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WEMBA B, Causal Research, Conjoint Analysis Entitle Insurance. Market Intelligence Julie Edell Britton Session 7 September 25, 2009. Today’s Agenda. Announcements WEMBA A Causal Research – Experiments Pre-experimental Designs True Experiments Factorial Designs and Interaction Effects - PowerPoint PPT Presentation
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WEMBA B, Causal Research,Conjoint AnalysisEntitle Insurance
Market IntelligenceJulie Edell Britton
Session 7September 25, 2009
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Today’s Agenda•Announcements
•WEMBA A
•Causal Research – Experiments
•Pre-experimental Designs
•True Experiments
•Factorial Designs and Interaction Effects
•Conjoint Analysis
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Announcements
• Submit IBM Global Mobile Computing slides by 10 pm tonight!
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WEMBA (A): School Choice Model
ValuesPerceptionsIndividual 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.)
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The Funnel
Matriculate
Admitted
Opt Out
Apply
SelectedOut
Attend Information SessionDo not attend Information Session
Do not apply Do not apply
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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
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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?
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Today’s Agenda•Announcements
•WEMBA A
•Causal Research – Experiments
•Pre-experimental Designs
•True Experiments
•Entitle Case
•Factorial Designs and Interaction Effects
•Conjoint Analysis
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Causal Research - ValidityThe strength of our conclusions
i.e., Is what we conclude from our experiment correct?
Threats to Validity
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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
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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?
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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”
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Online Investor Performance
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-5
0
5
10
15
-36
-33
-30
-27
-24
-21
-18
-15
-12 -9 -6 -3 0 3 6 9 12 15 18 21 24 27 30 33 36
Event Month (0 = month of first online trade)
Cu
mu
lati
ve M
arke
t A
dju
sted
Ret
urn
(%
)
Gross Returns
Net Returns
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Portfolio Turnover
0%
20%
40%
60%
80%
100%
120%
140%
-24
-21
-18
-15
-12 -9 -6 -3 0 3 6 9 12 15 18 21 24
Event Month (0 is month of first online trade)
An
nu
aliz
ed
Tu
rno
ve
r (%
)
Size-Matched
Online
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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 Group X1 (Auto Shop Course) O1 (6 hrs online)
---------------------------------------------------------
Control Group X2 (Electronics Course) O2 (3 hrs online)
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2 Groups: Matched Control Group (True Experiment)
Experimental Group R X1 (Auto Shop Course) O1 (6 hrs) ---------------------------------------------------------
Control Group R X2 (Electronics Course) O2 (3 hrs)
Control for Selection Threat
Key Point: For causal research, chance (not respondent) must determine
respondent assignment to condition.
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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.
1.3 - 0.2 = 1.1 increase in number of packs per week.
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2-group Before-After Design
• Now add a randomly assigned “Control” group with mean scores O1 = 0.3, O2 = 0.5.
O1 O2 O2 - O1Difference
ExperimentalO1 X O2 0.2 1.3 1.1
ControlO1 O2
0.3 0.5 0.2
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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.
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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
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Oreo Promotion Experiment(Expenditures/customer/2 wks)
a1 = no ads a2 = ads R ow A ve
b1 = regu lar shelf
.60 .90 .75
b2 = end a isle
.65 .95 .80
C o l. A ve .625 .925
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Sales of Oreos on Promotion as function of Local Advertising, Display Location
Sales of Oreos with Ads and Display
0
0.2
0.4
0.6
0.8
1
1 2
No Ads Ads
$/C
ust
om
er/2
wee
ks o
n
Ore
as Regular Shelf
End Display
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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.
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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.
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Interactions and segmentation
c
Exposure, Attention, & PerceptionPsychology of Consumers
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Sales of Oreos on Promotion as function of Local Coupons, ay Location
Sales of Oreos with Coupon in Suburbs
and Urban Areas
0.00
0.20
0.40
0.60
0.80
1.00
No Coupon
$/C
ust
om
er/2
wee
ks
on
Ore
os
Suburbs
Urban
Coupon
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Analyzing Factorial Design in SPSS
1
5
10
AdtypeInformational Emotional Transformational
Exposures
n = 9 per cell
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SPSS Output
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Estimated Means
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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
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Today’s Agenda•Announcements
•WEMBA A
•Causal Research – Experiments
•Pre-experimental Designs
•True Experiments
•Factorial Designs and Interaction Effects
•Conjoint Analysis
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•Conjoint analysis: family of techniques to measure customer preferences, tradeoffs.
CONJOINT ANALYSIS
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Applications
•New product concept identification•Pricing•Benefit segmentation•Competitive analysis•Repositioning or modifying existing products
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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
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• 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
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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 tart u (a1) = -2Shrimp cocktail u (a2) = +2Salad u (b1) = +1Asparagus u (b2) = +4Grouper u (c1) = +4Sole u (c2) = +6
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We cannot observe these true utilities (the u’s) directly, but we can observe the overall ratings R(ijk)
a1 = Tart a1 = Tart a2 = Shrimp Cocktail
a2 = Shrimp Cocktail
b1 = salad
b2 = asparagus
b1 = salad
b2 = asparagus
c1 = grouper
-2 + 1 + 4 = 3
-2 + 4 + 4 = 6
+2 +1 +4 = 7
+2 +4 +4 = 10
c2 = sole
-2 +1 +6 = 5
-2 +4 +6 = 8
+2 + 1 + 6 = 9
+2 +4 + 6 = 12
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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..) = 5.5 B: R(.1.) = 6.0 C: R(..1) = 6.5R(2..) = 9.5 R(.2.) = 9.0 R(..2) = 8.5
1. 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.
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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 tart u (a1) = -2 R(1..) = 5.5Shrimp cocktail u (a2) = +2 R(2..) = 9.5Salad u (b1) = +1 R(.1.) = 6.0Asparagus u (b2) = +4 R(.2.) = 9.0Grouper u (c1) = +4 R(..1) = 6.5Sole u (c2) = +6 R(..2) = 8.5
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Tradeoffs
Which meal would this guy prefer?
Option 1 Option 2Shrimp Cocktail Mushroom TartSalad AsparagusGrouper Sole
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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:
Combo Customer RatingMushroom tart, salad, grouper 3Mushroom tart, asparagus, sole 8Shrimp cocktail, salad, sole 9Shrimp cocktail, asparagus, grouper 10
Guess the average evaluation of untested combinations?
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a1 = tart a1 = tart a2 = shrimp cocktail
a2 = shrimp cocktail
b1 = salad
b2 = asparagus
b1 = salad
b2 = asparagus
c1 = grouper
3 10
c2 = sole
8 9
Goal: Compute expected evaluation of remaining four combos so we can pick the best out of 8.
Overall Average? = 7.5 Deviation from 7.5?a1=Mushroom tart Average = 5.5a2=Shrimp cocktail Average = 9.5b1=Salad Average = 6.0b2=Asparagus Average = 9.0c1=Grouper Average = 6.5c2=Sole Average = 8.5
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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 = 5.5 5.5 – 7.5 = -2a2= Shrimp Average = 9.5 9.5 – 7.5 = +2b1=Salad Average = 6.0 6.0 – 7.5 = -1.5b2=Asparagus Avg = 9.0 9.0 – 7.5 = +1.5c1=Grouper Average= 6.5 6.5 – 7.5 = -1c2=Sole Average = 8.5 8.5 – 7.5 = +1Compute 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) = 7.5 + (-2) + (-1.5) + (+1) = 5
a1 = Mushroom
a1 = Mushroom
a2 = Shrimp a2 = Shrimp
b1 = salad
b2 = asparagus
b1 = salad
b2 = asparagus
c1 = grouper
3 7.5- 2+1.5– 1 = 6
7.5+ 2–1.5–1 = 7
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c2 = sole
7.5–2–1.5 + 1 = 5
8 9 7.5+2+1.5+1 = 12
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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?