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A joint venture between Moskowitz Jacobs Inc & The Understanding and Insight Group Creating winning communications and features … in a cost effective, rapid, user friendly way. Stimulus and Response - Point of view. Knowledge: Consumers can’t tell you what they want

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slide1

A joint venture between Moskowitz Jacobs Inc & The Understanding and Insight GroupCreating winning communications and features …in a cost effective, rapid, user friendly way

stimulus and response point of view
Stimulus and Response - Point of view
  • Knowledge: Consumers can’t tell you what they want
  • Perception: But .. They know it when they see it
  • Empiricism: If you can present them test concepts and get ratings .. You can identify what wins
  • Structure: If you systematically vary these stimuli you can identify what specific drivers are working
five key knowledge benefits
Five key knowledge benefits
  • Better data … why settle for guessing or focus groups when you get solid quantitative answers
  • Clearer results … looking at the results gives you immediate insight and direction
  • Multi-media….Whether concepts, packages … you get to test many stimuli
  • Segmentation …you get to see new segments, and what turns them on
  • Synergies and suppressions .. Identify what works together, what doesn’t
conjoint measurement
Conjoint measurement
  • Standard research technique – Designed Experiment
  • Systematically vary the test stimuli
  • Get responses
  • Use statistics to link stimulus elements and responses
  • Build model at individual respondent level showing ‘drivers’ of response
what is conjoint a definition
What is conjoint? A definition…
  • Conjoint analysis is founded on the statistical method of experimental design (Box, Hunter & Hunter, 1976). By systematically varying the components of the concept and obtaining ratings, the researcher can identify what specific parts of the concept drive the ratings, and what specific parts are irrelevant.
  • The rationale underlying conjoint is that the consumers may not, in fact, know what is important, if they have to rate each of the components of the product or service on a one by one basis. The goal of all conjoint measurement is to provide a measure for each element or feature in the study. The measure shows the degree to which the presence of the element in a concept drives the rating. (If the rating is interest, then the utility or impact measure shows the ability of the element to increase or decrease interest when the element becomes a part of the concept).
marketing research satisfaction
Marketing Research Satisfaction

Source: PDMA Certification Workshop

Mahajan/ Wind 1991

example of conjoint analysis design

Packaging/ Color

Red

Green

White

Price

$1.25

$1.50

$1.75

Size/Count

6ct-6oz

8ct-8oz

10ct-10oz

Product Name

Creamy

Chunky

Crunchy

Example of Conjoint Analysis Design

Frozen Ice Cream Bar Concept

example of conjoint analysis design10
Example of Conjoint Analysis Design

Frozen Ice Cream Bar Concept

Packaging/ Color

Red

Price

$1.25

Size/Count

6ct-6oz

Product Name

Creamy

example of conjoint analysis design11
Example of Conjoint Analysis Design

Frozen Ice Cream Bar Concept

Packaging/ Color

Red

Price

$1.50

Size/Count

Product Name

Creamy

the process user oriented templates

The ‘process’User oriented templates

Set up the study

‘Fill in the blanks’

Oriented toward simplicity, speed

what the respondent sees concept screen
What the Respondent Sees – Concept Screen

Category 1

Food Descriptors

Category 2

Situational/ Mood

Category 3

Emotional Attributes

Rating Question

Category 4

Brand/ Benefit

example total sample results 3 key numbers base size constant and element utility scores
Example Total Sample Results – 3 Key Numbers: Base Size, Constant and Element/Utility Scores

Base Size

Constant

Element/Utility Score

classification questions
Classification Questions
  • For the following set of questions, there may be answer options that are relevant to some beverage categories and not others. Please answer each question based on your idea of what options are relevant for Chicken.
the regression
The Regression
  • IdeaMap®.Net uses regression (Y=MX+B)
  • The constant is B, which refers to the estimated value of Y (interest, persuasion) when X is 0 (X are elements... thus when there areno elements present)
  • The equation is always developed with a constant
  • The constant is a mathematical correction factor
two kinds of numbers from regression
Two Kinds of Numbers from Regression
  • Constant – represents the base level of interest in a specified category, before exposure to concept elements; reflects respondent’s prior experience
  • Element utility – represents the contribution of the element to the overall appeal of the concept; can be positive, neutral, or negative
interpreting constant scores
Interpreting Constant Scores
  • The constant reflects:
    • Captures any interest in the concept not tied back to any specific element
    • A respondents previous category experience
    • Any ingoing level of interest in the overall idea as presented in the positioning statement
norms for the additive constant
Norms for the Additive Constant
  • 0-20 Little base interest
  • 21-40 Modest base interest
  • 41-60 Typical base interest
  • 61-80 High base interest
  • 81+ Very high base interest
low additive constant
Low Additive Constant
  • If the constant is low, and the elements are low, then.... consumers are not interested in the general idea of the product and no communications tested will enhance their base level of interest.
  • If the constant is low, and the elements are varied (high...low) then ... the basic idea is fair, but you can increase acceptance by the correct choice of elements
high additive constant
High Additive Constant
  • If the constant is high, but the elements are low... consumers in-going level of interest is high, but none of the tested communications will further enhance their existing level of interest.
  • We have not seen a high constant and high positive elements
interpreting element scores
Interpreting Element Scores
  • Each element (concept component) is assigned an impact score that represents the interest contribution of that component.
    • Positive values indicate that the feature enhances consumer interest.
    • Scores that are near zero indicate consumers are indifferent to inclusion of that feature.
    • Negative values indicate that the feature detracts from consumer interest.
norms for the element contribution
Norms for the Element Contribution
  • > 20 = dynamite
  • 16-20 = excellent
  • 10-15 = very good, important
  • 6-10 = good
  • 0 - 5 = so what
  • < 0 = detracts
slide42

Looking at the DataSee it generate before your eyesUnderstand the sophisticated design behind what you see

A trial exercise – 4x3 design

experimental design 25 concepts
Experimental Design – 25 Concepts
  • Numbers in body of table show which of the 4 elements selected from that category appears in the specific concept
  • Each concept comprises 5 categories. If a 0 appears, then the concept has no element from that category
after the study

After the Study

Understanding the data that comes back

reports you receive
Reports You Receive
  • Topline report for total sample
    • Includes base size, elements, and classification
    • Ranked from highest to lowest element score in each category of the study
    • Summarized in a single sheet to allow for insights across categories
further files you can get
.ELM

.QSN

.QST

.CLS

.OP0

.OPC

.OPS

.OT0

.OTC

.OTS

.OPEN

.SGM

Further Files You Can Get
elm file
.ELM file
  • File with a list of all the elements in the project.
    • Note: these element keys (E_1, etc.) are used in the following files as column headers to identify utilities for each of the elements.
      • OP0
      • OPC
      • OPS
      • OT0
      • OTC
      • OTS
qsn and qst files
.QSN and .QST files

QSN file

  • File with Classification Questions - lists all the classification questions in the project

QST file

  • Text of Rating Question
cls file
.CLS file
  • This file contains data from the Classification part of the project.
  • UID is a unique respondent's ID maintained by database. Do not pay attention to values in this column (there may be skips, etc.) This is just a way for you to identify corresponding rows in different files if you need.
  • Qi - question number.
    • There is one column for Single Selection and Range questions.
    • There is NO column for Description "questions".
    • Multiple Selection Questions columns are labeled as Qn_Mm where n is question number and m is option number in this question.
op0 file
.OP0 file
  • Persuasion Model file - individual scores for each of the respondents by each element.
  • Ratings 1-9 has been recoded as: 1 goes to 11, 2 to 22, 3 to 33, etc. before running the regression.
  • The result is a regression model for each person – the regression model has an additive constant (estimated impact if no elements are present) and the individual impact value (one per element).
  • Const. is the constant for the regression.
  • One row = one person
  • Persuasion shows the intensity of the response
  • The additive constant and coefficient summed together show intensity of the combination
opc file
.OPC file
  • Combined Persuasion Model and Classification File -- this file is the aggregation of .OP0 and .CLS files.
  • Provided for your convenience. By merging .OP0 and .CLS we created a file that is easier to use if you want to create subgroups or just analyze data based on classification questions
  • Const. is the constant for the regression.
  • One row = one person
  • Persuasion shows the intensity of the response
  • The additive constant and coefficient summed together show intensity of the combination
ops file
.OPS file
  • Combined Persuasion Model for subgroups and Segmentation -- this file is the aggregation of .OP0 into different subgroups based on the classification questions and 2,3 and 4 segments.
  • This is a summary or average file
  • Each column corresponds to a subgroup
  • Segmentation Lite – uses a combination of K means and hierarchical clustering based on SPSS methods
  • Each row = one element
  • Persuasion shows the intensity of the response
  • The additive constant and coefficient summed together show intensity of the combination
ot0 file
.OT0 file
  • Interest Model -- file with respondents’ individual scores for each element.
  • Rating 1 to 6 has been recoded as 0 while ratings 7, 8 and 9 has been recoded as 100 before running regression (top 3 box).
  • The result is a regression model for each person – the regression model has an additive constant (estimated impact if no elements are present) and the individual impact value (one per element).
  • One row = one person
  • Interest shows the conditional probability that a person will go from disinterested in a concept to interested
  • The additive constant and coefficient summed together show the conditional probability of being interested if the elements are present
otc file
.OTC file
  • Combined Interest and Classification File -- this file is the aggregation of .OT0 and .CLS files.
  • Provided for your convenience. By merging .OT0 and .CLS we created a file that is easier to use if you want to create subgroups or just analyze data based on classification questions
  • Const. is the constant for the regression.
  • One row = one person
  • Interest shows the # of people who are interested
  • The additive constant and coefficient summed together show of the conditional probability of being interested if the elements are present
ots file
.OTS file
  • Combined Interest Model (top 3 box) for subgroups and Segmentation -- this file is the aggregation of .OT0 into different subgroups based on the classification questions and 2,3 and 4 segments
  • This is a summary or average file
  • Each column corresponds to a subgroup
  • Segmentation Lite – uses a combination of K means and hierarchical clustering based on SPSS methods
  • Each row = one element
  • Interest shows the # of people who are interested
  • The additive constant and coefficient summed together show of the conditional probability of being interested if the elements are present
open file
.OPEN file
  • File with combined UID (user ID) and data from open end questions
  • One line = one respondent
  • One column = one open end question
sgm file
.SGM file
  • File containing raw data from the Segmentation part of the project
  • One column = One segment solution
  • In a 2 segment solution 2 columns will be created
    • I.e. - Segment 1 of 2, Segment 2 of 2, etc.
  • 0 represents absence in the segment, 1 represents presence in that segment (Best viewed in Excel)