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- 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?
- 16. 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
- 17. Building a Regression Model Price Elasticity & Segmentation for Progresso Soup D3M
- 18. 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
- 19. 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….
- 20. 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
- 21. 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.
- 22. 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
- 23. 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
- 24. 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)
- 25. 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
- 26. Lets go to data for some intuition Price Elasticity & Segmentation for Progresso Soup D3M
- 27. 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?
- 28. 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?
- 29. 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
- 30. Create a New Variable “Season” Months of Oct to March as “High Season” New DefinitionOld Definition
- 31. Control for Seasonality of Sales Dependent variable: Log(Volume_Prog) We continue to get incorrect sign for “Other” brand cross price elasticity
- 32. 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
- 33. 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.
- 34. 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
- 35. Multivariate Analysis Segmenting Stores in Soup Case Study D3M This is the store demo file
- 36. Variables in Store Demo File
- 37. 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
- 38. 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
- 39. As usual, start by summarizing the data
- 40. Several of the Demographic variables are Highly Correlated
- 41. Correlations of Market Shares Across 2000 Stores What can we learn about Progresso’s Competitors from just correlations?
- 42. Campbell does well in Midwest & South Progresso is strong in East, followed by West
- 43. Segmentation of IRI Stores D3M
- 44. 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”
- 45. Note the Difference between Cluster and PCA/Factor analysis V1 V2 V3 V4 V5 V20….. Cluster Analysis (Group Subjects) Factor Analysis (Group Variables) Data
- 46. Variable Reduction Techniques You are working with columns here We will look at 3 Techniques Principle Component Analysis Factor Analysis Cluster of Variables
- 47. 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)
- 48. 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
- 49. 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
- 50. 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.
- 51. 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.
- 52. 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)
- 53. Cluster Analysis Segmentation of IRI Stores D3M
- 54. 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
- 55. 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.
- 56. 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
- 57. 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
- 58. 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
- 59. Hierarchical Clustering of Stores Questions to Ask: Clustering based on what? How Many Segments?
- 60. 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
- 61. Appendix Quick Review of Pricing Strategies Quick Review of Pricing Strategies Market Segmentation Optimal Pricing
- 62. Cost-Plus Pricing 𝑃𝑟𝑖𝑐𝑒 = 𝐴𝐶 × 1 + markup AC = Average cost of meeting a certain sales target markup = Mark-up percentage
- 63. Competition-Based Pricing 𝑃𝑟𝑖𝑐𝑒 𝑢𝑠 = 𝑃𝑟𝑖𝑐𝑒𝑡ℎ𝑒𝑚 ± 𝜀 “Example: Follow the leader ”
- 64. Competition Manufacturer Distributor Retailer Channel Pricing Psychology and Pricing Demand Measurement Π(p) = p*q(p) – [FC + c*q(p)]
- 65. Market Segmentation & Pricing
- 66. What is the Difference between the two? Airline Pricing How do airlines segment customers?
- 67. Amazon.com price histories
- 68. McDonald’s - WiFi charge:$0 The Plaza Hotel, NYC. WiFi charge in $1000/night suite: $14.95 Price of WiFi
- 69. Versioning: Product Line Sort Menu of products at different price points with different attributes:
- 70. 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
- 71. 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….”
- 72. Demand Measurement
- 73. Approaches: Regression Based Models Surveys Field Experiments Historical Data Conjoint Analysis
- 74. Psychology & Pricing Pay-What-You-Want Free Psychology and Pricing
- 75. 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.
- 76. 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”
- 77. 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
- 78. Dropbox: An Example of “Freemium”
- 79. Psychology: Price Framing $2.80/gallon, $0.10 discount if pay with cash $2.70/gallon, $0.10 surcharge if pay with credit card
- 80. 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.
- 81. Price Optimization p Π Π(p) 0 )( p p
- 82. 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
- 83. p Π Π(p) p Analytical solution: 0 )( p p Deriving the optimal price
- 84. • 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

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