This case study examines the pricing strategy for Progresso soup using data from over 1900 supermarkets across the U.S. It focuses on evaluating the effectiveness of countercyclical pricing, analyzing price elasticity, and segmenting the market based on demographics. The study also explores regression modeling and clustering techniques to inform better pricing strategies for maximum profitability.

1. 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

2. 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

3. 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.

4. 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

5. 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

6. 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?

7. 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

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

9. Average Price & Volume

10. By Census Regions

11. Time Series of Sales

12. Category Sales by Month
Extract “month” from Date

13. Average Price by Month
What do we infer?

14. Market Shares by Month for Progresso
Relate this to Pricing Strategy

15. What about our competitor?

18. 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

19. Building a Regression Model
Price Elasticity & Segmentation for Progresso Soup
D3M

20. 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

21. 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….

22. 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 εPlnββSln 

23. 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.

24. 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 εPββSlog 

25. Elasticities from Regression
Linear Model
SALES
PRICE
ae
PRICEaaSALES


1
10
PRICEae
PRICEaaSALES


1
10ln
1
10 lnln
ae
PRICEaaSALES
ii
itit


Semi-Log Model
Log-Log Model

26. 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)

27. Why Care About Elasticity?
Cross Price Elasticity is one of the best measure to understand Competition
Log-log regression model:
log 𝑞 𝐴 = 𝛽0𝐴 + 𝛽𝐴𝐴 log 𝑃𝐴 + 𝛽𝐴𝐵 log 𝑃𝐵 + 𝛽𝐴𝐶 log 𝑃𝐶 + 𝛽𝐴𝐷 log 𝑃 𝐷 + 𝜀 𝐴
Own price elasticity Cross price elasticities
Understand this intuitively

28. Lets go to data for some intuition
Price Elasticity & Segmentation for Progresso Soup
D3M

29. 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?

30. 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?

31. 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

32. Create a New Variable “Season”
Months of Oct to March as “High Season”
New DefinitionOld Definition

33. Control for Seasonality of Sales
Dependent variable: Log(Volume_Prog)
We continue to get incorrect sign for “Other” brand cross price elasticity

34. 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

35. 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.

36. 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

37. Multivariate Analysis
Segmenting Stores in Soup Case Study
D3M
This is the store demo file

38. Variables in Store Demo File

39. 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

40. 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

41. As usual, start by summarizing the data

42. Several of the
Demographic
variables are Highly
Correlated

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

44. Campbell does well in Midwest
& South
Progresso is strong in East,
followed by West

45. Segmentation of IRI Stores
D3M

46. 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”

47. Note the Difference between Cluster and PCA/Factor analysis
V1 V2 V3 V4 V5 V20…..
Cluster
Analysis
(Group Subjects)
Factor
Analysis
(Group Variables)
Data

48. Variable Reduction Techniques
You are working with columns here
We will look at 3 Techniques
 Principle Component Analysis
 Factor Analysis
 Cluster of Variables

49. 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)

50. 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

51. 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

52. 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.

53. 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.

54. 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)

55. Cluster Analysis
Segmentation of IRI Stores
D3M

56. 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. 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. 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. 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

60. 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

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

63. 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

64. Appendix
Quick Review of Pricing Strategies
 Quick Review of Pricing Strategies
 Market Segmentation
 Optimal Pricing

65. Cost-Plus Pricing
𝑃𝑟𝑖𝑐𝑒 = 𝐴𝐶 × 1 + markup
AC = Average cost of meeting a certain sales target
markup = Mark-up percentage

66. Competition-Based Pricing
𝑃𝑟𝑖𝑐𝑒 𝑢𝑠 = 𝑃𝑟𝑖𝑐𝑒𝑡ℎ𝑒𝑚 ± 𝜀
“Example: Follow the leader ”

67. Competition Manufacturer Distributor Retailer
Channel Pricing
Psychology and Pricing
Demand Measurement
Π(p) = p*q(p) – [FC + c*q(p)]

68. Market Segmentation & Pricing

69. What is the Difference between the two?
Airline Pricing
How do airlines
segment customers?

71. Amazon.com price histories

72. McDonald’s - WiFi charge:$0
The Plaza Hotel, NYC.
WiFi charge in $1000/night suite:
$14.95
Price of WiFi

73. Versioning: Product Line Sort
Menu of products at different price points with
different attributes:

74. 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

75. 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….”

76. Demand Measurement

77. Approaches: Regression Based Models
Surveys
Field Experiments
Historical Data
Conjoint Analysis

78. Psychology & Pricing
Pay-What-You-Want
Free
Psychology and Pricing

79. 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.

80. 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”

81. 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

82. Dropbox: An Example of “Freemium”

83. Psychology: Price Framing
$2.80/gallon, $0.10
discount if pay with
cash
$2.70/gallon, $0.10
surcharge if pay with
credit card

84. 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.

85. Price Optimization
p
Π
Π(p)
0
)(



p
p

86. 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

87. p
Π
Π(p)
p
Analytical solution: 0
)(



p
p
Deriving the optimal price

88. • Analytical solution:

1
1

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