Session 7- Conjoint Study

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Session 7. Sampling Strategy
and Conjoint Study
MKTG 3010 MARKETING RESEARCH
1
Marketing Research Process
Step 1: Defining the Problem
Step 2: Developing an Approach to the Problem
Step 3: Formulating a Research Design
Step 4: Doing Field Work or Collecting Data
Step 5: Preparing and Analyzing Data
Step 6: Preparing and Presenting the Report
2
Define the Information Needed
Design the Exploratory, Descriptive, and/or Causal Phases of the Research
Specify the Measurement and Scaling Procedures
Construct a Questionnaire
Specify the Sampling Process and the Sample Size
Develop a Plan of Data Analysis
3
Key Issues in Survey Design

Structure of the survey
Order of information


Respondent-driven design

Understanding the psychology of survey response
•
Question wording
−
4
Are they answering what we’re asking?
Review -
Do you find anything wrong with the
following questions? Why?
1.
2.
5
“How many cartons of orange juice did you buy
in the last year?”
“Do you think that patriotic Americans should
buy imported automobiles when that would put
American labor out of work?
Context Effects: Compromise
HD Space
Preference for the
middle option
RAM
6
Judgment
7
8
The Importance of Alternatives:
The Decoy Effect
Add a decoy choice to the choice
set, next to the choice you really
want people to take.
9
Define the Information Needed
Design the Exploratory, Descriptive, and/or Causal Phases of the Research
Specify the Measurement and Scaling Procedures
Construct a Questionnaire
Specify the Sampling Process and the Sample Size
Develop a Plan of Data Analysis
10
Large Sample Size vs.
Probability Design Sampling
2 Mil+
11
“Landon in a Landslide,” History Matters
Sampling Strategy
Define the Population
Determine the Sampling Frame
Select Sampling Technique(s)
Determine the Sample Size
Execute the Sampling Process
I. Define the Target Population



The target population is the collection of
elements or objects that possess the information
sought by the researcher and about which
inferences are to be made.
Who do you want to generalize to?
The target population should be defined in terms
of




13
Elements – the object that the information is desired
Sampling units – element, or entity contains the
element
Extent - geographical boundaries
Time – the time period of interest
Example: Revlon want to sample women over 18
years of age
Time Frame:
Upcoming
Summer
Sampling Unit:
Households
with 18 year
old females
14
Extent:
Domestic
United States
Element:
18 year old
females
II. Determine the Sampling Frame




A sampling frame is a representation of the
elements of the target population.
It consists of a list or set of directions for
identifying the target population.
How can you get access to them?
e.g.



15
telephone book
a mailing list purchased from a commercial
organization
Yellow page
III. Select Sampling Techniques
Sampling
Techniques
Nonprobability
Sampling
Techniques
16
Probability
Sampling
Techniques

Probability


Non-Probability


Population elements are selected in a non-random
manner
Advantages of Probability Samples


17
Every element has a known, non-zero probability of
inclusion in the sample
Allows quantification of sampling error
Generally more representative
Probability Sampling Techniques
Probability Sampling Techniques
Simple Random
Sampling
18
Systematic
Sampling
Stratified
Sampling
Cluster
Sampling
Nonprobability Sampling
Nonprobability Sampling Techniques
Convenience
Sampling
19
Judgmental
Sampling
Quota
Sampling
Snowball
Sampling
Simple Random Sampling
Each element of the population has the same
known nonzero probability of inclusion
Example:
Random selection
from voter
database
20
Simple Random Sampling
Mechanics



Table of random numbers
Computer generated random numbers
Random digit dialing
Advantages
• Easy to implement
• Does not always require a list
(e.g. random-digit dialing)
21
Disadvantages
• Less efficient than stratified
sampling
• May be more expensive than
cluster sampling
Is Random Sampling
“Random Enough”?

Can get outcomes that don’t “seem”
random.
1 out of 16 chance
OR
1 out of 16 chance
→“Inefficient”
22
Systematic Sampling
kth

Every
element from the list

Systematically spreading the
sample through
the population list

Sampling efficiency depends
on the ordering of the list
(e.g. sorted on key variable)
23
Example:
Every 5th voter
from registered
voter list
Leveraging Sample Structure: Stratified vs.
Cluster Sampling
Stratified
Stratified
Cluster
Cluster
Grouping:
homogenous
Strategy:
randomly sample within
randomly select clusters
e.g.
5 voters at each precinct
every voter at 5 precincts
Tradeoff:
more statistically efficient
cheaper
24
heterogenous
Matched Samples


Identify matched pairs (e.g. on demographics)
Controls for confounding variables in small
samples
25
Is There Ever Such a Thing as
a Random Sample?

Often there is no “master list”


Usually only a subset of the universe is relevant


What is the universe being sampled from?
“Likely voters”
Non-response bias



26
20-30% response rates common (9%, Pew Center, 2012)
Biases in contact, acceptance and completion
Attempts to improve response rates can backfire
Survey Bias, Slate, 5/17/2012
Case Study:
2008 California Democratic Primary
Zogby
Survey USA
Feb 3-4
N=895
MOE = +/- 3.3%
Obama 49%
Clinton 36%
Feb 3-4
N=872
MOE = +/- 3.4%
Obama 42%
Clinton 52%
Why?
Turnout assumptions…
27
Actual
“It appears that we underestimated
Hispanic turnout and overestimated the
importance of younger Hispanic voters.
We also overestimated turnout among
African-American voters…” Zogby
43%
52%
Coping With Non-Random Samples:
Account For and Manage Bias

Weights / Quotas


Emphasize comparisons over absolute numbers



When key characteristics of population are known
Testing alternatives vs. marketing sizing
Experiments
Validate large (cheap) non-random sample by
comparing with small (expensive) more random
sample
28
IV. Determining Sample Size:
Generalizing from Sample to Population

Basis of statistics: Law of Large Numbers

As the sample size increases, the sample
measure converges to the population “true”
measure
Example:
Proportion Choosing Coke

Interview 1 Person
1.0
0.8
0.6
0.4
0.2
0.0
1
0
2
3
4
5
6
7
8
9
50%
% preferring Coke
10
11
100%
Either chooses Coke
or Pepsi
Example:
Proportion Choosing Coke

Interview 2 People
1.0
0.8
0.6
0.4
0.2
0.0
1
0
2
3
4
5
6
7
8
9
50%
% preferring Coke
10
11
100%
3 outcomes;
Split most likely
Example:
Proportion Choosing Coke

Interview 4 People
1.0
0.8
0.6
0.4
0.2
0.0
1
0
2
3
4
5
6
7
8
9
50%
% preferring Coke
10
11
100%
5 outcomes;
Extremes less likely
Example:
Proportion Choosing Coke

Interview 10
People
1.0
0.8
0.6
0.4
0.2
0.0
1
0
2
3
4
5
6
7
8
9
50%
% preferring Coke
10
11
100%
10 outcomes;
Extremes less likely
Example:
Proportion Choosing Coke

Interview 100
People
1.0
0.8
0.6
Normal
distribution
0.4
0.2
0.0
1
0
2
3
4
5
6
7
8
9
50%
% preferring Coke
11
Extremes
100%
10
highly unlikely
Most of the time,
within 10%
Confidence Interval
(Margin of Error) Examples
N
MOE
For proportion of 20%:
N
MOE
10
30
50
100
250
500
1000
+/-30%
+/-18%
+/-14%
+/-10%
+/-6%
+/-4.4%
+/-3%
10
30
50
100
250
500
1000
For proportion of 50%:
+/-25%
+/-14%
+/-11%
+/-8%
+/-5%
+/-3.5%
+/-2.5%
Benefit of adding more cases drops off…
How Large Should the Sample Be?

Work backwards from a margin of error
table or confidence interval.
Sample size determination - means





1. Specify the level of precision (D)
2. Specify the confidence level (95%)
3. Determine the z value associated with the
CL(z=1.96)
4. Determine the standard deviation of the
population (secondary source) ()
5. Determine the sample size using the
formula for the standard error
 22
= 2

Sample size determination - proportions





1. Specify the level of precision (D)
2. Specify the confidence level (95%)
3. Determine the z value associated with the CL
(z=1.96)
4. Estimate the population proportion(secondary
source) (π)
5. Determine the sample size using the formula
for the standard error of the proportion
(1 − ) 2
=
2
A sample size of 400 is
enough to represent China’s
more than 1.3 billion people
or the more than 300 million
American people.
The sample size is
independent of the
population size for large
populations.
4-39
Conjoint Study
40
Getting to know your classmate
Find out what smartphone the person sitting
next to you most prefers.
Find out what feature he/she considers most
important.
41

Color


Screen Size


4.7 inch, 5.2 inch, 5.7 inch,
Capacity


White, Black, Gold
32 GB, 128 GB, 256 GB
Price

42
HKD 4688, HKD 6588, HKD
8288
Conjoint Analysis
Conjoint analysis is a popular marketing
research technique that marketers use to
determine what features a new product
should have and how it should be priced.
 Conjoint analysis became popular because
it was a far less expensive and more
flexible way to address these issues than
concept testing.

What is Conjoint Analysis?

Research technique developed in early 70s

Measures how buyers value components of a
product/service bundle

Dictionary definition-- “Conjoint: Joined together,
combined.”

Marketer’s catch-phrase-- “Features CONsidered
JOINTly”
Main Idea –
Suppose we want to produce a new golf ball

We know from experience and from talking
with golfers that there are three important
product features:



Average driving distance
Average ball life
Price

A range of feasible alternatives for each of
these features
“ideal” for the
consumer
“ideal” for the
company
What is the most
viable product?
Now consider the same two features taken
conjointly
Using some statistic method
Next, let’s figure out a set of values for
driving distance (eg. 100, 60,0) and a
second set for ball life (eg. 50,25,0)
 So that when we add the value together,
they can reproduce buyer 1’s rank orders.

Next, do the same thing to price and ball life
A complete set of values that capture
Buyer1’s trade-offs (Part-worth)
Use the values to predict choice
What’s So Good about Conjoint?

More realistic questions:
Would you prefer . . .
Nice attendants
Often late

or
Cold attendant
Never late
Logic:



If choose left, you prefer service. If choose right, you prefer
punctuality
Rather than ask directly whether you prefer service over
punctuality, we present realistic tradeoff scenarios and infer
preferences from your product choices
When respondents are forced to make difficult tradeoffs, we learn
what they truly value
Ask directly?

Ask direct questions about importance


How important is it that you get the <<brand, interest
rate, annual fee, credit limit>> that you want?
What is the problem with this?
Stated Importances

Importance Ratings often have low discrimination with
most responses falling in most important categories:
Average Importance Ratings
6.7
Brand
7.2
Interest Rate
8.1
Annual Fee
7.5
Credit Limit
0
5
10
How to Learn What Customers Want for Each
Component?

Ask direct questions about preference:







What brand do you prefer?
What processor do you prefer?
How much RAM do you prefer?
How much hard disk space do you prefer?
What type of monitor do you prefer?
What price do you prefer?
What is the problem with this?
Problems with Direct Questioning

Answers are often trivial and
unenlightening




“I prefer more processor speed to less”
“I prefer more RAM to less”
“I prefer more hard disk space to less”
“I prefer a lower price to a higher price”
Conjoint Study Process
Step 1
—Designing the conjoint study:
Step 1.1: Select attributes relevant to the product or service
category,
Step 1.2: Select levels for each attribute, and
Step 1.3: Develop the product bundles to be evaluated.
Step 2
—Obtaining data from a sample of respondents:
Step 2.1: Design a data-collection procedure, and
Step 2.2: Select a computation method for obtaining part-worth
functions.
Step 3
—Evaluating product design options:
Step 3.1: Part-worth functions
Step 3.2: Attribute importance
Step 3.3: Design market simulations
Step 2 Obtaining Data from respondents
Profile cards
 Rank order data
 Rating data

Step 3 Conjoint Analysis Output



Part-worth functions. The part-worth functions,
or utility functions, describe the utility consumers
attach to the levels of each attribute.
Relative importance weights. The relative
importance weights are estimated and indicate
which attributes are important in influencing
consumer choice.
Market simulations
I. Conjoint Utilities (Part-Worths)

Numeric values that reflect how desirable
different features are for each individual:
Color
Level
black
white
gold
Utility
4.67
6.33
4.00
Screen Size
4.7 inch
5.2 inch
5.5 inch
3.33
6.00
5.67
32 GB
128 GB
256 GB
2.67
6.33
6.00
$4,688
$6,588
$8,288
5.33
3.33
6.33
Capacity
Price

The higher the utility, the better
60
What is the
most desirable
offering?
II. Conjoint Importance


Measure of how much difference each attribute could
make in the total utility of a product.
Best minus worst level of each attribute, take percentage:
White - Gold
5.2 in- 4.7 in
128 GB –32 GB
$8288 - $2588
What is the
most
important
factor?

(6.33 – 4.00) = 2.33
(6.00 – 3.33) = 2.67
(6.33 – 2.67) = 3.66
(6.33 - 3.33) = 3.00
-----Totals:
11.66
20%
23%
31%
26%
-------100.0%
Importances are directly affected by the range of levels
61you choose for each attribute
III. Market Simulations

Make competitive market scenarios and predict
which products respondents would choose

Accumulate (aggregate) respondent predictions
to make “Shares of Preference” (some refer to
them as “market shares”)
62
III. Market Simulation Example

Consider the following two products:
1. White, 5.2 in, 256 GB, $6588
2. Gold, 5.7 in, 128 GB, $8288
Which one does he/she prefer?
For example 1. 6.33 + 6.0 + 6.0 + 3.33 = 21.66
2. 4.00 + 5.67 + 6.33 + 6.33 = 22.33
What is the
share in the
class?
63
Try it yourself – designing a new smart
phone

Color


Screen Size


4.7 inch, 5.2 inch, 5.7 inch,
Capacity


White, Black, Gold
32 GB, 128 GB, 256 GB
Price

64
HKD 4688, HKD 6588, HKD
8288
Guest Talk
Venue: YIA LT3
 Size: 249
 Time: 18:30 - 20:30
 Date: October, 29th
 No class on Tue

65
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