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© Arvind Rangaswamy 2017, All Rights Reserved
April 4, 2017
MKTG 555: Marketing Models
Decision Models in Marketing
Discussion
Kannan et al. paper 2009
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© Arvind Rangaswamy 2017, All Rights Reserved
NAP Business Model Over Time
1996
Sell
online
free
browsing
2003
Sell pdf
bundle
online
2005
Provide
free pdf
of slow
movers
2014
Free pdf
of all
titles
2016
Freemium
experiment
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© Arvind Rangaswamy 2017, All Rights Reserved
Initial Research Questions National Academy Press
How to price the different formats, what type of bundling strategy (if at all)?
Introducing pdf format for the first time in 2002
How should it be priced?
Should bundles be offered?
Are formats complements or substitutes?
Heterogeneous across customers?
How to position the formats?
What role do usage situations play?
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© Arvind Rangaswamy 2017, All Rights Reserved
The Model Customers’ utilities for Print, PDF and the Bundle
j = {1 = Print, 2 = pdf} 𝑼𝒊𝒋 = 𝜷𝒊𝒋𝑿𝒊 − 𝜷𝒑𝒊𝒑𝒋 + 𝜺𝒊𝒋
𝑿𝒊: Customer i’s degree of fit of content to his/her needs
𝜷𝒊𝒋: Value customer i places on product form j
𝜷𝒑𝒊: Price sensitivity for customer i
𝑼𝒊𝒋 = 0 for free browsing (i.e. customer does not buy either i or j)
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© Arvind Rangaswamy 2017, All Rights Reserved
The Model Customers’ utilities for Print, PDF and the Bundle
𝑼𝒊𝒃 = 𝜷𝒊 +𝜷𝒊𝟏 +𝜷𝒊𝟐 𝑿𝒊 − 𝜷𝒑𝒊𝒑𝒃 + 𝜺𝒊𝒃
𝒊: −min 𝜷𝒊𝟏, 𝜷𝒊𝟐 < 𝒊 < 𝟎: Incremental value of bundle,
which measures complementarity perceptions.
Consumers
with books in
shopping cart
Intercept and
present details
of pdf or book
Would you
like to order
pdf now?
Reduce pdf
price to one
level lower
Complete
pdf order
Complete
print order
Short
Survey (A)
For Add’l.
Discount
Short
Survey (B)
for free
shipping
1st
NO
Just pdf Just print
Go back
Complete pdf
& print order
Short
Survey (A)
For free shipping
and add’l. discount
Both pdf
and print
2nd NO
(No pdf
No print)
Group A
Consumers
browsing books
with pdf version
available
2nd
NO
Short
Survey (B)
for free
totebag
Continue Checkout Process
Group B
Online Choice
Experiments
at NAP
Answering the Pricing Question
Model customer
preferences for
individual forms
and bundle
Expected market
penetration of
forms, bundle
within segments
Determine
optimal prices for
forms and
bundle
Choice Data
Derive
optimal pricing
policy for
pdf and bundle
Implementation
in April 2003
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© Arvind Rangaswamy 2017, All Rights Reserved
Some General Findings
Mixed-bundling strategies are optimal
Customers are heterogeneous with respect to their complementarity-substitutability perceptions
Degree of perceived complementarity -
Accounting this heterogeneity is important for developing optimal policies
Model predicted the incremental demand/sales from pdf quite well.
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© Arvind Rangaswamy 2017, All Rights Reserved
NAP Implementation
PDF format introduced in April 2003
Price 75% of print price
Bundle 120% of print price
2500+ titles; free browsing still available
Revenues increased by 10% after controlling for introduction of new titles
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© Arvind Rangaswamy 2017, All Rights Reserved
Newer Titles
Their sales seem to show exponential decay . Assess how decay rate affected by intervention.
Before
During
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© Arvind Rangaswamy 2017, All Rights Reserved
Follow-up Questions
How to design the various digital content formats in terms of
their attribute quality and features?
How should they make the formats more complementary?
• Customer’s perception of complementarity versus
substitutability -
Impacts relative preferences of formats and bundles
How to influence the degree of complementarity?
To make the bundle more attractive
How can the firm influence this
Attribute qualities of the different formats - similarities
Usage situations – distinctive versus common usage
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© Arvind Rangaswamy 2017, All Rights Reserved
Findings Koukova, Kannan, & Kirmani
Journal of Marketing Research (2012)
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© Arvind Rangaswamy 2017, All Rights Reserved
Implications
Both formats have to be equally high on common attribute quality levels for the distinctive attributes to become salient in the bundle.
If one format dominates the other on a common attribute, bundle purchase is less likely.
Customers consider the option value of formats in making their decisions
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© Arvind Rangaswamy 2017, All Rights Reserved
“Take-away”s…
Customers are heterogeneous with respect to their complementarity-substitutability perceptions
Increased awareness of advantages that different forms may have over one another in different usage situations can increase demand for bundle
Firms can design digital service formats to be more complementary the through quality lever
Free samples can be designed to increase the complementarity impact
Overview of Business Analytics
and Decision Models
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© Arvind Rangaswamy 2017, All Rights Reserved
Definition of Business Analytics (What it is and what it is not)
Business Analytics refers to concepts, methods, tools, and processes to interpret all types of business-related data (e.g., numbers, text, video, etc.) to drive better business decisions and actions, with the goal of driving better business performance.
May involve sophisticated mathematics and statistics, but that is not necessary
Typically involves technology-enabled application of analytic methods, but that is also not necessary
It is something more than data organization or summarization – it involves interpretation (Is sales growing? Would increasing promotion increase sales sufficiently for us to make a profit? What is the likelihood a customer will cancel his subscription?)
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© Arvind Rangaswamy 2017, All Rights Reserved
Analytics Used in Business
Data Summarization/Visualization
Searching/Sorting/Filtering
Aggregation/Disaggregation (e.g. Clustering)
Dimension reduction
Detecting anomalies/exceptions
Triangulating
Forecasting, Trend Spotting
Establishing/Extracting relationships (e.g. between variables, between people)
Resource allocation (e.g. optimization)
……..
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© Arvind Rangaswamy 2017, All Rights Reserved
Decision Models
A decision model (for business) is a stylized representation of business reality that is easier to deal with and explore (than reality itself) for enhancing managerial/organizational decision making.
The academic objective in developing decision models is to provide a general model-supported approach to managerial decision making in a specific domain or problem area.
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© Arvind Rangaswamy 2017, All Rights Reserved
Vis
ible
Mo
dels
(In
tera
cti
ve)
Em
bed
ded
Mo
dels
(“M
od
els
In
sid
e”)
(1) STANDALONE MODELS
Example: Conjoint Analysis
Example: Marketing
Engineering Tools
(4) INTEGRATED SYSTEMS
Example: Group Decision
Systems
Example: Simulators
(2) COMPONENT OBJECTS
Example: Automated Software
Agents (Price Comparison
Agent, Recommendation
Agent)
(3) INTEGRATED COMPONENT
OBJECTS
Example: Revenue Management
Systems
Example: Google Analytics
Standalone Integrated Systems
Degree of Integration
Types of Decision Models
Implemented by Companies
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© Arvind Rangaswamy 2017, All Rights Reserved
The Readings
Historical evolution of Decision Models and future opportunities (Leeflang and Wittink 2000)
Factors that influence success of MMSS (Wierenga et al. 1999)
Direct and indirect impact of marketing science models (Roberts et al. 2013)
Conditions that influence how
well analytics/decision models
perform in organizational settings
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© Arvind Rangaswamy 2017, All Rights Reserved
Where Analytics Does Well Within Organizations
Repetitive decision situations in which the cost of a wrong decision is small (e.g. recommendation agent; voice recognition; adjacent product placements; road routing based on traffic; college admissions).
Managers/company cannot directly influence outcomes (e.g., interest rates; price of commodities like oil, weather).
In contexts that allow controlled experimentation such as A/B tests (e.g., tests of two different emails; different homepage layouts).
Strategic decisions in which data, analytics, and judgment are combined.
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© Arvind Rangaswamy 2017, All Rights Reserved
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© Arvind Rangaswamy 2017, All Rights Reserved
Some Contexts Where Analytics Hits the Bumpy Road
Fragmented data
Strategic/Important/Complex decisions
Lack of top management support/lack of an analytic organizational culture
Managers believe they can influence outcomes
A strong emotional context
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© Arvind Rangaswamy 2017, All Rights Reserved
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© Arvind Rangaswamy 2017, All Rights Reserved
Complex
Unstructured
Data – text, image,
audio, video
Traditional
Structured
Data
Growth in Non-Traditional Data
Source: IDC 2014, Structured versus Unstructured Data
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© Arvind Rangaswamy 2017, All Rights Reserved
The Changing Nature of Data for Marketing Analytics
Data
Siz
e (
Vo
lum
e)
Data Complexity (Variety, Velocity)
Low (structured) High (Unstructured)
Small
Large
Data for
Marketing
Analytics
Today
e.g., Social
media data
e.g., User reviews
Process data
e.g., Online
Advertising
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© Arvind Rangaswamy 2017, All Rights Reserved
The Business “Use” Case for Analytics
Better decision making
Better process design
Better organizational capabilities
Better Performance
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© Arvind Rangaswamy 2017, All Rights Reserved
Climbing the Ladder of Marketing Analytic Capabilities
Real-time analysis
Predictive modeling
Resource management
Event triggers
Segmentation
Customer database
Develop flexible and dynamic offers and prices
Become efficient and effective in marketing spend
Treat different customers differently
Learn to anticipate and prepare for the future
Develop process and response capabilities
Organize the customer database for the company
Adapted from Tom Davenport and Jeanne Harris (2007), Competing on Analytics
© Penn State 2015 (Rangaswamy, Jordan) All rights reserved