Case Study

Pricing Strategy for Progresso Soup

Source: IRI Academic data 1900 supermarkets, 102 Chains across the

country + Census Demographics

D3M

Vishal Singh

NYU-Stern

Learning Objectives

Methodological Topics Developing Regression based Demand Models

Understand elasticity, Controls for Seasonality, Competition

How to use Regression Estimates Pricing Strategies, Forecasting

Market Segmentation Use Principle Component/Factor Analysis to understand demographic

characteristics

Use Cluster Analysis for Market Segmentation

Basics of Pricing Strategies Price based Segmentation & Profitability

o Institutional: Understand the scope of Scanner Data Primary source of data for the CPG industry

Current Situation

You are recently hired by General Mills as a brand manager for one

of their key brands “Progresso”. This product category is dominated

by Campbell but Progresso has made strides gaining market share

in several markets.

Background: The soup category is highly seasonal with demand

peaking in Winter months. In the past, Progresso has employed a

strategy of significantly reducing prices in periods of high demand

(Winter months) and then raising the prices during off-peak months.

Objective

Pricing Strategy for Progresso

Using the data provided, evaluate the current pricing strategy

of Progresso. Does “countercyclical pricing” make sense?

Evaluate the performance of Progresso across geographies &

customer demographics

Develop a regression based demand model to analyze price

elasticity for Progresso How does your own & cross-price elasticity vary by Census region?

Across Consumer Segments?

Suggest an alternate pricing strategy using information on

elasticity estimates to maximize profits

Understand the Scope of Data

Data Source: IRI Sample: 2000+ supermarkets from 102 chains across the US

Six years (2001-2006)

Store demographics based on ZIP codes (from US Census)

Monthly Sales for each brand in each store, Price/Promotion

NOTE: This is pretty much the data that Campbell or Progresso would have

Approach

Always start by summarizing the data

Store Location & Demographics

Marketing Mix (Shares/Price/Promotion)

Seasonality

Strong Markets & Time Periods

There are two files:

“Transaction” data

Store demographics

What is the information contained in each? What is

the link? Why are they not merged to begin with?

Quick Examination of Store Demographics

Lets Keep a few variables (State, Income, & Income

Quintiles) from Store demographic file and merge

with Transaction data

– Note that a full merge will drastically increase the size of

our file

Always check your variables at a higher level

– Two important variables always are Time & Geography

Variables in Transaction Data Understand the variables. Why is “date” messed up?

Average Price & Volume

By Census Regions

Time Series of Sales

Category Sales by Month

Extract “month” from Date

Average Price by Month

What do we infer?

Market Shares by Month for Progresso

Relate this to Pricing Strategy

What about our competitor?

What have we learnt? Should we Change the definition of “Winter” dummy?

Using the data provided, evaluate the current pricing strategy of Progresso. Does “countercyclical pricing” make sense?

Evaluate the performance of Progresso across geographies & customer demographics

Develop a regression based demand model to analyze price elasticity for Progresso How does your own & cross-price elasticity vary by Census region?

Across Consumer Segments?

Suggest an alternate pricing strategy using information on elasticity estimates to maximize profits

Building a Regression Model

Price Elasticity & Segmentation for Progresso Soup

D3M

F1: Understand the Phenomenon

Examine your objectives at a broad/intuitive level

o Without thinking about data analysis

What factors might explain variation in monthly

sales of Progresso across stores in the US? o Our objective might be specific (e.g. estimate price elasticity to

guide pricing decisions) but we need to “control” for other

factors that impact the phenomenon

o Some things we just can’t control, e.g. we don’t have data or

maybe ability to measure

Regression Based Modeling Fundamental Modeling Tool

Why do we (teach) use regressions?

Determine whether the independent variables explain a significant

variation in the dependent variable: whether a relationship exists.

Determine how much of the variation in the dependent variable can be explained by the independent variables: strength of the relationship.

Control for other independent variables when evaluating the contributions of a specific variable or set of variables. Marginal effect

Forecast/Predict the values of the dependent variable.

Use regression results as inputs to additional computations: Optimal pricing, promotion, time to launch a product….

Log Models will Fit Data Better

Log-Log Model:

• The Price coefficient can be interpreted as :1

percent change in Price leads to an estimated b1

percentage change in the Sales. Therefore b1 is

the Price elasticity.

i1i10i ε Pln ββ Sln

Intuition for Log Models: Click on the link below. It takes you to GAPMINDER,

where you can see relationship between different Country attributes over time.

Change the scale in the corner from “Log” to “Linear” and imagine how a

regression line would fit.

Semi-log specification

For the semi-log model:

• Now Price is measured in regular units

and Sales in log.

– The coefficient of Price can be interpreted as :

a 1 unit change in Price leads to an

estimated b1 percentage change in the Sales.

i1i10i ε P ββ S log

Elasticities from Regression

Linear Model

SALES

PRICEae

PRICEaaSALES

1

10

PRICEae

PRICEaaSALES

1

10

ln

1

10

lnln

ae

PRICEaaSALES

ii

itit

Semi-Log Model

Log-Log Model

Why do we care about price elasticity?

How do you price a product?

o What factors must we consider in determining what price to

charge?

A key input into our pricing decision is consumer price

sensitivity to our product

Our exercise will involve

Estimating price elasticity for Progresso, after controlling for

other factors impacting sales

Examine how price elasticity varies by various segments (e.g.

East coast vs. South, High vs. low income, Output from

clustering of IRI stores)

Why Care About Elasticity? Cross Price Elasticity is one of the best measure to understand Competition

Log-log regression model:

log 𝑞𝐴 = 𝛽0𝐴 + 𝛽𝐴𝐴 log 𝑃𝐴 + 𝛽𝐴𝐵 log 𝑃𝐵 + 𝛽𝐴𝐶 log 𝑃𝐶 + 𝛽𝐴𝐷 log 𝑃𝐷 + 𝜀𝐴

Own price elasticity Cross price elasticities

Understand this intuitively

Lets go to data for some intuition

Price Elasticity & Segmentation for Progresso Soup

D3M

Lets start with the simplest model Sales only depend on my price

Linear Semi-log Log-log

What are the price elastitcities from the 3 models?

Log-log Model With Competitive Prices

Dependent variable: Log(Volume_Prog)

What brand competes most closely with Progresso?

How much would Sales of Progresso drop if Campbell runs a 10% promotion?

Question

What would happen to Progresso sales if

Progresso cuts its price by 10%?

Campbell/Other/PL cut price by 10%

Closest competitor to Progresso?

Anything unintuitive?

Keep in mind that what we can potentially understand from numbers depends on what inputs we feed in

GiGo stands for ‘Garbage in Garbage out’

Always question the broader context

Notice the implications when we build a better regression model and how price elasticity estimates change

Create a New Variable “Season”

Months of Oct to March as “High Season”

New Definition Old Definition

Control for Seasonality of Sales

Dependent variable: Log(Volume_Prog)

We continue to get incorrect sign for “Other” brand cross price elasticity

Control for Regional Differences

Regional control seem important in

our context:

1) Fit has improved

2) Elasticity estimates are quite

different

3) Cross-price elasticity for “other”

brand is finally positive as we

would expect

Regressions by Census Region Note: Seasonality controls not shown

East Coast Midwest

South West Coast

What can we say about competitive strength of Progresso across US Census Regions?

If we were manager of Progresso, these numbers provide a number of useful insights.

Discussion

Analyze the competitive position of Progresso across Census

Regions based on own & cross-price elasticity

What are the implications in terms of pricing & positioning

strategies for Progresso?

Next: Market Segmentation of Stores

Multivariate Analysis Segmenting Stores in Soup Case Study

D3M

This is the store demo file

Variables in Store Demo File

Objective

Segment the 2000 IRI stores into smaller groups Interpret the segments you created

Compute the price elasticity for each segment and discuss the pricing strategy that Progresso should pursue to maximize profits State the assumptions used in deriving optimal prices for

profit maximization

Discuss the practicality of your recommended pricing strategy

Approach

Questions you should ask

Segmentation based on what??

How many segments??

Always start by summarizing variables in your

data and understanding the basic relationships

Understand the correlation b/w variables –store

demographics & market shares

These are what we will use for segmentation

As usual, start by summarizing the data

Several of the

Demographic

variables are Highly

Correlated

Correlations of Market Shares Across 2000 Stores What can we learn about Progresso’s Competitors from just correlations?

Campbell does well in Midwest

& South

Progresso is strong in East,

followed by West

Segmentation of IRI Stores

D3M

Factor & Cluster Analysis

Learning Objectives

Unsupervised Learning Methods

Principle component, Factor Analysis, & Clustering

Objective is Dimension Reduction Reduce the number of collinear variables (PCA/Factor)

Group your rows (e.g. customers, markets, counties): Cluster Analysis

Additional Learning Resources

MIT Open Courses Lecture 11 & 14

Data Mining Class at U of Chicago (Lecture notes 7 & 8)

Stanford course on Machine Learning: Watch Lecture 10 on

“Unsupervised Learning”

Note the Difference between Cluster and PCA/Factor analysis

V1 V2 V3 V4 V5 V20 …..

Cluster Analysis

(Group Subjects)

Factor Analysis

(Group Variables)

Data

Variable Reduction Techniques You are working with columns here

We will look at 3 Techniques

Principle Component Analysis

Factor Analysis

Cluster of Variables

PCA/Factor Analysis

Our demographic variables are highly correlated If we were to use these in a Regression model for example, we will high

multicollinearity

A useful technique for reducing the number of variables is Principle Component Analysis (PCA) & Factor Analysis

PCA/Factor analysis is able to summarize the information contained in a larger number of variables into a smaller number of ‘factors’ without significant loss of information

Widely used technique in Psychometrics (less so in econometrics)

If we use 3 components, we capture approximately 84% of information

contained in the 10 demographics

Eigenvalues of a matrix are also

called characteristic roots and

represents the variance accounted

for by a linear combination of the

variables. Usually # of components

to use is Eigenvalue greater than 1.

In our case its 3

Principle Component Analysis

Cluster of Variable Algorithm

We can use Median Income, % Kids 18, and % Black. These 3 variables will be representative of other demographics in its cluster

Look for large positive or negative numbers for each factor. See the corresponding variable names to interpret the underlying ‘factor’

These are called factor “loadings”. Measures the correlation between each demographic

and the underlying “factor”. Our Job to Interpret and put a label to these.

Factor Analysis

Using 3 “factors” instead of 10

demographics, we capture approx.

84% of the information.

What do these techniques do?

Take a large number of variables

that are highly correlated & create

new variables

New variables (components or

factors) are linear combinations of

our current variables

Goal is to retain most of the

variability (information) in the data

Reduce the dimension of the

problem with little loss of

information

Newly created variables are

orthogonal (no correlation)

Note: Our current application of 10 demographic variables is quite trivial. We will see larger problems where these methods are more useful

These are the

new variables

in our data.

Our job is to

interpret

them. The

new variables

(factors) are

standardized

and

uncorrelated.

We can use

them further

for other

analysis, for

example

Segmentation

of stores in

our data.

Examine the Factor Scores

The new variables (Factors) have a mean of 0 and Std of 1. They are orthogonal to each other (zero correlation)

Cluster Analysis Segmentation of IRI Stores

D3M

Now we are interested in grouping rows (Stores in our case)

V1 V2 V3 V4 V5 V20 …..

Cluster Analysis

(Group Subjects)

Factor Analysis

(Group Variables)

Data

57

Cluster Analysis

Cluster analysis is a technique used

to identify groups of ‘similar’

customers in a market (i.e., market

segmentation).

Cluster analysis encompasses a

number of different algorithms and

methods for grouping objects of

similar kind into categories.

58

General question: how to organize observed data into meaningful structures

• Examples: o In food stores items of similar nature, such as

different types of meat or vegetables are displayed in the same or nearby locations.

o Biologists have to organize the different species of animals-- man belongs to the primates, the mammals, the amniotes, the vertebrates, and the animals.

o In medicine, clustering diseases, cures for diseases, or symptoms of diseases can lead to very useful taxonomies.

o In the field of psychiatry, the correct diagnosis of clusters of symptoms such as paranoia, schizophrenia, etc. is essential for successful therapy.

o Collaborative filtering & Recommendation systems

59

Cluster Analysis

Cluster analysis works on the principle of maximizing the between-cluster variance while minimizing the within cluster variance

Methods: Hierarchical & K-mean Clustering

Clustering Methods

Hierarchical clustering is an iterative process that starts with

each observation in its own cluster. At each stage, the

algorithm combines two clusters that are closest together. At

the final stage, all observations are in one cluster.

Useful for small data sets, takes a long time for large tables.

60

K-means clustering starts with a known number of clusters, k. The

algorithm picks k cluster seed points, then assigns each observation

to a cluster. It then replaces the cluster seeds with the cluster

means and repeats until the clusters stabilize.

Works well with large data sets

Hierarchical Clustering of Stores Questions to Ask: Clustering based on what? How Many Segments?

Exercise

Conduct a Hierarchical cluster analysis based on Saved Factor Scores & Market Shares of Brands

To keep things manageable, lets use a 5-segment solution

Interpret the clusters based on Median Income, % Kids Under 18, % White, & Market Shares

What segment has the highest appeal for Progresso?

Save the cluster membership and merge file with Transaction data Redo the regression analysis and analyze the own & cross-price elasticity in

each segment

Suggest an optimal pricing strategy for Progresso for each segment

Discuss practical considerations in using such segmentation/pricing scheme

Appendix

Quick Review of Pricing Strategies

Quick Review of Pricing Strategies

Market Segmentation

Optimal Pricing

Cost-Plus Pricing

𝑃𝑟𝑖𝑐𝑒 = 𝐴𝐶 × 1 + markup

AC = Average cost of meeting a certain sales target

markup = Mark-up percentage

Competition-Based Pricing

𝑃𝑟𝑖𝑐𝑒𝑢𝑠 = 𝑃𝑟𝑖𝑐𝑒𝑡ℎ𝑒𝑚 ± 𝜀

“Example: Follow the leader ”

Competition Manufacturer Distributor Retailer

Channel Pricing

Psychology and Pricing

Demand Measurement

Π(p) = p*q(p) – [FC + c*q(p)]

Market Segmentation & Pricing

What is the Difference between the two?

Airline Pricing

How do airlines

segment customers?

Amazon.com price histories

McDonald’s - WiFi charge:$0

The Plaza Hotel, NYC. WiFi charge in $1000/night suite: $14.95

Price of WiFi

Versioning: Product Line Sort

Menu of products at different price points with different attributes:

Price Customization: Legality

B2C: No pricing law Price discrimination is legal but you can get in trouble if

customers who pay a higher price are a protected class under civil rights law

In addition, some states have laws that hold private businesses liable for certain types of discrimination

Price Customization: Legality

B2B: Robinson-Patman Act of

1936: “It shall be unlawful for any person engaged in commerce, in the course of such commerce, either directly or indirectly, to discriminate in price between different purchasers of commodities of like grade and quality, where either or any of the purchases involved in such discrimination are in commerce….”

Demand Measurement

Approaches: Regression Based Models

Surveys

Field Experiments

Historical Data

Conjoint Analysis

Psychology & Pricing

Pay-What-You-Want

Free

Psychology and Pricing

Google’s History*

I. (1999-2001) Invent a way to do search that gets better as the Web gets bigger

II. (2001-2003) Adopt a self-service way for advertisers to create ads that match keywords

III. (2003-) Create countless other services (that users want) for free

* From “Free: The Future of a Radical Price”, Chris Anderson.

Google’s Max Strategy

Free is the fastest way to maximize market share and enable mass adoption.

“Take whatever it is you are doing and do it to the max in terms of distribution. The other way of saying this is that since marginal cost of distribution is free, you might as well put things everywhere”

Why does Free work so well?

$0.15 $0.01

73% choose Lindt 27% choose Hershey

A

Lindt Truffle Hershey Kiss

$0.14 Free!

31% choose Lindt 69% choose Hershey

B

Source: “Predictably Irrational”, Dan Ariely

Dropbox: An Example of “Freemium”

Psychology: Price Framing

$2.80/gallon, $0.10 discount if pay with cash

$2.70/gallon, $0.10 surcharge if pay with credit card

Price?: “It’s up to you. It’s

really up to you.”

Success? Probably

Estimate: 1M downloads with 40% paying something.

Pricing: You Decide!

Success? Probably

Estimate: 1M downloads with 40% paying something.

Price Optimization

p

Π

Π(p)

p

0)(

p

p

Simple case

• Given knowledge of my sales’ sensitivity to price and cost structure, how should I price?

• Let q(p) be my sales at price p. Total profit at p is then

• To make things easy assume that you are the market leader (ignore competition)

Π(p) = p*q(p) – [FC + c*q(p)]

Total cost at the price p

p

Π

Π(p)

p

Analytical solution: 0)(

p

p

Deriving the optimal price

• Analytical solution:

11

c

p

β is the own

price elasticity

We can obtain β from the log-log sales response model!

Optimal price depends

on marginal cost and

own price elasticity