2 Today’s Agenda Announcements WEMBA A Causal Research – Experiments Pre-experimental Designs True Experiments Factorial Designs and Interaction Effects Conjoint Analysis
3 Announcements Submit IBM Global Mobile Computing slides by 10 pm tonight!
4 4 WEMBA (A): School Choice Model Values Perceptions Individual Differences & Constraints Become a Duke MBA Assumes that behavior is driven by differences in: Values (Importance of key attributes) Perceptions (Duke and Competition on key attributes) Individual Differences & Constraints (travel, cost, etc.)
5 The Funnel Matriculate Admitted Opt Out Apply Selected Out Attend Information Session Do not attend Information Session Do not apply
6 6 The Analysis Approach Sample groups that differ in behavior Compare the groups on relevant dimensions: Perceptions Values Individual Difference & Constraints Infer that any difference found between groups are partly responsible for differences in behavior
7 7 WEMBA B What factors drive application? Perception of Duke – Perception of Comp Individual difference measures (demos, % paid by company, etc.) Conditional on applying, what drives acceptance? How do info sessions alter perceptions of Duke? Who should Nagy target, and how can he reach target? What perceptions might Nagy try to alter with info sessions?
8 Today’s Agenda Announcements WEMBA A Causal Research – Experiments Pre-experimental Designs True Experiments Entitle Case Factorial Designs and Interaction Effects Conjoint Analysis
9 Causal Research - Validity The strength of our conclusions i.e., Is what we conclude from our experiment correct? Threats to Validity 9 History: an event occurring around same time as treatment that has nothing to do with treatment Maturation: people change pre to post Testing: pretest causes change in response Instrumentation: measures changed meaning Statistical Regression: Original measure was due to a random peak or valley
10 Online Investor Performance X = brick and mortar brokerage customer moves online to trade in 1999 O = Annualized turnover 1998 – 40% annualized turnover 2000 – 100% annualized turnover Did going online cause people to trade more actively? Threats with one-group pre-post?
11 Quasi-Experimental Designs: Interrupted Time Series Same as one-group pretest posttest, but observations at many points in time before and after key treatment for same people: EG O1 O2 O3 X O4 O5 O6 Extra time periods help control for history, maturation, testing. “Quasi-experiment”
12 Online Investor Performance
13 Portfolio Turnover
14 2 Groups: Unmatched Control Group (Effect of Prior Knowledge on Search) Hypothesis: People with little knowledge about cars search less online 100 Durham residents who are in the market for a car Experimental GroupX1 (Auto Shop Course) O 1 (6 hrs online) Control Group X2 (Electronics Course) O 2 (3 hrs online)
15 2 Groups: Matched Control Group (True Experiment) Experimental Group R X1 (Auto Shop Course) O 1 (6 hrs) Control Group R X2 (Electronics Course) O 2 (3 hrs) Control for Selection Threat Key Point: For causal research, chance (not respondent) must determine respondent assignment to condition.
16 Breckenridge Brewery Ads Breckenridge Brewery wants to assess the efficacy of TV ad spots for its new amber ale. Time 1 (O1): Duke undergrads are brought to the lab and asked to rate their frequency of buying a series of brands in various categories over the past week. The list includes Breckenridge Amber Ale. Mean = 0.2 packs per week. Time 2 (X): Two weeks of ads for Breckenridge Ale. Time 3 (O2): Same Duke undergrads brought back to lab to rate frequency of buying same set of brands over past week. Mean = 1.3 packs per week. = 1.1 increase in number of packs per week.
17 2-group Before-After Design Now add a randomly assigned “Control” group with mean scores O1 = 0.3, O2 = 0.5.
18 Factorial Designs Independent Variable: Factor manipulated by the researcher Dependent Variable: Effect or response measured by researcher Factorial Design: 2 or more independent variables, each with two or more levels. All possible combinations of levels of A & levels of B.
19 Oreo Promotion Experiment Kroger: Supporting a discount on Oreo cookies Factor A: Ads in local paper a1 = no ads a2 = ad in Thursday local paper Factor B: Display location b1 = regular shelf b2 = end aisle
21 Sales of Oreos on Promotion as function of Local Advertising, Display Location
22 Oreo Example, No Interaction Main Effect of A (Ads)? Main Effect of B (Display Location)? No AxB (say A by B) interaction. Effect of changing A (Ads) is independent of level of B (Display Location). Sales go up by $0.30 when you advertise, regardless of location. Implies that Ad & Display decisions can be decoupled…they influence sales additively.
23 Managerial Implications of Interactions If two controllable marketing decision variables interact (e.g., advertising x display), implication is that you can’t decouple decisions; must coordinate. If A is a controllable decision variable and B is a potential segmentation variable (e.g., ads x urban/suburban), interaction means that segments respond differently to this lever.
24 Interactions and segmentation c Exposure, Attention, & PerceptionPsychology of Consumers
25 Sales of Oreos on Promotion as function of Local Coupons, ay Location
26 Analyzing Factorial Design in SPSS Adtype InformationalEmotionalTransformational Exposures n = 9 per cell
32 SPSS Output
33 Estimated Means
35 Takeaways for Causal Research Threats to validity in pre-experimental and quasi- experimental designs Factorial Designs – Main effects and interactions 2 marketing tactics interact coordinate Marketing tactic interacts with customer classification implies classification a potential basis for segmentation…different sensitivities to some marketing mix variable
36 Today’s Agenda Announcements WEMBA A Causal Research – Experiments Pre-experimental Designs True Experiments Factorial Designs and Interaction Effects Conjoint Analysis
37 Conjoint analysis: family of techniques to measure customer preferences, tradeoffs. CONJOINT ANALYSIS
38 Applications New product concept identification Pricing Benefit segmentation Competitive analysis Repositioning or modifying existing products
39 Modeling a Single Consumer Sysco wants to create first class lunch defined on: Appetizer a1 = Mushroom tart a2 = Shrimp cocktail Salad/Vegetable b1 = Tossed salad b2 = Fresh asparagus Entree c1 = Fried grouper c2 = Sole bonne femme
40 Goal Find the combination of appetizer, salad/veggie, and entree that will be most attractive to customers who are buyers at major airlines Procedure Customer evaluates subset of combos (15-pt scale) Estimate “average liking” item effects Forecast liking of all combos Design optimal meal for that customer Goal and Procedure
41 Imagine a customer who obeys an additive model: Overall Liking (ijk) = u a(i) + u b(j) + u c(k) = for Whole Meal Utility / liking for Appetizer (i) + Utility / liking for Salad/Veg (j) + Utility / liking for Entrée (k) And further, suppose: Mushroom tartu (a1) = -2 Shrimp cocktailu (a2) = +2 Saladu (b1) = +1 Asparagusu (b2) = +4 Grouperu (c1) = +4 Soleu (c2) = +6
42 We cannot observe these true utilities (the u’s) directly, but we can observe the overall ratings R(ijk)
43 Notice there is no interaction of preferences across attributes. When this holds, we can get a separate interval scale of “part-utility” from the marginal means for each factor: a + b (part Util) A:R(1..) = 5.5B: R(.1.) = 6.0C: R(..1) = 6.5 R(2..) = 9.5 R(.2.) = 9.0 R(..2) = Because these share a common unit, differences between two levels of factor A can be compared meaningfully to differences between two levels of B and C. Appetizer factor A twice as important as entrée factor C. 2.Because these scales have different and unknown intercepts, we cannot compare the absolute level of one level of factor A to that of a single level of factor B or C. e.g., Though R(2..)= 9.5 for shrimp > R(..2) = 8.5 for sole, u(a2) = +2 for shrimp < u(c2) = +6 for sole.
44 Imagine a customer who obeys an additive model: Overall Liking (ijk) = u a(i) + u b(j) + u c(k) = for Whole Meal Utility / liking for Appetizer (i) + Utility / liking for Salad/Veg (j) + Utility / liking for Entrée (k) And further, suppose: Mushroom tartu (a1) = -2R(1..) = 5.5 Shrimp cocktailu (a2) = +2R(2..) = 9.5 Saladu (b1) = +1R(.1.) = 6.0 Asparagusu (b2) = +4R(.2.) = 9.0 Grouperu (c1) = +4R(..1) = 6.5 Soleu (c2) = +6R(..2) = 8.5
45 Tradeoffs Which meal would this guy prefer? Option 1Option 2 Shrimp CocktailMushroom Tart SaladAsparagus GrouperSole
46 Same Conclusions from Subset Critically, we can get the same utility scales if we ask only for a specially chosen subset of all 8 possible combinations: ComboCustomer Rating Mushroom tart, salad, grouper 3 Mushroom tart, asparagus, sole 8 Shrimp cocktail, salad, sole 9 Shrimp cocktail, asparagus, grouper10 Guess the average evaluation of untested combinations?
47 Goal: Compute expected evaluation of remaining four combos so we can pick the best out of 8. Overall Average? = 7.5Deviation from 7.5? a1=Mushroom tart Average = 5.5 a2=Shrimp cocktail Average =9.5 b1=Salad Average = 6.0 b2=Asparagus Average = 9.0 c1=Grouper Average =6.5 c2=Sole Average =8.5
48 Now let’s consider how much of a bump up or down we get from the overall average (7.5) for each attribute level. Overall Average? = 7.5 Deviation from 7.5? a1= Mush. Tart Avg = – 7.5 = -2 a2= Shrimp Average = – 7.5 = +2 b1=Salad Average = – 7.5 = -1.5 b2=Asparagus Avg = – 7.5 = +1.5 c1=Grouper Average= – 7.5 = -1 c2=Sole Average = – 7.5 = +1 Compute predicted rating of missing cells by saying: Overall Average + Dev a(i) + Dev b(j) + Dev c(k) e.g., Tart (a1), Salad (b1), Sole (c2) = (-2) + (-1.5) + (+1) = 5
49 a.Best meal? b.If you now sell a1, b1, c1, what single change is best? What if you sell a2, b1, c1? c.Most important attribute? d.Can also cluster individual customers based on their part-utility differences for each attribute to get “benefit segments.” e.Can make market share forecasts (next) f.Can use for pricing, when price is an attribute What can we conclude?