10 nov 16 adan

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

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

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

Translating insurance product choice into Conjoint

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:

A standard CBC

1. Dynamic Pricing

2. Optimising the product

3. Testing Order

4. Testing 2 versus 3 choices

• 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

• 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

• 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%

0.27 0.2310.2290.16

Testing Order

Standard Control Premium

Why Conjoint & advantage over solely testing live

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

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

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!

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

What is WTP

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”

Markets are full of alternatives

WTP - There are three different terms to distinguish

Reservation Price

=Maximum Price

=Willingness-to-Pay

= min{ }Breidert (2006)

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

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

Common WTP methods

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

€ 10 € 30 € 50

2.52.0

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

Approach 2: Market compensation approach

Sim. Share

Price£21,000

Price-Premium:

£21,600

Automotive example: Adding Air Con to the trim line

No Aircon Aircon

25%Aircon

Orme (2001).

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

e.g. Miller et al. (2011)

Approach 3: Individual point of indifference analysis

Utility Respondent

Price HighLow

TESTHighPrice

Utility Indifference!

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

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)

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)

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

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).

Discussion

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

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: k.vanderwagt@skimgroup.comwww.skimgroup.com@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

-2% 0% 2% 4% 6% 8% 10% 12%

Likelihood of chosing a room

Situation before redesign

-2% 0% 2% 4% 6% 8% 10% 12%

Sort & filter made available

-2% 0% 2% 4% 6% 8% 10% 12%

If sort & filter are used

0% 2% 4% 6% 8% 10% 12%

If sort & filter are not used

0% 2% 4% 6% 8% 10% 12%

All compared

0% 2% 4% 6% 8% 10% 12%

Likelihood of chosing the room

Top box satisfaction

if sort and filter are ...Not available:

Available:

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:

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

gerard.loosschilder@gmail.comwww.studiogerart.com

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