Pricing 301: Going Beyond Preference Modeling to Improve Pricing Decisions

MEASURING the VOICE-OF-CUSTOMER in HEALTHCARE

Pricing 301: Going Beyond Preference Modeling to Improve Pricing Decisions

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Scott Davis, PhDDr. Davis is the founder of the consultancy Strategic Marketing Decisions and has assisted TMTG with quantitative analytics and modeling for over fifteen years.

Before founding SMD he served on the marketing faculties of the Olin School of Business at Washington University in Saint Louis and the Graduate School of Management at University of California, Davis. He has also taught MBA pricing and economics classes at a number of the country’s leading business schools at campuses that include University of California Berkeley, UCLA, the University of Minnesota, the University of Rochester and Stanford University.

He has written scholarly articles that have appeared in the Journal of Marketing Research, Journal of Retailing, and the Harvard Business Review, among others. One of the articles he co-authored, “How to Fight a Price War” is a Harvard Business School Publishing best-seller.

Presenting and our Guest

Moderator:Christian Renaudin

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Small Pricing Errors Have a Significant Profit ImpactFor a typical S&P 1500 company a 1% increase in price will lead to an 8% increase in profitability if sales volume can be maintained

Revenues Fixed Costs Variable Costs Operating Profits

10019.2

68.3

14.5

1.0%Price Increase

101.01

8.0%Profit Increase

13.51

* Source: McKinsey analysis

Typical Scenario:

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TODAY’S WEBINAR GOAL: “Beyond standard preference modeling”

To show how one can improve customer choice models so they correspond more closely to real market behavior than

standard preference models

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Going Beyond Preference ModelingModeling pyramid

Goals for using Market Models: Improve pricing and product design decisions by improved estimates of customer response to changes in them

Price

Optimizatio

n

Market Model

Preference Model

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PRICE SENSITIVITY MEASUREMENT

DirectQuestioning

Monetary (Economic)

Value AnalysisTrade-off / Conjoint

Going Beyond Preference ModelingMeasurements Approaches

Method 1: Paired comparison tasks (Example: a scanner)

Conjoint Measurement

Brand:Siemens

Field of View:50x50x45

High Resolution Gradients:

120Price:

$1,000,000

Brand:Philips



180Price:

$800,000

Going Beyond Preference Modeling

Method 2: Choice-based tasks

Conjoint Measurement

Brand:Siemens



120Price:

$1,000,000

Brand:Philips



180Price:

$800,000

Brand:General Electric



180Price:

$1,200,000


Conjoint Value Estimates: “part-worth” utility values

The utility of a product offering is computed by adding the part-worth utility estimates of the attribute values associated with it

Siemens Philips GE 45x45x40 50x50x45 55x55x50

800,000 1,000,000 1,200,000 120 180


o Expected Utility Maximization– Customers make random errors in estimating the utility of alternatives– Example: Logit Models

– Where is an estimated parameter reflecting choice or model accuracy (ideally estimated based on past choices)

We create a Preference Choice Model

Utility of Product 1

Utility of Product Utility of No Purchase

Choice Set

Pr (Choose Product 1) i

i

ee e

Us

Us + The Alternatives


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The reality check:

Preference model as a sales predictor

What can explain the difference?


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Biases can result from a poorly designed preference study:

• Omitted or misspecified attributes or levels• Improper framing of attribute values (particularly price)

– Reference price effects– Framing price as the customer views it when making purchase

decisions

Going Beyond Preference ModelingPotential preference measurement design issues

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Crossing the Preference Chasm: From Preference to Market Modeling

Preference Models

MarketModels

o Preference is one of multiple factors influencing choice

o Other relevant factors include:• Marketing activities• Purchase decision processes

o When possible are calibrated to actual purchase data

In many cases preferences are imperfect predictors of actual choiceThere are higher mountains to climb to get accurate predictions!So we need to cross the chasm!

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It is worth the extra investment to improve prediction accuracy

o A first look at an advanced market simulator:

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Issues to Address to Improve PredictionAccounting for Marketing Influences

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Not Accounting for Perceptions or Marketing InfluencesIssue #1: Customer purchase likelihood is likelyto be influenced by marketing and/or sales activities

o Advertising builds:• Awareness • Salience of products and their features

o Sales forces:• Inform customers of a specific

alternative’s offers • Directly encourage purchases • Facilitate the purchase process

WHY IS THIS A PROBLEM?• Different products receive different levels of advertising support• Product benefit communication effectiveness vary by vendor• Resellers can influence sales of one product over another if margins

are significantly different• Some products are readily promoted through bundling or discounts

PREFERENCE MODELS WILL SYSTEMATICALLY UNDERESTIMATE:• Sales of products that are heavily advertised or that receive more

effective sales support • Sales of alternatives that are more effectively bundled or otherwise

discounted

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Addressing the Problem:

Accounting for Marketing Influences

o Measure familiarity with marketing activitieso Measure quality of relationship with the salesforce and

customer service repso Obtain marketing or sales expenditure estimates from

industry analysts

Measure marketing or sales force

performance

o Statistical models can estimate the impact of marketing variables when historical sales data is available

o Choose a model based on reasonableness and improvement in predictive accuracy

Use measures of marketing activity in

model calibration

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Issues to Address to Improve PredictionAccounting for Perceptions and Decision Processes

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Not Accounting for Individual Decision Processes

Issue #2: Some brands may not be in the choice set

o Why?• Customers may not be aware of the product• Their chosen vendor may not carry the product or display it

effectively• May be incompatible with technology they currently use or

may have a “deal-breaker”o Pricing implications

• Price changes won’t influence sales among such customers unless doing so brings the product/brand in the choice set

• Non-price marketing efforts may need to be required to get the product considered

THIS MAY BE A PROBLEM WHEN:• Product purchase decisions are routinized and customers don’t seek

out alternatives• Different resellers offer different product selections or have different

markup or discount policies

PREFERENCE MODELS FALL SHORT BY OVERESTIMATING:• The attractiveness of unfamiliar or new alternatives• Sales of products with limited distribution

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Accounting for Choice Sets

o Measure product familiarityo If offerings vary across vendors identify which vendors

are usedo Ask customers how they determine which options to

consider

Survey about alternatives considered

o Exclude options that a customer would not consider in his/her choice set

o In market model eliminate alternatives that have deal-breaking characteristics

Modify the choice sets on the individual

level

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Not Accounting for Individual Decision ProcessesIssue #3: Decision-makers will often take steps to reduce their decision-making effort

THIS MAY BE A PROBLEM WHEN:• The product is complex (has a lot of potentially relevant

attributes) or product category is not “mission critical”• The decision-makers are not the primary users• The alternatives are categorized and categories are screened

based on one or more attributes (such as manufacturer) and only alternatives within the selected categories are considered

PREFERENCE MODELS FALL SHORT BY:• Overestimating the value of non-critical features and of new

features (unless heavily promoted)• Underestimating the sensitivity to price

o Customers may not consider all attributes• Products often have many relevant attributes but

customers often only consider a few of them• Economic value to the customer and conjoint analyses

will often over-estimate the importance of less-important attributes

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Accounting for Choice Sets

o Begin preference measurement with self-stated attribute importance

o When many attributes may be relevant show only the attributes that are important to the individual

Identify relevant

attributes

o Assume a zero utility weight for the least important attributes

Model preferences

based on only the most important attributes

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Not Accounting for Individual Decision ProcessesIssue #4: Nested Decision-Making

• Consider a restaurant that servers hamburgers and hot dogs Assume price is set so that the customer is

indifferent between them• What is the probability of the customer’s

choosing each alternative?• What happens if a cheeseburger is

added to the menu? Assume the price of the cheeseburger is just high

enough to leave the consumer indifferent between all three alternatives

• What is the probability of the customer’s choosing each alternative?

Hamburgers

Hot Dogs

50%

50%

HamburgersCheeseburgers

Hot Dogs

33.3%33.3%

33.3%

HamburgersCheeseburgers

Hot Dogs

or

25%25%

50%

o Customers simplify decisions by breaking alternatives down into categories first:

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Accounting for Nested Decisions

o Market shares or survey responses may reveal a nested decision process

Choosing manufacturer before deciding on preferred offering

Choosing vendor then choosing from alternatives offered Choosing among product types then preferred option

Survey about decision process

o Nested logit models are commonly used Based on the model that is consistent with stated

decision processes Based on the model that provides the best fit with actual

purchase data

Model sequential

choices

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Complexities of Group Decision ProcessesIssue #5: multiple parties influencing decisions

o Different parties may place different values on different features

o If you are estimating the value of only one party, your choice predictions will be biased

THIS MAY BE A PROBLEM WHEN:• The final decision-maker is heavily influenced by requests or

recommendations of primary users of the product or service• Purchase decision influencers have different priorities • Senior management or other may veto or discourage some alternatives

PREFERENCE MODELS FALL SHORT BY:• Providing biased sales and price sensitivity estimates by only addressing

the preferences of a single party• Failing to account for the interactions among interested parties

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Accounting for Multiple Decision Influencers

o Identify key influencers and their role in the decision process

o Identify decision criteria for key influencersSurvey about decision-

making process

o Often this not the final decision maker but may have a big influence on them

o Solicit their insights about views of other decision influencers

Focus attention on the party with the greatest

influence on the purchase decision

o Confirm the views of the primary decision influencer by interviewing or surveying other influential parties

Measure norms of other decision

influencing parties

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Accounting for Multiple Decision Influencers

o Have the relevant parties take a survey jointlyo Have the relevant parties take a survey independently -

Estimated utility can be approximated by a weighted average of the respondents

Estimate a joint utility function

o View one party as reducing the choice set by eliminating unacceptable alternatives

o The second party can be viewed as choosing among the alternatives in the reduced choice set.

o Example in Medical Equipment: Administrator sets maximum cost or minimum return on

investment Practitioner chooses from alternatives that satisfy the

administrator’s criteria

Treat the decision as a sequential process

(e.g. with two decision makers)

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Summary

Effective demand modeling can improve on preference-driven modeling by accounting for factors that impact real-world purchase decisions

1. Marketing and sales activities

2. Reduced choice sets

3. Simplified decisions

4. Nested decisions

5. Multiple decision makers

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It is worth the extra investment to improve prediction accuracy

o How the simulator went beyond preference modeling:

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Things to Remember

A small improvement in price can have a

substantial impact on profit!

o Traditional preference modeling methodologies are a good first step, but …they may provide predictions that differ significantly from what is seen in the marketplace

o Effective market modeling can significantly improve predictive accuracy and improve pricing decisions

MARKETS SERVED

MEDICAL IMAGING

METHODOLOGIES

GO-TOMARKET

EXECUTION

SALES & MARKETINGCOMMUNICATION

PRICINGOPTIMIZATION

NEWPRODUCTROADMAP

MARKETOPPORTUNITYASSESSMENT

Qualitative

MEDICAL DEVICES AND DIAGNOSTICS HEALTHCARE IT PHARMACEUTICAL INDUSTRIES TECH TRANSFER

MEASURING the VOICE-OF-CUSTOMER in HEALTHCARE

Quantitative