When you want to understand the market dynamics among hundreds of products with different sizes and prices, we're typically forced to simplify reality for it to fit into a research. However, we should be aware not to oversimplify too much, as we risk losing out on important insights. Think, for example, about the number of soft-drink options or the number of snacks possible in a large competitive space. Consumers might switch between any number of products available; over-simplifying the market would result in us not accurately capturing interaction between products. That's why we argue that you should try to include, as much as possible, the full market in studies, with all the options that are available to consumers and shoppers. However, due to the limitations of screen real estate in a conjoint survey, we sometimes need to limit what we show to each individual. That's where evoked set comes in. For any given consumer, there is a subset of products from which they actually make trade-offs. With evoked set, we can find out what products make up a specific respondent's consideration set and build a custom conjoint task. In this webinar, we'll share how we design a conjoint study with evoked sets using a well-thought-out approach to experimental design and expertise. Find out more at http://skimgroup.com/webinar-evoked-sets.

1. When the marketplace seems
too big: Using evoked sets to
model how shoppers buy
SKIM

2. 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

3. 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?

4. 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
>

5. Additional Reasons to Use Evoked Set
Easier for
respondent to
focus
Respondent more
engaged
Survey seems
more relevant
Better data quality

6. 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

7. Custom shelf sets require
programming expertise
Customized but Structured and Meaningful Shelf Set
Evoked from
multiple
screening
criteria
Random
non-evoked
products
Rules
apply

8. 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

9. Evoked Sets Require Analytical Expertise
1) 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

10. Explanation of threshold
-
+
-
+
threshold threshold

11. Evoked Sets Require Analytical Expertise
2) Large Marketplace Means Sparsity of Data
Sparsity Easy to overfit
the data
Calibrate/Tune
model for sparsity

12. Evoked Sets Require Analytical Expertise
3) 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

13. 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

14. Kees van der Wagt
Senior Research Director
Based in Rotterdam
k.vanderwagt@skimgroup.com
Kevin Lattery
VP Methodology & Innovation
Based in New York
k.lattery@skimgroup.com
Contact us
skimgroup.com
@SKIMgroup
SKIMgroup
SKIMgroup
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