MKTG 555: Marketing Models

Transcription

1 MKTG 555: Marketing Models Decision Models in Marketing April 4,

2 Discussion Kannan et al. paper 2009

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4 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 4

5 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? 5

6 The Model Customers utilities for Print, PDF and the Bundle U ij = β ij X i β pi p j + ε ij j = {1 = Print, 2 = pdf} X i : Customer i s degree of fit of content to his/her needs β ij : Value customer i places on product form j β pi : Price sensitivity for customer i U ij = 0 for free browsing (i.e. customer does not buy either i or j) 6

7 The Model Customers utilities for Print, PDF and the Bundle U ib = β i +β i1 +β i2 X i β pi p b + ε ib i : min β i1, β i2 < i < 0: Incremental value of bundle, which measures complementarity perceptions. 7

8 Group A Consumers with books in shopping cart Group B Consumers browsing books with pdf version available Intercept and present details of pdf or book Just pdf Complete pdf order 2 nd NO (No pdf No print) Would you like to order pdf now? Both pdf and print Short Survey (B) for free totebag Complete pdf & print order Go back 1st NO Reduce pdf price to one level lower 2nd NO Just print Complete print order Online Choice Experiments at NAP Short Survey (A) For Add l. Discount Short Survey (A) For free shipping and add l. discount Short Survey (B) for free shipping Continue Checkout Process

9 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

10 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. 10

11 NAP Implementation PDF format introduced in April 2003 Price 75% of print price Bundle 120% of print price titles; free browsing still available Revenues increased by 10% after controlling for introduction of new titles 11

12 Newer Titles Their sales seem to show exponential decay. Assess how decay rate affected by intervention. Before During 12

13 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 13

14 Findings Koukova, Kannan, & Kirmani Journal of Marketing Research (2012) 14

15 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 15

16 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 16

17 Overview of Business Analytics and Decision Models

18 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?) 18

19 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).. 19

20 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. 20

21 Embedded Models ( Models Inside ) Visible Models (Interactive) Types of Decision Models Implemented by Companies (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 21

22 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) 22

23 Conditions that influence how well analytics/decision models perform in organizational settings

24 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 s; different homepage layouts). Strategic decisions in which data, analytics, and judgment are combined. 24

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26 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 26

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28 Growth in Non-Traditional Data Complex Unstructured Data text, image, audio, video Traditional Structured Data Source: IDC 2014, Structured versus Unstructured Data 28

29 Data Size (Volume) The Changing Nature of Data for Marketing Analytics Large e.g., Online Advertising e.g., Social media data Small Data for Marketing Analytics Today e.g., User reviews Process data Low (structured) High (Unstructured) Data Complexity (Variety, Velocity) 29

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32 The Business Use Case for Analytics Better decision making Better process design Better organizational capabilities Better Performance 32

33 Climbing the Ladder of Marketing Analytic Capabilities Develop flexible and dynamic offers and prices Become efficient and effective in marketing spend Treat different customers differently Real-time analysis Predictive modeling Resource management Event triggers Segmentation Customer database 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 33 Penn State 2015 (Rangaswamy, Jordan) All rights reserved