Implementing Marketing Analytics

Transcription

1 Implementing Marketing Analytics

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

3 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.

4 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

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

6 Germann, Lilien, and Rangaswamy (2013)

7 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 Control, Adaptation Objectives Evaluation

8 Sales Implementing Marketing Analytics A simple (linear) response model Actual and predicted sales as a function of advertising spending Advertising spending Predicted sales Actual sales

9 A nonlinear response model

10 The profit equation Profit = Revenues Costs Sales Volume Price Variable Costs Fixed Costs (Advertising, Distribution) (Other Fixed Costs) Industry sales Market Share

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

12 Market segmentation Response B 2 A 2 A 1 B 1 Who s this? Discriminant analysis Who s this? Segment B Cluster analysis Segment A x 1 x 2 marketing variable

13 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?

14 II (26.5%) Implementing Marketing Analytics Positioning map with perceptions and preferences Chewy Nutrine R1 R2 Chlormint I (50.2%) Exciting Mint-O-Fresh Flavours Cooling Effect Mentos Fresh Long Lasting Hard Mahalacto R3

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

16 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