

Tri

ps

Ba

se

Pri

ce

Ind

ex

Oth

er

Av

g Ite

ms

En

din

g

Due-To Analysis

• Decomposition identifies drivers of elasticity change

Driver

Chg.

+ 3.9% +5.4% Y-o-Y

+21.4%

-0.1 -0.33 pts +4.3% Cat Base

Price +3%

+0.3

Items

+0.6

Note: 12 Months Ending Oct 09

BRAND B

The economy was

major driver of

elasticity increase!

Elasticity Forecasting

• A negative economic outlook will further increase

price sensitivity for Brand B

-3.5

-3

-2.5

-2

-1.5

-1

-0.5

0

1-O

ct-0

5

1-D

ec-0

5

1-F

eb-0

6

1-A

pr-

06

1-J

un

-06

1-A

ug-

06

1-O

ct-0

6

1-D

ec-0

6

1-F

eb-0

7

1-A

pr-

07

1-J

un

-07

1-A

ug-

07

1-O

ct-0

7

1-D

ec-0

7

1-F

eb-0

8

1-A

pr-

08

1-J

un

-08

1-A

ug-

08

1-O

ct-0

8

1-D

ec-0

8

1-F

eb-0

9

1-A

pr-

09

1-J

un

-09

1-A

ug-

09

1-O

ct-0

9

1-D

ec-0

9

1-F

eb-1

0

1-A

pr-

10

Elasticity Forecast Positive Outlook Negative Outlook

BRAND B

Financial Impact

• Predicting shifts in price sensitivity can result in

significant savings

Price Increase 5%

Price Change Time April 2010

Elasticity Prior To Increase -1.4

Elasticity 6 Months From Increase -2.0

Elasticity 12 Months From Increase -1.6

Impact of Price Chg – Price Now -9.3%

Impact of Price Chg – Price in 12 mo -7.5%

Savings $ 596,224

• Traditional marketing mix analysis measures

short-term impact of marketing drivers

• Marketing effectiveness measurement can now

include an estimate of the “value” of these long-

term benefits

Short – Term

Incremental

Volume

ROI

Long – Term

Value of

Elasticity

Improvement

Marketing Effectiveness

+

Improved Measurement of Marketing Effectiveness

Marketing Effectiveness (cont’d)

• Increased TV investment generates additional “value”

for the brand through the ability to support higher

pricing

$0.00

$0.10

$0.20

$0.30

$0.40

$0.50

$0.60

$0.70

$0.80

$0.90

ST Incr Vol Impact LT Elasticity Impact

Note: LT Elasticity impact is based on the incremental pricing that is supported

by the reduction in price elasticity

BRAND A

Case Study

• Leveraged successful Proof-of-Concept to roll

out approach in 2009 across 20% of core

product groups

• Purpose of study

– Determine the impact of current economic crisis on

price sensitivity

– Understand the role that competition and category

dynamics

– Develop price elasticity forecasts based on different

scenarios of economic, category, marketing plans

assumptions

Driver Results

• The drivers

of price

sensitivity

vary by

brand both

in terms of

direction

and

magnitude

FORECASTING MODEL RESULTS

Key Insights

• Increases in base pricing can have negative impact on price sensitivity

• 2 economic drivers had a consistent and significant negative impact across our brands

• Merchandising can have both a positive and negative influence on price sensitivity – Feature activity can act as an “advertising” proxy

for some brands

• Advertising support can have a long term benefit to brands in addition to the short term gains measured through our MMX analyses

Benefits

• Provides a more time sensitive

measurement of price elasticity

• Determines the key drives of price

sensitivity and decomposes their impact

– Relative magnitude and duration

• Allows for future scenario planning of likely

price elasticity levels

• Provides a long-term estimate of the value

of marketing mix

Questions?