mktg 555: marketing models - pennsylvania state universityapril_2017).pdfhits the bumpy road...

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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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NAP Business Model Over Time

1996

Sell

print

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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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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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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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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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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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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Newer Titles

Their sales seem to show exponential decay . Assess how decay rate affected by intervention.

Before

During

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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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Findings Koukova, Kannan, & Kirmani

Journal of Marketing Research (2012)

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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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“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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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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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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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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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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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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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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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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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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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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The Business “Use” Case for Analytics

Better decision making

Better process design

Better organizational capabilities

Better Performance

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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

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