Pricing 301: Going Beyond Preference Modeling to Improve Pricing Decisions
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Transcript of Pricing 301: Going Beyond Preference Modeling to Improve Pricing Decisions
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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
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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
Field of View:45x45x40
High Resolution Gradients:
180Price:
$800,000
Going Beyond Preference Modeling
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Method 2: Choice-based tasks
Conjoint Measurement
Brand:Siemens
Field of View:50x50x45
High Resolution Gradients:
120Price:
$1,000,000
Brand:Philips
Field of View:45x45x40
High Resolution Gradients:
180Price:
$800,000
Brand:General Electric
Field of View:50x50x45
High Resolution Gradients:
180Price:
$1,200,000
Going Beyond Preference Modeling
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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
Going Beyond Preference Modeling
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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
Going Beyond Preference Modeling
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The reality check:
Preference model as a sales predictor
What can explain the difference?
Going Beyond Preference Modeling
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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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Addressing the Problem:
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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Addressing the Problem:
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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Addressing the Problem:
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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Addressing the Problem:
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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Addressing the Problem:
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
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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