• Marketing Research
  • Preface
  • 1 Introduction
    • 1.1 Approaches to Research
      • 1.1.1 Theory First Approaches
      • 1.1.2 Empiric First-Apporaches
      • 1.1.3 A Shift to Observable-to-construct links
      • 1.1.4 Conceptual Contributions
      • 1.1.5 Disruption-Driven Anomalies Apporach
  • I CONSTRUCTS
  • 2 Construct vs. Variable
  • 3 Satisfaction
  • 4 Innovation
    • 4.1 Innovation Measure
    • 4.2 New Product Development
    • 4.3 Research and Development
  • 5 Market Entry
  • 6 WOM / Virality
    • 6.1 Structural Virality
      • 6.1.1 Network Structure and position
      • 6.1.2 Seeding Strategies
    • 6.2 Mechanisms/ Processes
      • 6.2.1 Impression Management
      • 6.2.2 Emotion regulation
      • 6.2.3 Information acquisition
      • 6.2.4 Social bonding
      • 6.2.5 Persuading others
    • 6.3 Moderators
      • 6.3.1 Tie Strength
    • 6.4 Drivers of Virality
      • 6.4.1 Social Currency
      • 6.4.2 Accessibility
      • 6.4.3 Emotions
      • 6.4.4 Usefulness
      • 6.4.5 Narratives
    • 6.5 Other variables
      • 6.5.1 Controversality
      • 6.5.2 Popularity
      • 6.5.3 Contractuality
      • 6.5.4 Locus of Control
      • 6.5.5 Horizontal/Vertical Individualism
      • 6.5.6 Linguistic style
    • 6.6 Negative Virality
    • 6.7 Articles
  • 7 Sarcasm
  • II SUBSTANCE
  • 8 Branding
    • 8.1 Brand Elements
      • 8.1.1 Brand Name
      • 8.1.2 Brand Logos
      • 8.1.3 Brand Slogans
    • 8.2 Brand Marketing
      • 8.2.1 Brand Advertising
      • 8.2.2 Brand Promotion
    • 8.3 Brand Equity
      • 8.3.1 Brand Loyalty
      • 8.3.2 Brand Awareness
      • 8.3.3 Brand Associations
      • 8.3.4 Perceived Quality
    • 8.4 Brand Authenticity
    • 8.5 Brand Relationship
      • 8.5.1 Brand Love
    • 8.6 Reputation
    • 8.7 Brand Evaluation
    • 8.8 Brand Favorability
    • 8.9 Branding Portfolio Management
      • 8.9.1 Brand Architecture/Portfolio
      • 8.9.2 Brand Extensions
    • 8.10 Strategic Branding Decisions
      • 8.10.1 Brand Crisis/ Recovery
      • 8.10.2 Global Brand Strategy
      • 8.10.3 Competitors
    • 8.11 Brand-Consumer Interaction
      • 8.11.1 Brand Experience
      • 8.11.2 Brand Cocreation
      • 8.11.3 Brand Community
  • 9 Virtual Environment
    • 9.1 Email
    • 9.2 Social Media
    • 9.3 Who-Generated Content
    • 9.4 Images
    • 9.5 Mobile and Smartphone
    • 9.6 Review
    • 9.7 Slacktivism
    • 9.8 e-Marketplace
    • 9.9 Social Listening Platforms
      • 9.9.1 Fake Check Platform
    • 9.10 Live Streaming
    • 9.11 Augmented Reality
    • 9.12 Artificial Intelligence
    • 9.13 Search Engine
    • 9.14 Customer Engagement
    • 9.15 Wisdom of the Crowds
  • 10 Advertising
    • 10.1 Behavioral Approach
      • 10.1.1 Cognitive and Affective
      • 10.1.2 Involvement
      • 10.1.3 Visual Cues
    • 10.2 Econometric Approach
      • 10.2.1 Product Placements
      • 10.2.2 Deceptive Advertising
      • 10.2.3 Advertising Effects
      • 10.2.4 Estimation of Advertising Effects
      • 10.2.5 Spillovers
      • 10.2.6 Attribution of Advertising Effects
      • 10.2.7 Advertising Content
      • 10.2.8 Consumer Demand for Ads
  • 11 Communication
    • 11.1 Information Theory
      • 11.1.1 Entropy
      • 11.1.2 Divergence
      • 11.1.3 Channel Capacity
  • 12 Sales
    • 12.1 Sales from Rank
    • 12.2 Salespeople
  • 13 Customer Lifetime Value (CLV)
    • 13.1 Example
    • 13.2 Referral value (CRV)
  • 14 Celebrity Endorsement
  • 15 Nudges
  • 16 Marketing-Finance Interface
    • 16.1 Marketing Value
    • 16.2 Business Valuation
    • 16.3 M&A
    • 16.4 Stock Return Response Modeling
    • 16.5 Tobin’s Q
    • 16.6 Corporate Agility
    • 16.7 Firm Complexity
    • 16.8 Initial Coin Offerings
    • 16.9 Initial Public Offerings
  • 17 Privacy
    • 17.1 Psychology of Privacy
    • 17.2 Organizational Perspective
    • 17.3 Privacy Paradox
      • 17.3.1 Tradeoff
      • 17.3.2 No tradeoff
    • 17.4 Privacy Calculus/ Economics of Privacy
    • 17.5 GDPR
  • III METHODOLOGY
  • 18 Metrics
    • 18.1 Finance
      • 18.1.1 Return on Investment (ROI)
      • 18.1.2 Economic Value Added
      • 18.1.3 Market Value Added
      • 18.1.4 Unexpected size-adjusted advertising investments
      • 18.1.5 Shareholder Complaints
      • 18.1.6 Profitability
      • 18.1.7 Firm Size
      • 18.1.8 Sales Growth
      • 18.1.9 Financial Flexibility
      • 18.1.10 Cash flows
      • 18.1.11 Financial Leverage
      • 18.1.12 Stock return
      • 18.1.13 Financial Flexibility
      • 18.1.14 Book Equity
      • 18.1.15 Net Contribution
      • 18.1.16 Diversity
    • 18.2 Marketing
      • 18.2.1 Trust
      • 18.2.2 Sentiment
      • 18.2.3 Purchase Intention
      • 18.2.4 Brand Reputation
      • 18.2.5 Capabilities
  • 19 Data
    • 19.1 MongoDB
    • 19.2 WRDS
    • 19.3 YouTube
      • 19.3.1 OAuth
      • 19.3.2 API
      • 19.3.3 Python
    • 19.4 Consumer Expenditure
    • 19.5 Gender, Age, Nationality
    • 19.6 Google Trends
      • 19.6.1 Relative Search
      • 19.6.2 Absolute Search
    • 19.7 Baidu Index
  • 20 Modeling in Marketing
    • 20.1 Definitions
    • 20.2 Quasi-Experimental
    • 20.3 Transformation
      • 20.3.1 Log-transformation
    • 20.4 Endogeneity
      • 20.4.1 Control Function
    • 20.5 Variance Info Factors
  • 21 Analytical Models
    • 21.1 Building An Analytical Model
    • 21.2 Hotelling Model
    • 21.3 Positioning Models
    • 21.4 Market Structure and Framework
      • 21.4.1 Cournot - Simultaneous Games
      • 21.4.2 Stackelberg - Sequential games
    • 21.5 More Market Structure
    • 21.6 Market Response Model
    • 21.7 Technology and Marketing Structure and Economics of Compatibility and Standards
    • 21.8 Conjoint Analysis and Augmented Conjoint Analysis
    • 21.9 Distribution Channels
    • 21.10 Advertising Models
    • 21.11 Product Differentiation
    • 21.12 Product Quality, Durability, Warranties
      • 21.12.1 Akerlof (1970)
      • 21.12.2 Spence (1973)
      • 21.12.3 S. Moorthy and Srinivasan (1995)
    • 21.13 Bargaining
      • 21.13.1 Non-cooperative
      • 21.13.2 Cooperative
      • 21.13.3 Nash (1950)
      • 21.13.4 Iyer and Villas-Boas (2003)
      • 21.13.5 Desai and Purohit (2004)
    • 21.14 Pricing and Search Theory
      • 21.14.1 Varian and Purohit (1980)
      • 21.14.2 Lazear (1984)
    • 21.15 Pricing and Promotions
      • 21.15.1 Narasimhan (1988)
      • 21.15.2 Balachander, Ghosh, and Stock (2010)
      • 21.15.3 Goić, Jerath, and Srinivasan (2011)
    • 21.16 Market Entry Decisions and Diffusion
    • 21.17 Principal-agent Models and Salesforce Compensation
      • 21.17.1 Gerstner and Hess (1987)
      • 21.17.2 Basu et al. (1985)
      • 21.17.3 Raju and Srinivasan (1996)
      • 21.17.4 Lal and Staelin (1986)
      • 21.17.5 Simester and Zhang (2010)
    • 21.18 Branding
    • 21.19 Marketing Resource Allocation Models
      • 21.19.1 Case study 1
      • 21.19.2 Case study 2
      • 21.19.3 Case study 3
    • 21.20 Mixed Strategies
    • 21.21 Bundling
    • 21.22 Market Entry and Diffusion
    • 21.23 Principal-Agent Models and Salesforce Compensation
      • 21.23.1 Basu et al. (1985)
      • 21.23.2 Lal and Staelin (1986)
      • 21.23.3 Raju and Srinivasan (1996)
      • 21.23.4 Joseph and Thevaranjan (1998)
      • 21.23.5 Simester and Zhang (2010)
    • 21.24 Meta-analyses of Econometric Marketing Models
    • 21.25 Dynamic Advertising Effects and Spending Models
    • 21.26 Marketing Mix Optimization Models
    • 21.27 New Product Diffusion Models
    • 21.28 Two-sided Platform Marketing Models
  • 22 Empirical Models
    • 22.1 Attribution Models
      • 22.1.1 Ordered Shapley
      • 22.1.2 Markov Model
    • 22.2 Sales Funnel
      • 22.2.1 Example 1
      • 22.2.2 Example 2
    • 22.3 RFM
      • 22.3.1 Visualization
      • 22.3.2 RFMC
    • 22.4 Customer Segmentation
      • 22.4.1 Example 1
      • 22.4.2 Example 2
    • 22.5 Shopping carts analysis
      • 22.5.1 Multi-layer pie chart
      • 22.5.2 Sankey Diagram
      • 22.5.3 Sequence in-depth analysis
    • 22.6 Geodemographic Classification
  • 23 Model Building
  • 24 Structural Models
    • 24.1 Top Seminal Papers
    • 24.2 To get started in this area
      • 24.2.1 Books
    • 24.3 Structural modeling and Causal Inference
  • 25 Qualitative Research
    • 25.1 Inter-rate reliability methods
      • 25.1.1 Percent Agreement
      • 25.1.2 Cohen’s Kappa
      • 25.1.3 Fleiss’kappa
    • 25.2 Krippendorff’s Alpha
      • 25.2.1 Kendall’s W
      • 25.2.2 Intraclass correlation coefficients
      • 25.2.3 Light’s kappa
  • 26 Measurement Scales
  • 27 Preference Measurement
    • 27.1 Conjoint Analysis
      • 27.1.1 Full-Profile
      • 27.1.2 Choice-based
      • 27.1.3 Adaptive
      • 27.1.4 Hybrid
      • 27.1.5 Max-Diff
      • 27.1.6 Self-explicated
      • 27.1.7 Hierarchical Bayes analysis
      • 27.1.8 Application
  • 28 Image Processing
  • 29 Surveys
    • 29.1 Anchoring Vignettes
      • 29.1.1 Nonparametric method
      • 29.1.2 Parametric method
  • 30 Experiment
  • IV OTHERS
  • 31 Report
  • 32 Review Process
    • 32.1 Review at JM
    • 32.2 Review Process Back end
  • 33 Scientific Writing
  • 34 CB Seminar
    • 34.1 Overview
    • 34.2 Social Influence
    • 34.3 Interpersonal perception and consumer lay beliefs
    • 34.4 Emotions, mood and affect
    • 34.5 Persuasion and attitude change
    • 34.6 Judgment and decision making and behavioral pricing
    • 34.7 Goals and Motivation
    • 34.8 Culture and consumer behavior
    • 34.9 Prosocial behavior and morality
    • 34.10 Consumer well-being & Food Consumption Decisions
    • 34.11 Digital marketing and WOM
    • 34.12 Experiential consumption and time
  • 35 Marketing Mix Models
    • 35.1 Discrete Choice Models and Continuous Heterogeneity
    • 35.2 Structural Models, Endogeneity
      • 35.2.1 Background
      • 35.2.2 Examples
    • 35.3 Cross-Category and Store Choice Models
      • 35.3.1 Background
      • 35.3.2 Examples
    • 35.4 Policy Applications of Discrete Choice Models
      • 35.4.1 (Khan, Misra, and Singh 2015)
      • 35.4.2 (A. Rao and Wang 2017)
      • 35.4.3 (Tuchman 2019)
      • 35.4.4 (Seiler, Tuchman, and Yao 2020)
    • 35.5 Frontier Papers
      • 35.5.1 (Neumann, Tucker, and Whitfield 2019)
    • 35.6 Advertising Response Measurement
      • 35.6.1 (Terui, Ban, and Allenby 2011)
      • 35.6.2 (Narayanan and Kalyanam 2015)
      • 35.6.3 (Lewis and Rao 2015)
      • 35.6.4 (Gordon, Zettelmeyer, et al. 2019)
  • 36 Strategic Dynamic Models
    • 36.1 Market Entry
      • 36.1.1 (Peter N. Golder and Tellis 1993)
      • 36.1.2 (J. Johnson and Tellis 2008)
      • 36.1.3 (Zervas, Proserpio, and Byers 2017)
    • 36.2 Product Adoption and Diffusion
      • 36.2.1 Background
      • 36.2.2 Discussion
    • 36.3 Take-off Disruption
      • 36.3.1 Disruptive Technologies
      • 36.3.2 (Peter N. Golder and Tellis 1997) takeooff
      • 36.3.3 (Chandy and Tellis 2000) Incumbent’s curse
      • 36.3.4 (Tellis, Stremersch, and Yin 2003) International Takeoff
      • 36.3.5 (Hauser, Tellis, and Griffin 2006)Review on Innovation
      • 36.3.6 (Chandrasekaran and Tellis 2008) Global Takeoff
      • 36.3.7 (Sood and Tellis 2011) Predict takeoff
      • 36.3.8 (M. Zhang and Luo 2016) Restaurant survival from Yelp
    • 36.4 Advertising Response (Effectiveness)
      • 36.4.1 (Tellis, Chandy, and Thaivanich 2000) Direct TV ad
      • 36.4.2 (Tellis and Franses 2006) Optimal Data Interval for estimating ad response (on sales)
      • 36.4.3 (T. S. Teixeira, Wedel, and Pieters 2010) Ad Pulsing to prevent consumer ad avoidance
      • 36.4.4 (Sethuraman, Tellis, and Briesch 2011) Advertising effectiveness meta-analysis
      • 36.4.5 (Liaukonyte, Teixeira, and Wilbur 2015) TV advertising on online shopping
      • 36.4.6 (Tirunillai and Tellis 2017) TV ad on Online chatter: synthetic control
    • 36.5 Marketing Return
      • 36.5.1 (Fornell et al. 2006) Customer satisfaction and stock return
      • 36.5.2 (S. Srinivasan and Hanssens 2009) Marketing and Firm Value
      • 36.5.3 (Sood and Tellis 2009) Innovation and Stock Return
      • 36.5.4 (Jacobson and Mizik 2009b)
      • 36.5.5 (Jacobson and Mizik 2009a)
      • 36.5.6 (Borah and Tellis 2014) Choice of Payoff from announcements (Innovations)
      • 36.5.7 (Tirunillai and Tellis 2012) Chatter effect on stock performance
    • 36.6 Creativity
      • 36.6.1 (Bayus 2013) Crowdsourcing New Product Ideas over Time
      • 36.6.2 (Toubia and Netzer 2017) Idea generation, creativity, prototypicality
      • 36.6.3 (Y. “Max”. Wei, Hong, and Tellis 2021) Machine leaning creativity
      • 36.6.4 Can AI do ideation? 2022
      • 36.6.5 (Berger and Packard 2018) Content Atypicality
      • 36.6.6 (Stephen, Zubcsek, and Goldenberg 2016) The Effects of Network Structure on Redundancy of Ideas
    • 36.7 Quality
      • 36.7.1 (Tellis, Yin, and Niraj 2009) Network effects and quality in high tech
      • 36.7.2 (Peter N. Golder, Mitra, and Moorman 2012) An Integrative Framework for Quality
      • 36.7.3 (Tirunillai and Tellis 2014) Mining Quality from Consumer Reviews
      • 36.7.4 (Borah and Tellis 2016) Spillover Effects in Social Media
  • 37 WashU Analytical Model
    • 37.1 Platforms
      • 37.1.1 (Jiang, Jerath, and Srinivasan 2011) Amazon’s mid-tail
      • 37.1.2 (Zou and Zhou 2021) search neutrality
      • 37.1.3 Diao et al. 2021 P2P rideshare vs. taxi
      • 37.1.4 (Gal-Or and Shi 2022) Subscription platform
      • 37.1.5 (Long, Jerath, and Sarvary 2022) Design Amazon marketplace
      • 37.1.6 (Hajihashemi, Sayedi, and Shulman 2021) Personalized Pricing with Network Effects
    • 37.2 Dynamic Pricing and Bundling
      • 37.2.1 (Prasad, Venkatesh, and Mahajan 2017) Bundling
      • 37.2.2 (Diao, Harutyunyan, and Jiang 2019) Intertemporal price, consumer fairness concern
      • 37.2.3 (Dana and Williams 2022) Intertemporal price
      • 37.2.4 (Kolay and Tyagi 2022) Event bundle
    • 37.3 Consumer Fairness
      • 37.3.1 (Fu et al. 2021) Unfair machine learning algorithms
    • 37.4 Decentralized Channels
      • 37.4.1 (Jiang and Tian 2022) Reactive capacity on product quality and profitability in uncertain markets
      • 37.4.2 (Hu, Zheng, and Pan 2022) Wholesale vs. Agency
    • 37.5 Consumer Search
      • 37.5.1 (Jiang and Zou 2020) Consumer Search and Filtering on Online Retail Platforms
      • 37.5.2 (Yanbin Chen et al. 2021) Consumer Search with blind buying
      • 37.5.3 (X. Li and Xu 2022) Superior knowledge, price discrimination, and customer inspection
    • 37.6 Review paper
  • 38 Marketing Strategy
    • 38.1 Intro
    • 38.2 Branding
    • 38.3 Channels and Customer Management
    • 38.4 Human Capital
  • 39 Others
  • V CASE STUDIES
  • 40 Case Studies in Branding
    • 40.1 Apple: Innovation and Design as Brand Identity
    • 40.2 Nike: Building a Global Brand Through Storytelling and Innovation
    • 40.3 Tesla: Revolutionizing the Automotive Industry Through Innovation and Sustainability
    • 40.4 Amazon: Transforming Retail and Beyond
    • 40.5 Zoom: Connecting the World Through Video Communications
    • 40.6 Beyond Meat: A Plant-Based Revolution
    • 40.7 TikTok: A Dance with Global Success
    • 40.8 Coca-Cola: Quenching the World’s Thirst for Over a Century
    • 40.9 Netflix: Redefining the Future of Entertainment
    • 40.10 Airbnb: Disrupting the Hospitality Industry
    • 40.11 Starbucks: Brewing Success Through Innovation and Responsibility
    • 40.12 The Walt Disney Company: A Kingdom of Creativity and Innovation
    • 40.13 McDonald’s: Serving Success with a Side of Innovation
    • 40.14 Dove (Unilever): Crafting Beauty and Confidence
    • 40.15 IKEA: A Symphony of Design, Affordability, and Sustainability
    • 40.16 LEGO: Building Blocks of Innovation and Success
    • 40.17 Slack: Revolutionizing Workplace Communication
    • 40.18 Patagonia: A Case Study in Sustainable Business Practices
    • 40.19 Spotify: Transitioning from music sales to subscription streaming
    • 40.20 Warby Parker: Disrupting the traditional eyewear market with an online-first approach
    • 40.21 Allbirds: A Case Study in Sustainable Footwear Innovation
  • 41 Case Studies in Advertising
  • References
  • Published with bookdown

Marketing Research

9.9 Social Listening Platforms

According to Forrester Wave’s report by Jessica Liu and Sara Dawson (2020), the top 10 social listening platforms (SLPs) are

  1. Brandwatch
  2. Digimind
  3. Linkfluence
  4. ListenFirst
  5. Meltwater
  6. NetBase Quid
  7. Sprinklr
  8. Synthesio
  9. Talkwalker
  10. Zignal

9.9.1 Fake Check Platform

  • https://mightyscout.com/influencer-lookup

  • https://socialauditor.io/

  • https://analisa.io/

  • https://hypeauditor.com/free-tools/instagram-audit/

  • https://www.inbeat.co/fake-follower-checker/

  • https://grin.co/influencer-marketing-tools/fake-influencer-tool/

  • https://www.fakecheck.co/

  • https://www.modash.io/fake-follower-check/