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Case Study
Pricing Strategy for Progresso Soup
Source: IRI Academic data
1900 supermarkets, 102 Chains across the
country ...
Learning Objectives
 Methodological Topics
 Developing Regression based Demand Models
 Understand elasticity, Controls ...
Current Situation
 You are recently hired by General Mills as a brand manager for one
of their key brands “Progresso”. Th...
Objective
Pricing Strategy for Progresso
 Using the data provided, evaluate the current pricing strategy
of Progresso. Do...
Understand the Scope of Data
 Data Source: IRI
 Sample: 2000+ supermarkets from 102 chains across the US
 Six years (20...
Approach
 Always start by summarizing the data
 Store Location & Demographics
 Marketing Mix (Shares/Price/Promotion)
...
Quick Examination of Store Demographics
 Lets Keep a few variables (State, Income, & Income
Quintiles) from Store demogra...
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 pr...
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
...
Regression Based Modeling
Fundamental Modeling Tool
Why do we (teach) use regressions?
 Determine whether the independent...
Log Models will Fit Data Better
Log-Log Model:
• The Price coefficient can be interpreted as :1
percent change in Price le...
Intuition for Log Models: Click on the link below. It takes you to GAPMINDER,
where you can see relationship between diffe...
Semi-log specification
For the semi-log model:
• Now Price is measured in regular units
and Sales in log.
– The coefficien...
Elasticities from Regression
Linear Model
SALES
PRICE
ae
PRICEaaSALES


1
10
PRICEae
PRICEaaSALES


1
10ln
1
10 ln...
Why do we care about price elasticity?
 How do you price a product?
o What factors must we consider in determining what p...
Why Care About Elasticity?
Cross Price Elasticity is one of the best measure to understand Competition
Log-log regression ...
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 ...
Log-log Model With Competitive Prices
Dependent variable: Log(Volume_Prog)
What brand competes most closely with Progresso...
Question
 What would happen to Progresso sales if
 Progresso cuts its price by 10%?
 Campbell/Other/PL cut price by 10%...
Create a New Variable “Season”
Months of Oct to March as “High Season”
New DefinitionOld Definition
Control for Seasonality of Sales
Dependent variable: Log(Volume_Prog)
We continue to get incorrect sign for “Other” brand ...
Control for Regional Differences
Regional control seem important in
our context:
1) Fit has improved
2) Elasticity estimat...
Regressions by Census Region
Note: Seasonality controls not shown
East Coast Midwest
South West Coast
What can we say abou...
Discussion
 Analyze the competitive position of Progresso across Census
Regions based on own & cross-price elasticity
 W...
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 elast...
Approach
 Questions you should ask
 Segmentation based on what??
 How many segments??
 Always start by summarizing var...
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, & Cl...
Note the Difference between Cluster and PCA/Factor analysis
V1 V2 V3 V4 V5 V20…..
Cluster
Analysis
(Group Subjects)
Factor...
Variable Reduction Techniques
You are working with columns here
We will look at 3 Techniques
 Principle Component Analysi...
PCA/Factor Analysis
 Our demographic variables are highly correlated
 If we were to use these in a Regression model for ...
If we use 3 components, we capture approximately 84% of information
contained in the 10 demographics
Eigenvalues of a matr...
Cluster of Variable Algorithm
We can use Median
Income, % Kids 18,
and % Black. These
3 variables will be
representative o...
Look for large positive or negative numbers for
each factor. See the corresponding variable
names to interpret the underly...
What do these techniques do?
 Take a large number of variables
that are highly correlated & create
new variables
 New va...
Examine the Factor Scores
The new variables (Factors) have a mean of 0 and Std of 1.
They are orthogonal to each other (ze...
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...
57
Cluster Analysis
Cluster analysis is a technique used
to identify groups of ‘similar’
customers in a market (i.e., mark...
58
General question: how to organize observed
data into meaningful structures
• Examples:
o In food stores items of simila...
59
Cluster Analysis
Cluster analysis works on the principle of maximizing the between-
cluster variance while minimizing t...
Clustering Methods
 Hierarchical clustering is an iterative process that starts with
each observation in its own cluster....
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 thin...
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
𝑃𝑟𝑖𝑐𝑒 𝑢𝑠 = 𝑃𝑟𝑖𝑐𝑒𝑡ℎ𝑒𝑚 ± 𝜀
“Example: Follow the leader ”
Competition Manufacturer Distributor Retailer
Channel Pricing
Psychology and Pricing
Demand Measurement
Π(p) = p*q(p) – [F...
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
custom...
Price Customization: Legality
B2B:
 Robinson-Patman Act of
1936:
“It shall be unlawful for any person
engaged 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...
Google’s Max Strategy
Free is the fastest way to maximize market
share and enable mass adoption.
“Take whatever it is you ...
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% c...
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 c...
Price?: “It’s up to you. It’s
really up to you.”
Success? Probably
Estimate: 1M downloads with 40% paying something.
Prici...
Price Optimization
p
Π
Π(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 s...
p
Π
Π(p)
p
Analytical solution: 0
)(



p
p
Deriving the optimal price
• Analytical solution:

1
1

c
p
β is the own
price elasticity
We can obtain β from the log-log sales response model!
O...
Pricing Strategies for Brands
Pricing Strategies for Brands
Pricing Strategies for Brands
Pricing Strategies for Brands
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Pricing Strategies for Brands

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This case study utilizes a large database (2000 stores, 6-years of scanner data) to study pricing strategies for brands. Methods include Advanced regression, PCA and Clustering algorithms.

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Pricing Strategies for Brands

  1. 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. 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. 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. 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. 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. 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. 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. 8. Variables in Transaction Data Understand the variables. Why is “date” messed up?
  9. 9. Average Price & Volume
  10. 10. By Census Regions
  11. 11. Time Series of Sales
  12. 12. Category Sales by Month Extract “month” from Date
  13. 13. Average Price by Month What do we infer?
  14. 14. Market Shares by Month for Progresso Relate this to Pricing Strategy
  15. 15. What about our competitor?
  16. 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. 17. Building a Regression Model Price Elasticity & Segmentation for Progresso Soup D3M
  18. 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. 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. 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. 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. 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. 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. 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. 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. 26. Lets go to data for some intuition Price Elasticity & Segmentation for Progresso Soup D3M
  27. 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. 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. 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. 30. Create a New Variable “Season” Months of Oct to March as “High Season” New DefinitionOld Definition
  31. 31. Control for Seasonality of Sales Dependent variable: Log(Volume_Prog) We continue to get incorrect sign for “Other” brand cross price elasticity
  32. 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. 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. 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. 35. Multivariate Analysis Segmenting Stores in Soup Case Study D3M This is the store demo file
  36. 36. Variables in Store Demo File
  37. 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. 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. 39. As usual, start by summarizing the data
  40. 40. Several of the Demographic variables are Highly Correlated
  41. 41. Correlations of Market Shares Across 2000 Stores What can we learn about Progresso’s Competitors from just correlations?
  42. 42. Campbell does well in Midwest & South Progresso is strong in East, followed by West
  43. 43. Segmentation of IRI Stores D3M
  44. 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. 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. 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. 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. 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. 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. 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. 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. 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. 53. Cluster Analysis Segmentation of IRI Stores D3M
  54. 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. 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. 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. 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. 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. 59. Hierarchical Clustering of Stores Questions to Ask: Clustering based on what? How Many Segments?
  60. 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. 61. Appendix Quick Review of Pricing Strategies  Quick Review of Pricing Strategies  Market Segmentation  Optimal Pricing
  62. 62. Cost-Plus Pricing 𝑃𝑟𝑖𝑐𝑒 = 𝐴𝐶 × 1 + markup AC = Average cost of meeting a certain sales target markup = Mark-up percentage
  63. 63. Competition-Based Pricing 𝑃𝑟𝑖𝑐𝑒 𝑢𝑠 = 𝑃𝑟𝑖𝑐𝑒𝑡ℎ𝑒𝑚 ± 𝜀 “Example: Follow the leader ”
  64. 64. Competition Manufacturer Distributor Retailer Channel Pricing Psychology and Pricing Demand Measurement Π(p) = p*q(p) – [FC + c*q(p)]
  65. 65. Market Segmentation & Pricing
  66. 66. What is the Difference between the two? Airline Pricing How do airlines segment customers?
  67. 67. Amazon.com price histories
  68. 68. McDonald’s - WiFi charge:$0 The Plaza Hotel, NYC. WiFi charge in $1000/night suite: $14.95 Price of WiFi
  69. 69. Versioning: Product Line Sort Menu of products at different price points with different attributes:
  70. 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. 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. 72. Demand Measurement
  73. 73. Approaches: Regression Based Models Surveys Field Experiments Historical Data Conjoint Analysis
  74. 74. Psychology & Pricing Pay-What-You-Want Free Psychology and Pricing
  75. 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. 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. 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. 78. Dropbox: An Example of “Freemium”
  79. 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. 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. 81. Price Optimization p Π Π(p) 0 )(    p p
  82. 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. 83. p Π Π(p) p Analytical solution: 0 )(    p p Deriving the optimal price
  84. 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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