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MRS Advanced Analytics Innovation Symposium
10th November 2016
#MRSlive
68 Lombard Street, London EC3V 9LJ Tel: 0870 787 4490
Modelling insurance online buying behaviour
Presentation to
MRS ADAN Network Symposium
November 2016
Kathy Ellison & John McConnell
What we are covering
3
The context The challenge Translating insurance product choice into Conjoint
The testing challenge The conjoint task – a standard CBC or something more? Why Conjoint & advantage over solely testing live
What we learnt Conclusions to the client When it doesn’t match what consumer says Being pragmatic
The challenge
4 |
The challenge we were givenIncrease buying of the ‘premium’ home insurance product once clicked through to site from Price Comparison Website (PCW)
Key questions were: 1. What is the right price differential? (when premium is personal)2. What is the optimal cover combination?3. Should premium product be shown 1st or last? 4. Should we show 2 or 3 products on the screen?
Online survey 500 home insurance switchers who have used a PCW or planning
to do so In 3 parts
1. An exploration of the product in context of a PCW2. A Trade off exercise that replicates real life purchasing
decisions3. A deep dive into the detailed features
1.
3.
2.
Translating insurance product choice into Conjoint
5
12 screens each with 2 or 3 ‘randomly produced’ products Combination of 7 cover features with 2 or 3 levels of each Premium of each ‘product’ was automatically calculated using ‘real’ prices Respondents chose between products on each screen in turn
Choices fed into a model which calculated:– how decisions were being made – influence on choice of the different features of cover
1. Compulsory Excess £50 (Increases price by x%) £100 (No addl cost)
2. Buildings Sums Insured* £A,000 (reduces price by 5%)
£B,000 (No addl cost) £C,000 (Increases price by 5%)
£D,000 (Increases price by 10%)
3. Contents Sums Insured £E,000 (No addl cost) £F,000 (Increases price by 5%)
£G,000 (Increases price by 10%)
4. Feature A (Optional) (No addl cost) (Included) (Increases price by £15)
5. Feature B (Optional) (No addl cost) (Included) (Increases price by £20)
6. Feature C (Optional) (No addl cost) (Included) (Increases price by £20)
7. Feature D (Optional) (No addl cost) (Included) (Increases price by £35)
It was explained that a tick represented the element was included
It was explained that a cross represented the element was optional
The testing challenge:
6
A standard CBC
1. Dynamic Pricing
2. Optimising the product
3. Testing Order
4. Testing 2 versus 3 choices
The testing challenge:
7
• We want to understand share of preference
• 7 attributes• 4 levels max• 8 random tasks, 3 tasks per
concept• 2 fixed tasks
• The current “Standard” product• A current “Premium” product
• We export the design and our ops agency programs it into the main survey
A standard CBC
The testing challenge:
8
• Each respondent has a different anchor point
• We need to calculate a total price (not a tested attribute) from other pricing level in other attributes
1. Dynamic Pricing
• To answer the question “what is the product with the highest increase in preference relative to the standard product?”
• This was an additional calculation on top of the interactive simulator
• Essentially a programmatic way of testing all choice combinations
2. Optimising
The testing challenge:
9
• We used the fixed tasks• Comparing “standard” and
“upgrade” products• Presented in different orders
3. Testing Order
• Sawtooth doesn’t let us test variable numbers of concepts per task
• So we create a 2nd separate project and randomly assigned 2 concepts per task back into the main project rotations
4. Testing 2 versus 3 choices
Pos 1 Pos 2 Pos 30%
10%
20%
30%
40%
50%
60%
0.27 0.2310.2290.16
Testing Order
Standard Control Premium
Why Conjoint & advantage over solely testing live
10
Here we are looking at product sales specifically through the client’s website
Platforms like “Optimizely” offer A/B Testing or Multivariate Testing (“MVT”) on live web sites (or other digital devices).
Based on Design of Experiments. Typically applying either GLM, MANOVA or Taguchi methods.
So in theory we could have tested in the live environment.
Reasons not to use this approach include:
• We will impact revenue • We may confuse customers• Overall it is likely to be more expensive than a study
What we learnt: Conclusions
11|
Key questions were: 1. What is the right price differential? (when premium is personal)
2. What is the optimal cover combination?
3. Should premium product be shown 1st or last?
4. Should we show 2 or 3 products on the screen?
What we learnt: Conclusions
12|
Answers were: 1. What is the right price differential? (when premium is personal)
No ‘optimum’ price differential to make people think ‘I might as well upgrade’. Decision made based on a trade off between incremental price and inclusions of cover.
2. What is the optimal cover combination?A ‘premium’ product, must differentiate from the ‘basic’ in a popular way – the mix of covers makes a big difference (20% more preference). & do not offer too high a SI limit.
3. Should premium product be shown 1st or last? If premium product is shown first more likely to choose this
4. Should we show 2 or 3 products on the screen?Offering a choice of 3 products rather than 2 does not encourage more to upgrade, provided that the 2nd, more comprehensive, option has the most popular cover elements
What we learnt: Matching reality & being pragmatic!
13
Model tried to replicate reality but….. Premium product chosen in model in 56% of cases BUT was the stated choice only in 25% of cases Why?
– Model simpler (7 vs 15 features) ?– Price differential lower?– Consumers not logical - often CHEAP wins over quality?
And some conclusions are just not practical…. We concluded that price anchoring had an impact - the jump from the ‘basic’ (shown in the
PCW) to the ‘premium’ can be too great to encourage upgrades so they could:– LOWER the price of premium product & reduce covers – NOT recommended– RAISE the price of basic product –recommended
However they would never do the latter – PCWs must show the CHEAPEST price possible!
14© GfK 2016 | Turbo Event | February 2016
Willingness to Pay in the Conjoint Space
Chris MooreGfK UK
ADAN Innovation Symposium – Conjoint AnalysisNovember 10th
15© GfK 2016 | Turbo Event | February 2016
What is WTP
16© GfK 2016 | Turbo Event | February 2016
Jedidi/Zhang (2002), S. 1352.
What we talk about when we talk about WTP
“Price at which the consumer is indifferent between buying and not buying the product,
given the alternative(s) available”
17© GfK 2016 | Turbo Event | February 2016
Markets are full of alternatives
18© GfK 2016 | Turbo Event | February 2016
WTP - There are three different terms to distinguish
Reservation Price
=Maximum Price
=Willingness-to-Pay
= min{ }Breidert (2006)
19© GfK 2016 | Turbo Event | February 2016
Apple vs Samsung
• A famous example is the $2.5 billion law suit against Samsung regarding infringement of patents
• Apple commissioned 2 conjoint studies to quantify the damages (one on iPhones and the other on Tablets). Both
contained 7 attributes and 16 choice tasks
• Real-life dynamics of what people are willing to pay is more complex as it needs to take in to account the supply part
of the equation as well as the demand side (called the equilibrium price)
• Analyst was tasked with calculating the WTP for the demand side of the equation. Other experts provided the Supply
part of the equation
• Apple were awarded $1 billion (at the initial hearing)
http://www.sawtoothsoftware.com/download/apple_v_samsung_conjoint_analysis.pdfhttp://www.sawtoothsoftware.com/support/technical-papers/general-conjoint-analysis/assessing-the-monetary-value-of-attribute-levels-with-conjoint-analysis-warnings-and-suggestions-2001
20© GfK 2016 | Turbo Event | February 2016
Gilligan’s Island
http://www.sawtoothsoftware.com/support/technical-papers/general-conjoint-analysis/assessing-the-monetary-value-of-attribute-levels-with-conjoint-analysis-warnings-and-suggestions-2001
• One of the most important aspects within WTP is the notion of
competition
• WPT will vary depending on what the competition is !
• The Sawtooth paper discusses the WTP of a person trying to
escape Gilligan’s island when there is only one way of escape
versus when there are multiply ways to escape
21© GfK 2016 | Turbo Event | February 2016
Common WTP methods
22© GfK 2016 | Turbo Event | February 2016
Approach 1: Post-hoc monetary scaling of utilities
Individual part worth utility structures… …can be converted into utility differences.Brand A Brand B Brand C
Brand A 3.7 8.9
Brand B -3.7 5.2
Brand C -8.9 -5.2
By using a linear price parameter… …utility differences can by scaled in monetary units.
Brand A Brand B Brand C
Brand A 37 € 89 €
Brand B -37 € 52 €
Brand C -89 € -52 €
10€ Util
Orme (2001).
Brand A Brand B Brand C
-6
-4
-2
0
2
4
64.2
0.5
-4.7
Utili
ty
€ 10 € 30 € 50
-2.5
-2.0
-1.5
-1.0
-0.5
0.0
0.5
1.0
1.5
2.0
2.52.0
0.0
-2.0
23© GfK 2016 | Turbo Event | February 2016
Approach 1: Post-hoc monetary scaling of utilities
WTP CONCEP MEASUREMENT OBJECTSTRENGTHS No special software required Easy to implement
WEAKNESSES Assumption that Price is linear
Not linked to a specific product
Competition not included
Not linked to actual purchase behavior (e.g., none option is not considered).
Respondents that react highly insensitive to price will bias the estimates upwards (cleaning procedure required).
Does not conform to the definition to WTP
24© GfK 2016 | Turbo Event | February 2016
Approach 2: Market compensation approach
Sim. Share
Price£21,000
20%
Price-Premium:
£600
£21,600
Automotive example: Adding Air Con to the trim line
No Aircon Aircon
25%Aircon
Orme (2001).
25© GfK 2016 | Turbo Event | February 2016
Approach 2: Market compensation approach
WTP CONCEPSTRENGTHS Linked to a specific product
Realistic competitive environment
Easy to interpret
No special software required
WEAKNESSES WTP estimation relates only to the specific
product tested
Not based on the idea of an Indifference point but at the point at which the share of a product returns to the same value
Trial and error to find exact price difference
Not guaranteed that change in share can be compensated within the given price range
While taking in to account individual respondents, analysis is typically based at the aggregate level
26© GfK 2016 | Turbo Event | February 2016
A
e.g. Miller et al. (2011)
Approach 3: Individual point of indifference analysis
Utility Respondent
1
Price HighLow
None
B
C
TEST
TESTHighPrice
Utility Indifference!
WTP
27© GfK 2016 | Turbo Event | February 2016
Approach 3: Individual point of indifference analysis
How to arrive at WTP on level-basis for a certain individual:
Run point of indifference analysis for every combination of attribute levels
Contrast the WTP for test products with a certain feature
WTPi = £20,000 WTPi(blue) = £22,000
Premiumi(blue - grey) = +£4,000
WTPi(grey) = £18,000
28© GfK 2016 | Turbo Event | February 2016
Approach 3: Individual point of indifference analysis
WTP CONCEPSTRENGTHS Takes into account a pre-defined competitive
environment
WTP estimates are not linked to a specific product
Based on the definition of WTP (indifference point)
Less prone to outliers: WTP estimates are restricted to a pre-defined range
Not prone to human error
Can fix attribute levels to be static and can respect alternative specific rules
WEAKNESSES Client need to be educated on how to interpret
the resulting estimates correctly
Problems may arise if many respondents are insensitive to price or the competitive scenario contains dominating or dominated alternatives.
Specialist routines need to be developed (in R typically)
29© GfK 2016 | Turbo Event | February 2016
Other approaches
Sonnier, G., Ainslie, A., Otter, T. (2007), Heterogeneity Distributions of Willingness-to-Pay in Choice Models, Quantitative Marketing & Economics, 5, 3, 313–331.
Allenby, Greg M., Jeff D. Brazell, John R. Howell, Peter E. Rossi, 2014. “Economic Valuation of Product Features” Quantitative Marketing and Economics 12:421-456 Allenby, Greg M., Jeff Brazell, John R. Howell, Peter E. Rossi, 2014. “Valuation of Patented Product Features” Journal of Law and Economics 3:629-663
Sawtooth Software (2012)
30© GfK 2016 | Turbo Event | February 2016
Observations
WTP is no objective measurement concept. The outcome is strongly based on assumptions. Make this assumptions transparent for you client and help them to interpret the delivered figures correctly
The monetary scaling approach should not be used as it is likely to produce extreme results and lacks theoretical foundation
The individual point of indifference approach tends to lead to more conservative estimates but is the only approach that measure the true definition of WTP
The price attribute should be constrained to avoid scenarios of infinite WTP
Generally advised to use the Median WTP rather than average WTP
31© GfK 2016 | Turbo Event | February 2016
Do not just give 1 WTP figure. Obtain different WTP results with different competitive context and give a range of WTP outputs
WTP results can be heavily affected by dominated and/or dominating competitors and will skew results
Keep in mind that you are calculating WTP from stated preferences. There is always a hypothetical bias which flaws the measured WTPs*
Has the conjoint produced an accurate measure off price sensitivity?
Have the relevant attributes been included?
Have the relevant competitors been included?
Observations
*Orme (2001).
32© GfK 2016 | Turbo Event | February 2016
Discussion
33© GfK 2016 | Turbo Event | February 2016
ReferencesBreidert, C. (2006), Estimation of Willingness-to-Pay, Theory, Measurement, Application, 1st ed.
Jedidi, K., Zhang, Z. J. (2002), Augmenting Conjoint Analysis to Estimate Consumer Reservation Price, Management Science, 48, 10, 1350–1368.
Miller, K. M., Hofstetter, R., Krohmer, H., Zhang, Z. J. (2011), How Should Consumers' Willingness to Pay Be Measured? An Empirical Comparison of State-of-the-Art Approaches, Journal of Marketing Research, 48, 1, 172–184.
Orme, B. K. (2001), Assessing the Monetary Value of Attribute Levels with Conjoint Analysis: Warnings and Suggestions, Sawtooth Software Research Paper Series.
When the marketplace seems too big: Using evoked sets to model how shoppers buyKees van der Wagt | Senior Director Methodology & InnovationNovember 2016
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
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?
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
>
Additional Reasons to Use Evoked Set
Easier for respondent to
focus
Respondent more engaged
Survey seems more relevant
Better data quality
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
Custom shelf sets require programming expertise
Customized but Structured and Meaningful Shelf Set
Evoked from multiple
screening criteria
Random non-evoked
products
Rules apply
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
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 has not seen certain items
Need to inform our predictive model that missing means “undesirable”
A. Add Synthetic Data1. 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
Explanation of threshold
-
+
-
+
threshold threshold
Evoked Sets Require Analytical Expertise2) Large Marketplace Means Sparsity of Data
Sparsity Easy to overfit the data
Calibrate/Tune model for sparsity
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
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
Contact meKees van der WagtTel: +31 10 282 3500email: [email protected]@skimgroup
The impact of choice environment on choice behavior
Studio GerARTGerard Loosschilder
ONCE UPON A TIME …
ONE DAY ….
Animal Welfare Conference
Call to action
Hotel Chain, Type & Style
Distance to city center
Placement of 50 hotel on the results page - top to bottom Review Score on cleanliness, staff and
facilities; result in a mean score and a label
Including room price per night
MEANWHILE @ THE BOOKING SITE
CHARLOTTE, INTERACTION DESIGNERIntroducing
Filter functions on price and ratings
Sort functions on price and rating
BACK TO ASTRIDOne year later
THE RESULTS ARE IN!Meanwhile, back in the office …
Of those having the functions available
67% uses sort and/or filter functions at least once across the four tasks47%
uses the filter function at least once42%
uses filter on price
27%uses filter on rating
40%uses the sort
function at least once34%uses Sort on price
11%uses Sort on rating
33%does not use the functions, not even once
Ideal situation
50
47
44
41
38
35
32
29
26
23
20
17
14
11
8
5
2
-2% 0% 2% 4% 6% 8% 10% 12%
Likelihood of chosing a room
Posi
tion
on th
e re
sults
pag
e
Situation before redesign
50
47
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Likelihood of chosing a room
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Sort & filter made available
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44
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38
35
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Likelihood of chosing a room
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page
If sort & filter are used
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41
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35
32
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If sort & filter are not used
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32
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Likelihood of chosing a room
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All compared
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Likelihood of chosing the room
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Top box satisfaction
if sort and filter are ...Not available:
56%
Available:
62%
OSCAR STRIKES BACKBut then,
Average room price
if filter is... Not available€123
Available€116Not used€126
Used€102
Use of the none option, if filter wasNot available:
12%
Not used: 12%
Used: 17%
WHO DID WIN?So what?
Flatter distribution of choices Higher task satisfaction
Lower room prices Higher drop-out rates
Flatter distribution of choices Higher task satisfaction
Lower room prices Higher drop-out rates
WHAT’S IN IT FOR YOU?Dear Colleagues
Dude it’s just a story
CHANGEAHEAD
Stakeholderthe market
Stakeholder, protagonist
Stakeholderantagonist
Experiment with usGerard Loosschilder, Paolo Cordella,
Jean-Pierre van der Rest and Zvi Schwartz