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Market Intelligence Session 7 Experimental Research.
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Transcript of Market Intelligence Session 7 Experimental Research.
Market Intelligence Session 7
Experimental Research
Experiments
• Only way to test causal hypotheses
• Independent Variable = hypothesized cause– Usually manipulated by the researcher/manager – Example: Send a color or black and white brochure
• Dependent Variable = effect– Measured (observed) by researcher/manager– Example: New accounts secured
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3
3 key features of true experiments
1. Manipulation of a variable2. Control/comparison group3. Random assignment to groups
4
Can’t always run true experiment
• Sometimes can’t manipulate (or ethically manipulate) variable of interest (smoking)
• Sometimes can’t get a comparison group beforehand (all people affected by event)
• Can’t always randomly assign people to groups (which class people take)
5
Can’t always run true experiment
• Sometimes can’t manipulate (or ethically manipulate) variable of interest
• Sometimes can’t get a comparison group beforehand
• Can’t always randomly assign people to groups
• Solution: Correlational or Quasi-experimental designs– Goal: get as close to true experiment as possible
6
Types of designs
• Correlational (cross-lag panel)• Quasi-experimental
– One group posttest only– One group pretest-posttest– Nonequivalent control posttest only– Nonequivalent control pretest-posttest– Time series– Non-equivalent control time series
• True experiments– Posttest equivalent groups– Pretest-posttest equivalent groups
Notation (used across social sciences)
• X = Manipulation – No X means that group did not receive manipulation
• R = Random assignment to different experimental groups or conditions
• On = Observation of DV at Time N
– O1 = before manipulation
– O2 = after manipulation
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8
Types of designs
• Correlational (cross-lag panel)• Quasi-experimental
– One group posttest only– One group pretest-posttest– Nonequivalent control posttest only– Nonequivalent control pretest-posttest– Interrupted time series– Non-equivalent control time series
• True experiments– Posttest equivalent groups– Pretest-posttest equivalent groups
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Correlational designs
• Examine correlations between 2 variables• Most common: Cross lag panels
– Examine correlations between 2 variables at 2 time points
– Purpose: to see if evidence supports 1 causal direction more than the other
– Notation: O1 O2
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Key: Look at diagonal correlations
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Example: Lefkowitz et al. (1972)
.21
.01
.38
.05
.31
.-.05
10 years
TV Violence TV Violence
Aggression Aggression
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Example: Lefkowitz et al. (1972)
.21
.38
.05
-.05
10 years
TV Violence TV Violence
Aggression Aggression
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Example: Lefkowitz et al. (1972)
.21
.01
.38
.05
.31
-.05
10 years
TV Violence TV Violence
Aggression Aggression
14
Types of designs
• Correlational (cross-lag panel)• Quasi-experimental
– One group posttest only– One group pretest-posttest– Nonequivalent control posttest only– Nonequivalent control pretest-posttest– Interrupted time series– Non-equivalent control time series
• True experiments– Posttest equivalent groups– Pretest-posttest equivalent groups
15
One group posttest onlyX O2
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Types of designs
• Correlational (cross-lag panel)• Quasi-experimental
– One group posttest only– One group pretest-posttest– Nonequivalent control posttest only– Nonequivalent control pretest-posttest– Interrupted time series– Non-equivalent control time series
• True experiments– Posttest equivalent groups– Pretest-posttest equivalent groups
17
One group pretest-posttest O1 X O2
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Types of designs
• Correlational (cross-lag panel)• Quasi-experimental
– One group posttest only– One group pretest-posttest– Nonequivalent control posttest only– Nonequivalent control pretest-posttest– Interrupted time series– Non-equivalent control time series
• True experiments– Posttest equivalent groups– Pretest-posttest equivalent groups
19
Nonequivalent control posttest onlyX O2
O2
20
Types of designs
• Correlational (cross-lag panel)• Quasi-experimental
– One group posttest only– One group pretest-posttest– Nonequivalent control posttest only– Nonequivalent control pretest-posttest– Interrupted time series– Non-equivalent control time series
• True experiments– Posttest equivalent groups– Pretest-posttest equivalent groups
21
Nonequivalent control pretest-posttestO1 X O2
O1 O2
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Types of designs
• Correlational (cross-lag panel)• Quasi-experimental
– One group posttest only– One group pretest-posttest– Nonequivalent control posttest only– Nonequivalent control pretest-posttest– Interrupted time series– Non-equivalent control time series
• True experiments– Posttest equivalent groups– Pretest-posttest equivalent groups
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Interrupted time seriesO1 O1 O1 X O2 O2 O2
(no)
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Removal of treatment
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Nonequivalent control time seriesO1 O1 O1 X O2 O2 O2
O1 O1 O1 O2 O2 O2
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Adding control condition
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Threats to internal validity
• Selection bias: people in different groups/conditions may be different (because groups occurred naturally)
• History: an event occurring around same time as manipulation that has nothing to do with manipulation
• Maturation: people change over time
• Testing: repeatedly testing can change responses
• Differential attrition: when attrition is related to condition
• No control/baseline: nothing to compare it to 29
30
Types of designs
• Correlational (cross-lag panel)• Quasi-experimental
– One group posttest only– One group pretest-posttest– Nonequivalent control posttest only– Nonequivalent control pretest-posttest– Interrupted time series– Non-equivalent control time series
• True experiments– Posttest equivalent groups– Pretest-posttest equivalent groups
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True Experimental Designs
Posttest equivalent groupsR X O2
R O2
Pretest posttest equivalent groupsR O1 X O2
R O1 O2
Breckenridge Brewery Ad
• Breckenridge Brewery wants to assess the efficacy of TV ad spots for its new Amber Ale.
X: Two weeks of ads for Breckenridge Ale. O: Give survey on beer brands purchased over past week.
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33
Match up the 8 designs
• Quasi-experimental– One group posttest only– One group pretest-posttest– Nonequivalent control posttest only– Nonequivalent control pretest-posttest– Time series– Non-equivalent control time series
• True experiments– Posttest equivalent groups– Pretest-posttest equivalent groups
Breckenridge Amber Ale
O2
Mean = 1.3 Packs per Week
XTwo Weeks of Ads for
Breckenridge Amber Ale
O2
Mean = 0.5 Packs per WeekDifference b/w cities = 0.8
Durham
Chapel Hill
Design? ______________
Breckenridge Amber Ale
O2
Mean = 1.3 Packs per Week
XTwo Weeks of Ads for
Breckenridge Amber Ale
O2
Mean = 0.5 Packs per WeekDifference b/w cities = 0.8
Durham
Chapel Hill
Design? Nonequivalent control posttest only
36
Mean Breckenridge consumption(packs per week)
O1 O2 O3 O4 O5 O60
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
Durham (ad shown)
Design? ______________
37
Mean Breckenridge consumption(packs per week)
O1 O2 O3 O4 O5 O60
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
Durham (ad shown)
Design? Interrupted Time series
Breckenridge Amber Ale
O1 O2
Mean = 0.2 Packs per Week Mean = 1.3 Packs per Week
XTwo Weeks of Ads for
Breckenridge Amber Ale
O1
Mean = 0.3 Packs per Week
O2
Mean = 0.5 Packs per Week
Δ=1.1
Δ=0.2
ΔΔ=0.9
Experimental Group (Randomly Assigned)
Control Group (Randomly Assigned)
Design? ______________
Breckenridge Amber Ale
O1 O2
Mean = 0.2 Packs per Week Mean = 1.3 Packs per Week
XTwo Weeks of Ads for
Breckenridge Amber Ale
O1
Mean = 0.3 Packs per Week
O2
Mean = 0.5 Packs per Week
Δ=1.1
Δ=0.2
ΔΔ=0.9
Experimental Group (Randomly Assigned)
Control Group (Randomly Assigned)
Design? Pretest-posttest equivalent groups
Breckenridge Amber Ale
O2
Mean = 0.16 Packs per Week
X Two Weeks of Ads for
Breckenridge Amber Ale
Design? ______________
Breckenridge Amber Ale
O2
Mean = 0.16 Packs per Week
X Two Weeks of Ads for
Breckenridge Amber Ale
Design? One group posttest only
Breckenridge Amber Ale
O1 O2
Mean = 0.2 Packs per Week Mean = 1.3 Packs per Week
XTwo Weeks of Ads for
Breckenridge Amber Ale
O1
Mean = 0.3 Packs per Week
O2
Mean = 0.5 Packs per Week
Δ=1.1
Δ=0.2
ΔΔ=0.9
Durham
Chapel Hill
Design? ______________
Breckenridge Amber Ale
O1 O2
Mean = 0.2 Packs per Week Mean = 1.3 Packs per Week
XTwo Weeks of Ads for
Breckenridge Amber Ale
O1
Mean = 0.3 Packs per Week
O2
Mean = 0.5 Packs per Week
Δ=1.1
Δ=0.2
ΔΔ=0.9
Durham
Chapel Hill
Design? Nonequivalent control pretest-posttest
Breckenridge Amber Ale
O2
Mean = 1.3 Packs per Week
XTwo Weeks of Ads for
Breckenridge Amber Ale
O2
Mean = 0.5 Packs per Week
Difference between groups=0.8
Experimental Group (Randomly Assigned)
Control Group (Randomly Assigned)
Design? ______________
Breckenridge Amber Ale
O2
Mean = 1.3 Packs per Week
XTwo Weeks of Ads for
Breckenridge Amber Ale
O2
Mean = 0.5 Packs per Week
Difference between groups=0.8
Experimental Group (Randomly Assigned)
Control Group (Randomly Assigned)
Design? Posttest equivalent groups
46
Mean Breckenridge consumption(packs per week)
O1 O2 O3 O4 O5 O60
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
Durham (ad shown)Chapel Hill (ad not shown)
Design? ______________
47
Mean Breckenridge consumption(packs per week)
O1 O2 O3 O4 O5 O60
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
Durham (ad shown)Chapel Hill (ad not shown)
Design? Nonequivalent control time series
Breckenridge Amber Ale
O1 O2
Mean = 0.2 Packs per Week Mean = 1.3 Packs per Week
X Two Weeks of Ads for
Breckenridge Amber Ale
Δ=1.1
Design? ______________
Breckenridge Amber Ale
O1 O2
Mean = 0.2 Packs per Week Mean = 1.3 Packs per Week
X Two Weeks of Ads for
Breckenridge Amber Ale
Δ=1.1
Design? One group pretest-posttest
Experiments - Factorial Designs
• 2 or more independent variables (manipulated and/or measured), each with two or more levels. – Type 1: 2 marketing mix variables
• Both variables manipulated• Important for determining whether you need to coordinate
marketing actions
– Type 2: “tactical segmentation” (1 segment responds differently to a marketing mix variable than another segment)
• Segmenting variable is measured, marketing action is manipulated
• Important for determining whether you should segment for that particular marketing action 50
Experiments - Factorial Designs
• What to look for in factorial designs– Is there a main effect of A?– Is there a main effect of B?– Key: Is there an interaction between A and B?
(interaction: effect of one IV on DV depends on level of another IV)
• Analysis – Eye-ball method– Analysis of Variance (ANOVA)
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Type 1: 2 marketing mix variables
• Assume two of you manage the Oreo account at Kroger. – One manages advertising, one
manages in store promotions like end-of-aisle display
• You have been asked to evaluate whether ads and/or end-of-aisle display would increase sales …
AdvertisingEnd of Aisle
Oreo Promotion Experiment
Kroger: Supporting a discount on Oreo cookies
Factor A: Ads in local papera1 = no adsa2 = ad in Thursday local paper
Factor B: Display locationb1 = regular shelfb2 = end aisle
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OREO PROMOTION EXPERIMENTScenario 1
(EXPENDITURES/CUSTOMER/2 WKS)
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OREO PROMOTION EXPERIMENTScenario 1
(EXPENDITURES/CUSTOMER/2 WKS)
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Main effect of A?
Main effect of B?
OREO PROMOTION EXPERIMENTScenario 1
(EXPENDITURES/CUSTOMER/2 WKS)
56Interaction?
this diffvs.
this diff
OREO PROMOTION EXPERIMENTScenario 1
(EXPENDITURES/CUSTOMER/2 WKS)
57Interaction?
this diffvs.
this diff
SALES OF OREOS
58
SALES OF OREOS
59
2 main effects, no interaction
SALES OF OREOS
60
Do they need to coordinate to make their decisions?
Oreo Promotion ExperimentScenario 2
(Expenditures/customer/2 wks)
61
0.95 0.70
0.75
0.8250.800.77
5
Oreo Promotion ExperimentScenario 2
(Expenditures/customer/2 wks)
62
0.95 0.70
0.75
0.8250.800.77
5
Main effect of A?
Main effect of B?
Oreo Promotion ExperimentScenario 2
(Expenditures/customer/2 wks)
63
0.95 0.70
0.75
0.8250.800.77
5
Interaction?
64
A1=ads A2=no ads0.5
0.55
0.6
0.65
0.7
0.75
0.8
0.85
0.9
0.95
1
B1-reg shelfB2-end aisle
SALES OF OREOS(Expenditures/customer/2 wks)
65
A1=ads A2=no ads0.5
0.55
0.6
0.65
0.7
0.75
0.8
0.85
0.9
0.95
1
B1-reg shelfB2-end aisle
“Cross-over” interaction
SALES OF OREOS(Expenditures/customer/2 wks)
66
A1=ads A2=no ads0.5
0.55
0.6
0.65
0.7
0.75
0.8
0.85
0.9
0.95
1
B1-reg shelfB2-end aisle
SALES OF OREOS(Expenditures/customer/2 wks)
Do they need to coordinate to make their decisions?
Oreo Promotion ExperimentScenario 3
(Expenditures/customer/2 wks)
67
1.30
Oreo Promotion ExperimentScenario 3
(Expenditures/customer/2 wks)
68
1.30
Main effect of A?
Main effect of B?
Oreo Promotion ExperimentScenario 3
(Expenditures/customer/2 wks)
69
Interaction?
1.30
70
SALES OF OREOS(Expenditures/customer/2 wks)
a1=no ads a2=ads0.5
0.6
0.7
0.8
0.9
1
1.1
1.2
1.3
1.4
1.5
b1=regular shelfb2=end aisle
71
SALES OF OREOS(Expenditures/customer/2 wks)
a1=no ads a2=ads0.5
0.6
0.7
0.8
0.9
1
1.1
1.2
1.3
1.4
1.5
b1=regular shelfb2=end aisle
“fan effect” interaction
72
SALES OF OREOS(Expenditures/customer/2 wks)
a1=no ads a2=ads0.5
0.6
0.7
0.8
0.9
1
1.1
1.2
1.3
1.4
1.5
b1=regular shelfb2=end aisle
Do they need to coordinate to make their decision?
Oreo Example
• No A x B interaction– Effect of changing A (Ads) is independent of level of B
(Display Location). – Implies that Ad & Display decisions can be
decoupled…they influence sales additively
• A x B interaction– Effect of changing A (ads) depends on level of B
(display location), and/or vice-versa– Fan effect: Cannot decouple variables– Cross-over: Cannot decouple variables
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Type 2: Tactical Segmentation
• Should groups be treated same or differently with respect to specific marketing decision variable?
• A is a controllable decision variable and B is a potential segmentation variable – Interaction means that segments respond differently to
this marketing lever.– Example: coupons x urban/suburban
• Question: does marketing mix variable have bigger effect for segment A or B?
• Is coupon more effective in urban or suburban neighborhoods? 74
Interactions and segmentation
75
Interactions and segmentation
76
Coupons have a bigger effect in the suburbs
Tactical Segmentation
• Example - Dog Food• 1 potential segmentation variable (Size of Dog)• 2 decisions
– Price: Hi v. Lo – Ad Theme: “Love between dog and owner”
vs. “Dog’s Active Life”
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Segmentation Example: Dog Food I (rated on 10 pt scale)
• Price
• Advertising
78
Segmentation Example: Dog Food I• Price
• Advertising
79
80
Implications of Contrast
• A variable that is an excellent basis for segmentation with respect to one decision about a marketing mix element may be a poor basis for segmentation with respect to another mix element
• For any given mix element decision, when evaluating alternative bases for segmentation, look for ones with big differences in sensitivity to mix variable.
81
Tactical Segmentation II
• Example - Dog Food • 1 decision: Price (hi vs. lo)• 2 potential segmentation variables
– Size of Dog – Income of owner
82
Segmentation Example: Dog Food II
83
How to compare? ANOVA
Segmentation Example: Dog Food II
84
Eye-ball method: compare difference of differences
Segmentation Example: Dog Food II
85
Price
Price
86
Garlic Chopper Prototype Breakout
• Survey: what do you want to know?• How to structure survey?• What type(s) of scales to use?• Can randomly assign to conditions
– What would you want to manipulate/measure?
87
For next time
• IBM case (team assignment due)• Guest lecture: Kevin Clark• Due: Cola conjoint assignment• Not due: “product line scenarios”• Quiz 2 next Friday
– Study guide will be on Sakai shortly