1. N. Kumar, Asst. Professor of Marketing Database Marketing Factor Analysis

2. N. Kumar, Asst. Professor of Marketing Web Advertising Objective: Identify the profile of customers who visit your website Important information for advertisers who may wish to use your advertising services

3. N. Kumar, Asst. Professor of Marketing Repositioning your Web Site You may wish to learn of features that consumers value when browsing thro’ websites Analysis of consumer data may help uncover different facets (dimensions) of customers’ preferences Can make a perceptual map to help form the basis of your strategy

4. N. Kumar, Asst. Professor of Marketing How can Factor Analysis Help? Often Factor Analysis can help summarize the information in many variables into a few underlying constructs/dimensions Reduces the number of variables that you have to deal with little loss of information

5. N. Kumar, Asst. Professor of Marketing Why Reduce Data? Census Bureau – each zip code has more than 200 pieces of information Typical customer survey on attitudes, lifestyles, opinions will probably have responses to more than 100 questions

6. N. Kumar, Asst. Professor of Marketing Why Reduce Data … contd. Too much data can be hard to absorb and comprehend Difficult to work with too much data Even if you can get it to work results will be distorted (multicollinearity problem) – regression example

7. N. Kumar, Asst. Professor of Marketing What is Factor Analysis? Factor analysis is a MV technique which analyzes the structure of the interrelationships among a large number of variables Can identify the separate dimensions of the structure and can also determine the extent to which each variable is explained by each dimension

8. N. Kumar, Asst. Professor of Marketing Factor Analysis: Intuitive Description Factor Analysis summarizes information in Data by reducing original set of “items”/attributes to a smaller set of “factors”/“dimensions”/“constructs” A Factor can be viewed as an “Index”: Dow Jones Index -- summarizes the movement of stock market Consumer Price Index -- reflects prices of consumer products and indicator of inflation How to create such an “index” that appropriately summarizes the data with the minimum loss of information?

9. N. Kumar, Asst. Professor of Marketing Factor Analysis: Intuitive Description (cont.) How does Factor Analysis work? Factor Analysis “constructs” factors/axes by including original attributes with different weights If the responses are rated almost identically for an attribute, Factor Analysis gives much lower weight If two attributes, say attributes #3 and #4, are highly correlated i.e. stores which rate highly on attribute #3 are also rated high on #4, Factor Analysis treats #3 and #4 as measurements of the same underlying construct

10. N. Kumar, Asst. Professor of Marketing Factor Analysis: e-admission Data: Students’ scores on different subjects – say Physics, Chemistry, Math, History, English and French Task at hand: to make an assessment about the student’s ability to succeed in school given these scores Do we need to look at the scores on all subjects or can we use a simplified heuristic?

11. N. Kumar, Asst. Professor of Marketing Single Factor Model Suppose we could get something like this: M = 0.8 I + A m P = 0.7 I + A p C = 0.9 I + A c E = 0.6 I + A e H = 0.5 I + A h F = 0.65 I + A f A’s denote aptitude specific to the subject

12. N. Kumar, Asst. Professor of Marketing Factor Analysis vs. Regression Regression Have data on I Objective is to work out the weight on I Factor Analysis I is the underlying construct that we are trying to work out

13. N. Kumar, Asst. Professor of Marketing Some Terminology Communality – that which is common with the variable and the underlying factor. Formally, the square of the pattern loading Unique/Specific Variance – that which is unexplained by the factor(s)

14. N. Kumar, Asst. Professor of Marketing Input: Correlations MPCEHF M1 P0.561 C0.720.631 E0.480.420.541 H0.400.350.450.301 F0.520.460.590.390.331

15. N. Kumar, Asst. Professor of Marketing Results VariableCommunalityUnique Variance Pattern Loading M0.6400.3600.8 P0.4900.5100.7 C0.8100.1900.9 E0.3600.6400.6 H0.2500.7500.5 F0.4230.5770.65 Total2.9733.027

16. N. Kumar, Asst. Professor of Marketing Two-Factor Model Suppose we could get something like this: M = 0.8 Q + 0.2 V +A m P = 0.7 Q + 0.3 V + A p C = 0.6 Q + 0.3 V +A c E = 0.2 Q + 0.8 V + A e H = 0.15 Q + 0.82 V +A h F = 0.25 Q + 0.85 V + A f A’s denote aptitude specific to the subject

17. N. Kumar, Asst. Professor of Marketing Results: VariableQVUnique Variance QV M0.6400.0400.3210.8000.200 P0.4900.0900.4200.7000.300 C0.3600.0900.5510.6000.300 E0.0400.6400.3210.2000.800 H0.0230.6720.3040.1500.820 F0.0630.7230.2150.2500.850 Total1.6162.2552.132

18. N. Kumar, Asst. Professor of Marketing Results: 2 VariableQVUnique Variance QV M0.4450.2340.3210.667-0.484 P0.4620.1180.4200.680-0.343 C0.3780.0710.5510.615-0.267 E0.5490.1300.3210.7410.361 H0.5260.1700.3040.7250.412 F0.6590.1260.2150.8120.355 Total3.0190.8492.132

19. N. Kumar, Asst. Professor of Marketing Factor Analysis: Basic Concepts Each original item (variable) is expressed as a linear combination of the underlying factors X1X1 X4X4 X2X2 X3X3 Original Items F1F1 F2F2 Underlying Factors

20. N. Kumar, Asst. Professor of Marketing Factor Analysis: Basic Concepts (cont.) Each Factor can be expressed as a linear combination of the original items (variables) X1X1 X4X4 X2X2 X3X3 Original Items F1F1 F2F2 Underlying Factors

21. N. Kumar, Asst. Professor of Marketing Factor Analysis: Basic Concepts (cont.) Mathematical Model Common Factors, F 1, …, F M, can be expressed as linear combinations of the original variables, X 1, …, X N F 1 = r 11 X 1 + r 12 X 2 + … + r 1N X N …………………………………………….. F M = r M1 X 1 + r M2 X 2 + … + r MN X N r ij = factor loading coefficient of the ith variable on the jth factor

22. N. Kumar, Asst. Professor of Marketing Key Words Factor Loading: Correlation of a factor with the original variable. Communality: Variance of a variable summarized by the underlying factors Eigenvalue (latent root): Sum of squares of loadings of each factor – just a measure of variance e.g. the eigenvalue of factor 1, 1, 1 = r 11 2 + r 12 2 + … + r 1M 2 Factor Analysis: Basic Concepts (cont.)

23. N. Kumar, Asst. Professor of Marketing Factor Analysis: Basic Concepts (cont.) What does a Factor Analysis program do? finds the factor loadings, r i1, r i2, …, r iN, for each of the underlying factors, F 1, …, F M, to “best explain” the pattern of interdependence among the original variables, X 1, …, X N How are Factor Loadings determined? select the factor loadings, r 11, r 12, …, r 1N, for the first factor so that Factor 1 “explains” the largest portion of the total variance select the factor loadings, r 21, r 22, …, r 2N, for the second factor so that Factor 2 “explains” the largest portion of the “residual” variance, subject to Factor 2 being orthogonal to Factor 1 so on...

24. N. Kumar, Asst. Professor of Marketing How many Factors do you Choose? Look at the Eigen Values of the Factors If K of P factors have an eigen value > 1 then K factors will do a pretty good job Scree plot helpful

25. N. Kumar, Asst. Professor of Marketing Scree Plot: Selection of # of Factors 654321654321 2 4 6 8 10 “elbow”

26. N. Kumar, Asst. Professor of Marketing Factor Analysis: Geometric Interpretation Error F1F1 F2F2 x1x1

27. N. Kumar, Asst. Professor of Marketing Illustrative Example: Measurement of Department Store Image Description of the Research Study: To compare the images of 5 department stores in Chicago area -- Marshal Fields, Lord & Taylor, J.C. Penny, T.J. Maxx and Filene’s Basement Focus Group studies revealed several words used by respondents to describe a department store e.g. spacious/cluttered, convenient, decor, etc. Survey questionnaire used to rate the department stores using 7 point scale

28. N. Kumar, Asst. Professor of Marketing Portion of Items Used to Measure Department Store Image

29. N. Kumar, Asst. Professor of Marketing Department Store Image Measurement: Input Data Store 1 Store 2 Store 3 Store 4 Store 5 Attribute 1… Attribute 10 Respondents … … …

30. N. Kumar, Asst. Professor of Marketing Pair-wise Correlations among the Items Used to Measure Department Store Image X1X1 X2X2 X3X3 X4X4 X5X5 X6X6 X7X7 X8X8 X9X9 X 10 X 1 1.00 0.79 0.41 0.26 0.12 0.89 0.87 0.37 0.32 0.18 X 2 1.00 0.32 0.21 0.20 0.90 0.83 0.31 0.35 0.23 X 3 1.00 0.80 0.76 0.34 0.40 0.82 0.78 0.72 X 4 1.00 0.75 0.30 0.28 0.78 0.81 0.80 X 5 1.00 0.11 0.23 0.74 0.77 0.83 X 6 1.00 0.78 0.30 0.39 0.16 X 7 1.00 0.29 0.26 0.17 X 8 1.00 0.82 0.78 X 9 1.00 0.77 X 10 1.00

31. N. Kumar, Asst. Professor of Marketing Principal Components Analysis for the Department Store Image Data : Variance Explained by Each Factor Factor Variance (Latent Root) Explained Factor 1 5.725 Factor 2 2.761 Factor 3 0.366 Factor 4 0.357 Factor 5 0.243 Factor 6 0.212 Factor 7 0.132 Factor 8 0.123 Factor 9 0.079 Factor 10 0.001

32. N. Kumar, Asst. Professor of Marketing Scree Plot: Selection of # of Factors 654321654321 2 4 6 8 10 “elbow”

33. N. Kumar, Asst. Professor of Marketing Unrotated Factor Loading Matrix for Department Store Image Data Using Two Factors

34. N. Kumar, Asst. Professor of Marketing Factor Loading Matrix for Department Store Image Data after Rotation of the Two Using Varimax

35. N. Kumar, Asst. Professor of Marketing Procedure for Conducting a Factor Analysis Data Collection Step 1 Run Factor Analysis Step 2 Determine the Number of Factors Step 3

36. N. Kumar, Asst. Professor of Marketing Rotate Factors Step 4 Interpret Factors Step 5 Calculate Factor Score Step 6 Do Other Stuff Procedure for Conducting a Factor Analysis Step 7

37. N. Kumar, Asst. Professor of Marketing Product Differentiation & Positioning Strategy Product Differentiation: creation of tangible or intangible differences on one or two key dimensions between a brand/product and its main competitors Example: Toyota Corolla and Chevy Prizm are physically nearly identical cars and yet the Corolla is perceived to be superior to the Prizm Product Positioning: set of strategies that firms develop and implement to ensure that these perceptual differences occupy a distinct and important position in customers’ minds Example: KFC differentiates its chicken meal by using its unique blend of spices and cooking processes

38. N. Kumar, Asst. Professor of Marketing Product Positioning & Perceptual Maps Information Needed for Positioning Strategy: Understanding of the dimensions along which target customers perceive brands in a category and how these customers perceive our offering relative to competition How do our customers (current or potential) view our brand? Which brands do those customers perceive to be our closest competitors? What product and company attributes seem to be most responsible for these perceived differences?  Competitive Market Structure  Assessment of how well or poorly our offerings are positioned in the market

39. N. Kumar, Asst. Professor of Marketing Product Positioning & Perceptual Maps (cont.) Managerial Decisions & Action: Critical elements of a differential strategy/action plan What should we do to get our target customer segment(s) to perceive our offering as different? Based on customer perceptions, which target segment(s) are most attractive? How should we position our new product with respect to our existing products? What product name is most closely associated with attributes our target segment perceives to be desirable  Perceptual Map facilitate differentiation & positioning decisions

40. N. Kumar, Asst. Professor of Marketing Application Summary: Data Reduction Identifying underlying dimensions, or FACTORS, that explain the correlation among a set of variables e.g. a set of lifestyle statements may be used to measure the psychographic profiles of consumers Statement 1 ……………. Statement N Life-style Statements M < N Psychographic Factors Psychographic Profiles

41. N. Kumar, Asst. Professor of Marketing Understanding customer preferences What dimensions to differentiate on to be successful – implications for repositioning or introduction strategy Application Summary: Product Positioning/Introduction

42. N. Kumar, Asst. Professor of Marketing Web Advertising Understanding the profile of customers Conduct a survey Analyze the data – extract the factors Interpret the factors – score the customers Can even draw a perceptual map of customers in the factor space

43. N. Kumar, Asst. Professor of Marketing Repositioning your Web Site To learn of features that consumers value when browsing thro’ websites – conduct a survey Factor analyze the data to uncover the underlying factors that influence customers’ preferences – interpret the factors How score on these dimensions relative to your competition - perceptual map to help form the basis of your strategy