Demand estimation & Pricing

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. Step1 in Model Building
Understand the Phenomenon
 Examine your objectives at a
broad/intuitive level
o Without thinking about data analysis
 What might be an “ideal” data/situation to
examine the issue
o Controlled experiments are often
difficult/costly/or at times impossible

3. Example: Marlboro in Early 90’s

4. o Early 1990’s
o Problem– losing market share for our flagship
brand Marlboro
o Question – Why is market share being lost
Situation
Chen, T, B. Sun, V. Singh, "Investigating Consumer Choice Dynamics Around Marlboro Friday", Marketing Science
1990: Philip Morris (PM)
and RJ Reynolds (RJR)
capturing almost 75% of
the market
By 1993, Marlboro’s share
dropped from 30% to 24%

5. 5
Overall slow economy, dramatic
growth in low tier brands and
generics
To maintain revenues, management
kept increasing prices for the
premium brands.
Discount brands were approx. $0.85 cheaper than Marlboro. For a
moderate smoker (consuming one pack a day), this amounts to
approximately $303 in annual saving by switching from Marlboro to a
discount brand.

6. Solution
o Solution: Reduce the
price gap
o Announcement:
Friday, Apr 2, 1993, the
day known as Marlboro
Friday
o The company's stock
tanked 26% following
the announcement,
losing about $10 billion
off its market cap in a
single day
6
The Day the Marlboro Man Fell
Off His Horse

7. Solution
 Several other major
household brands including
Heinz, Coca-Cola, Quaker
Oats, and P&G, collectively
lost $50 billion in value on
the same day
 Long Run: Some competitors
were priced out of the
market, and only two years
later, Philip Morris's stock
had fully recovered from
Marlboro Friday's loss
7
The Day the Marlboro Man Fell
Off His Horse

8. Key Event in Branding
8

9. Who smokes in America?
(see interactive visualization)

10. 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

11. 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

12. 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.

13. 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

14. 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

15. 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?

16. 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

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

18. Average Price & Volume

19. By Census Regions

20. Time Series of Sales

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

22. Average Price by Month
What do we infer?

24. 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

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

26. 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

27. 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….

28. 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 

29. 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.

30. 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 

31. 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

32. 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)

33. 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

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

35. 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?

36. 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?

37. 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

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

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

40. 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

41. 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.

42. 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

43. Price Optimization 101
p
Π
Π(p)
0
)(



p
p

44. 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

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

46. • 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