when the marketplace seems too big: using evoked sets to ... · when the marketplace seems too big:...
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When the marketplace seems too big: Using evoked sets to model how shoppers buy
SKIM
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Conjoint analysis used to understand tradeoffs
• Many shopper decisions involve
tradeoffs
• Conjoint analysis can be used to
understand and predict how shoppers
will make tradeoffs
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Some tradeoffs occur in a large competitive space
• A Grocery Store may have hundreds of SKUs relevant
to your category
• We can program realistic shelf sets where we vary
prices and products to understand tradeoffs
• But a computer screen is not a store
What if we have too many products
to show on a computer screen?
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Evoked sets can help when you have a large market space
• Most shoppers make tradeoffs
between a smaller set of products in
their consideration set
• For each respondent, we can customize
the conjoint screen to show only those
products that are relevant to them
>
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Additional Reasons to Use Evoked Set
Easier for
respondent to
focus
Respondent more
engaged
Survey seems
more relevant Better data quality
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How do we customize the products shown?Ask respondents to tell us what products are relevant to them
Past behavior Future behavior Required Features /
Unacceptable Features
Multiple screening criteria to avoid eliminating items hastily
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Custom shelf sets require programming expertise
Customized but Structured and Meaningful Shelf Set
Evoked from
multiple
screening
criteria
Random
non-evoked
products
Rules
apply
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Disadvantages of Evoked Set
We may be eliminating
some products the
respondent would buy
Introduces “Selection Bias”
must do more complex
modeling to account for this
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Evoked Sets Require Analytical Expertise1) Selection Bias
Most mathematical models assume
this missing data is missing at random
Raw conjoint data only shows that a
respondent was not shown certain items
Need to inform our predictive model that missing means “undesirable”
A. Add Synthetic Data
1. Add non-evoked items to model (not picked)
2. Define Threshold
> Evoked products beat a threshold
> Other products lose to threshold
B. Respondent Level Penalized
Regression
> Individual level constraints
> Can set predictions at 0
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Explanation of threshold
-
+
-
+
threshold threshold
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Evoked Sets Require Analytical Expertise2) Large Marketplace Means Sparsity of Data
Sparsity Easy to overfit
the data
Calibrate/Tune
model for sparsity
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Evoked Sets Require Analytical Expertise3) Large Marketplace Typically Has Nesting Structure
Some items are grouped together as more similar to
each other more likely to choose between these
Brand
A
Diet Not-Diet
Brand
B
Size1 Size2 Size3
Use Nested Logit or similar approach
Ensembles of Different Nests
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Conclusion
Evoked Sets Enable Us to Study
a Large Marketplace of Products
> Survey customized to respondent
> More engaged respondents
> Requires programming expertise
Evoked Sets Require Careful Screening
> Adding other products to evoked set is recommended
Evoked Sets Require Analytical Expertise
> Solutions to Selection Bias
> Calibrate for Data Sparsity
> Model Natural Groupings or Nests
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Kees van der WagtSenior Research DirectorBased in [email protected]
Kevin Lattery
VP Methodology & Innovation
Based in New York
Contact us
skimgroup.com
@SKIMgroup
SKIMgroup
SKIMgroup
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