Implementing Marketing Analytics

Outline Potential benefits of and skepticism toward marketing analytics Performance implications of deploying marketing analytics Putting it all together

Challenges faced by today’s marketing decision makers Global, hypercompetitive business environment. More demanding customers served by a greater number of competitors on a global scale. Exploding volume of data “We’re drowning in data. What we lack are true insights.” Need for faster decision making Information overload and lack of time, yet decisions have to be made all the time. Higher standards of accountability Marketing expenditures have to be justified in the same way as other investments.

Need for better marketing decision making Intuitive decision making In a world characterized by rapid change, information overload, greater accountability, etc. intuition is unlikely to generate superior results; Data- and model-based decision making Marketing Engineering: “A systematic approach to harness data and knowledge to drive effective marketing decision making and implementation through a technology-enabled and model-supported interactive decision process” (LRB, p. 2) Yet, “paralysis through analysis” and other criticisms of marketing analytics

Marketing Engineering Marketing Environment Automatic scanning, data entry, subjective interpretation Data Database management, e.g.., selection, sorting, summarization, report generation Information Engineering Marketing Mental models, Decision models Insights Judgment under uncertainty, e.g.., modeling, communication, introspection Decisions Financial, human, and other organizational resources Implementation

Germann, Lilien, and Rangaswamy (2013)

Response models in the decision loop Marketing actions (inputs) Competitive actions Observations (outputs) Product design, Price, Advertising, Selling effort, etc. Response Model Awareness Preferences Sales Environmental Conditions Objectives Control, Adaptation Evaluation

A simple (linear) response model

A nonlinear response model

The profit equation  

STP – Segmentation, Targeting, Positioning Product Price Communication Distribution All consumers in the market Target market segment(s) Marketing mix Marketing strategies of competitors Target marketing and positioning

Market segmentation Discriminant analysis Segment B Cluster analysis Response Who’s this? Segment B B2 Cluster analysis A2 Segment A A1 B1 Who’s this? marketing variable x1 x2

Positioning What are the central dimensions that underlie customers’ perceptions of brands in the product class? How do customers view our brand on these dimensions? How do customers view our competitors? How do perceptions relate to preferences?

Positioning map with perceptions and preferences

The company’s profit chain Choice models Conjoint analysis Customer value Customer satisfaction Customer loyalty Company profitability Analyzing and managing customer satisfaction CLV

The digital revolution digital marketing provides many new opportunities for interacting with customers and exploiting the traces of these interactions search analytics a lot of unstructured data is available in the online world and marketers can extract useful information from these online conversations by their customers; text analysis