1 1 Two emerging opportunities for consumer research Innovation of Ideas Graphics Designs Howard R. Moskowitz Moskowitz Jacobs Inc. White Plains, NY USA [email protected]
Dec 28, 2015
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Two emerging opportunities for consumer research
Innovation of IdeasGraphics Designs
Howard R. MoskowitzMoskowitz Jacobs Inc.White Plains, NY [email protected]
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Opportunity # 1: Innovation
Bringing respondents into the design of features
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A General Point Of View
Innovation may be accomplished by recombining old features into new mixtures
If so … then can we create a system to make this easy?
What do we do? What do we learn?
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One way is to develop ‘creative consumers’
Find consumers who are articulate– Work with them– Get ideas– Hope that the system generates innovation
Or work with them and ‘creatives’– Use consumer inputs– But rely on ‘creatives’ to create/innovate
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What does this imply
Trust in the ‘expert’ Outsource creation / innovation to
someone– Put your hopes in that person
If it were my money– I’d rather spend it on a machine that creates– Yes.. There is no soul … but the machine may
end up being more efficient
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So let’s design our machine
Precompiled databases of featuresA combinatorial machine
A consumer who respondsAn analytic strategy to recombine
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Why we did what we did Innovation is like the ‘weather’
– Everyone talks about it– There are metrics and books
But how ..how do we actually do it?– Inspiration?– Deus ex machina
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The features of the machine Assembled Raw materials: A set of defined
databases prepared for conjoint analysis– Different product features – Already populated with actionable elements….the key step
New Idea Seeding: Within each database.. 3-4 more questions to open up the respondent mind– Problem scenarios demanding a specific solution
Combinatorics machine to create, test, use ideas– With specific, pre-set analysis strategies
Chance favors the prepared mind (Pasteur)– With high level optimization to create new, strong ideas
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Getting to the heart of innovation
DatabasesDatabasesDatabasesDatabases
FieldworkFieldworkFieldworkFieldwork
AnalysisAnalysisAnalysisAnalysis
ResultsResultsResultsResults
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We’re going to talk about foods
But think of consumer electronics or any other area
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Part 1 – Precompiled database of raw materials (idealets)
Rationale: Do the client’s homework for them
If the client can’t or won’t think .. Then the researcher should think for the client
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Database means assembling raw ideas
Corporate culture believes in single & simple problems
However lots of innovation comes from broadening scope & using analogies– Need multiple studies– Independent of close-in needs of a manufacturer– If the work is done…people MAY actually use it
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Precompiled database of ideasCreate 70 databases of raw concept elements ahead of time
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Common structure, actionable ideasCreate/standardize six silos of ideas…..
With the elements ‘real’ and ‘appropriate’
– Appearance/Texture: What does it look like, feel like?
– Primary ingredients: What does it contain ?
– Secondary ingredients: What healthful features does it offer?
– Taste/Flavor: What does it taste like?
– Packaging: How is it stored or packaged?
– Merchandising: Where is it merchandised in the store, or how is it used?
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Make all the elements availableIf users see the full range ..they’ll work out of the box
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Problem-solutionSeeding the next generation ideas
We wanted to have the respondents help us ideate– Give them open ended problems– Ask for solutions– Do this with 3-4 questions per database
Rationale– Making things specific focuses their minds
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Examples of problem-solution
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Part 2 -- Fieldwork
If we’re going to have an invention machine…
Make it easy to inventMake data acquisition a ‘snap’
After all .. We’re half way there already with precompiled data bases
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Field ExecutionInternet-based conjoint
Easy to set up– Template driven
Easy to execute– Send to respondents– Get data– Automatic analysis
So far … so good– Precompiled ideas +– Simple data acquisition
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Example of concept + rating scale
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Part 3 - Analysis
Using the data to drive innovation by identifying what works & what doesn’t,
and where there are ‘synergisms’
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ModelingIdentifying what works
We set up the elements ..& varied them
Now let’s relate elements to ratings– Additive Constant = baseline interest without any elements– Utilities = additive or conditional probability of interest if
element is present in concept
Looking at– Total panel & key subgroups (standard stuff)– ‘Latent’ segments .. Groups with like minds (based on
patterns of utilities)
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Deconstruct concepts to componentsTotal panel, subgroups, even so-called latent segments
Go after Segment 2 – the Health Seekers
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Part 4 - Results
Create new ideas
Combine ideas from different datasets according to evolution rules
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Use optimizer Select objectives
Mix & match components … making job easier
Objective..who / what we’re creating
Results … new combination
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Evolve, innovate by ‘rule based’ genetic algorithms
Merge winning ideas from different categories into a new product idea / even new category
Cookies Donut Chocolate Candy New Features Category #1 - Appearance/Texture
Bite size for a quick indulgence
Bite-size honey glazed donut holes with swirls of vanilla icing for a quick indulgence
Dense, velvety chocolate with a heavy texture for a decadent taste Bite size for a quick indulgence
Soft and chewy…just like homemade
A classic cake donut made with buttermilk…moist and rich
Creamy milk chocolate with a soft, chewy center for a satisfying experience
A classic cake donut made with buttermilk…moist and rich
Crisp and crunchy…perfect for dunking
A moist dark chocolate cake donut…for the real chocolate lover
Creamy chocolate with a crunchy, nougat center
Creamy chocolate with a crunchy, nougat center
Category #2 - Ingredients (Primary)Sweetened with natural fructose for a healthy indulgence
Made with canola oil which helps lower blood cholesterol levels
Made with the finest Swiss chocolate
Sweetened with natural fructose for a healthy indulgence
Made with only the freshest ingredients…eggs, milk, butter
For a healthy source of protein…made with unpasteurized egg whites
Made with the finest Belgian chocolate…for the discerning chocolatier
For a healthy source of protein…made with unpasteurized egg whites
Made with unprocessed whole grain flour…keeping all the goodness in
Sweetened with natural fructose for a healthy indulgence
Carob as the main ingredient…a healthy alternative
Carob as the main ingredient…a healthy alternative
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The bottom lineBeyond metrics around innovation & into its heart
Innovation requires hard work and some thinking– Homework up front removes some barriers– Setting up an innovation bed of elements, field,
analytics & optimization further helps Showing a path might move people out of
their ‘dogmatic slumbers’
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Opportunity #2: Graphics
Now that we have innovation treated as science ..
What about research in a more ‘artistic area’
Say… magazine cover design!!!!
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Business, Art, Science The magazine industry
How can we sell more at the ‘stand’?– Better content– Better covers– This isn’t the subscription part of the business
How do we attract advertisers?– Right now it’s a share of wallet issue– Limited money– Now what do we offer them?
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Let’s return to the combinatorics ideaBut this time …completely graphical (techno-art?)
Template (structure) Features and elements (components) Combine features by experimental design Test the combinations among consumers Model the results Interpret the data
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We saw before ..No problem with partial text concepts
Mind fills in the blanks
5 Element concept
4 Element concept
3 Element concept
2 Element concept
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But..what about graphics?Does the mind fill in the blanks?
Full concept 5 Elements
4 Elements Concept
2 Elements Concept
3 Elements Concept
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Experimental designABS = absent (incomplete picture)
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Let’s try it out
Applying the same ‘scientific’ approach to design
Well … at least the beginnings of design
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A1 A2 A3
Category 1 – Magazine Cover Background
Category 2 – Logo
B1 B2 B3
Category 3 – Head/Subhead
C1 C2 C3
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Example of setting up a visual stimulus according to a
systematized design
Example from ‘tea’Same approach done with
magazine covers
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Let’s try again…
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Now the package looks right Replace each visual with encompassing
rectangle Result – categories placeholders layout
(‘template’) The concrete example produces the schematic (not the other way around!!)
Create Template
Category ECategory E
Category CCategory C
Category DCategory D
Category BCategory B
CategoryCategory AA(background)(background)
Category DCategory D
CategoryCategory BB
Category CCategory C
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Template is a ‘multilayered cake’
Each layer == category
“Holes” == transparencyEach layer == category
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The respondent experience
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Who participated
Email invitation sent out from a list provider
523 respondents participated & completed (11% response rate)– Over a period of 36 hours
A sense of who they are comes from the classification pages at the end of the interview
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Respondents are consistent Individual data .. good fit of ‘model
0.5 0.6 0.7 0.8 0.9 1.0
Individual R-Square For Model
Poor Good
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The two types of information
First response = interest – Number = conditional probability of a respondent
being interested in the visual package if the feature is present
– Constant = baseline interest, without visuals Second response = time
– Number of tenths of seconds required for respondent to process information
– Computer measures response time, allocates time to the components … discovering what holds the eye
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A1 A2 A3
Category 1 – Magazine Cover Background
Category 2 – Logo
B1 B2 B3
Category 3 – Head/Subhead
C1 C2 C3
To refresh your memory ..elements
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What the different features contribute
-2 C2
-1 C3
2 C1
Head/Subhead
-1 B3
0 B2
2 B1
Logo
-4 A2
3 A3
8 A1
Background/Cover
18 Constant
TOT
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How about segments
We looked at naturally occurring groups of consumers– Segmentation based on pattern of hot buttons (response
to element features) Three segments
– Seg 1 (43%) = elaborate old houses– Seg 2 (23%) = clean, blue, New England colonial– Seg 3 (34%) = hates New England type colonial
Our research shows visual segmentation often not as clear nor compelling as text-base segments– Whole new area of insights
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What the different features contribute
1-4-2-2 C2
2-6-2-1 C3
3112 C1
-25-3-1 B3
13-30 B2
38-22 B1
Logo
-1880-4 A2
0-7103 A3
33158 A1
Background/Cover
22181318 Constant
S3S2S1TOT
Head/Subhead
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What about pair-wise interactions? How do we find interactions..if we don’t know
where they are?– Synergisms, suppressions– Virtually all conjoint methods have to ‘build in’ the
interactions to find them What about graphics interactions and the ‘art’
of design … can we discover them?– We’ll use the same empirical discover approach
that we did with cookies
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Interactions… Discovery system**
Use main effects design– With ‘zero conditions’
Permute design – Each respondent has different combinations
Put all data together from all respondents– Across all data you can estimate both linear and
significant pairwise interactions terms– Discover & estimate magnitude
**Patent pending**Patent pending
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Three permuted designs - example
C2C4C3
C3C2C2
C4C1C1
Category 3 – Head/Subhead
B1B2B4
B2B4B3
B3B3B2
Caegory 2 - Logo
A2A1A3House3
A3A2A2House2
A1A3A1House1
Category 1 – Background/Cover
Design 3Design2Design 1
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Linear Interaction
A1B3 ? -3
A2C2 ? 2
A3C2 ? -1
B1C2 ? -3
Only four significant interactions emergedNot big ones … and mostly negativeIn other studies we discover interactions > +15 or < -15
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In larger scale studies with hundreds of interactions .. Only 3% – 5% are significant
Strictly empirical ..which synergize, which suppress
SuppressionSynergismNo Interaction
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Response time as a new parameter
Question….. How much time is spent looking at a specific feature of the cover?– Approach … measure response time in 10ths
of seconds– Allocate response time to each element– Additive constant = ‘dead time’ that cannot be
allocated Use .. Engineer attention to the cover!!
– Whole new area for consumer research
606030 C3
11 C2
20 C1
-6 B3
-3 B2
3 B1
19 A3
25 A2
17 A1
19 CONSTANT
Response Time (10ths Seconds)
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What’s the bottom line?
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Systematics generate five knowledge benefits
1. Better data:
– why settle for guessing or focus groups when you get solid quantitative answers?
– better respondents experience
2. Clearer results: looking at the results gives you immediate insight and direction
3. Multi-media: whether concepts, packages … you get to test many stimuli
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Five key knowledge benefits (cont.)
4. Segmentation: you get to see new segments, and what turns them on
5. Synergies and suppressions: identify what works together, what doesn’t
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Systematics generate three process benefits
1. Democratizes the insights business: You don’t have to have years of experience to get clear direction from the data
2. Faster: Overnight vs. a few days (weeks)
3. Cheaper: About the price of a focus group fully analyzed (with segmentation)
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Contact Info:
Moskowitz Jacobs inc.White Plains, NY
Bert KriegerDanny MoskowitzDr. Howard R. [email protected](914) 421-7400
MJI’s website: www.mji-designlab.comIdeaMap.Net internet tool: www.ideamap.netIdeaMap.Net open-source innovation: www.innovaidonline.net