Implementing Analytics Implementing Marketing Analytics

Outline ▪ Potential benefits of vs. skepticism toward marketing analytics ▪ Empirical evidence about the per- formance implications of deploying marketing analytics ▪ Putting it all together Implementing Marketing Analytics

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. Implementing Marketing Analytics 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 : “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 Implementing Marketing Analytics Marketing Engineering

Marketing Environment Automatic scanning, data entry, subjective interpretation

Data Database management, e.g.., selection, sorting, summarization, report generation Information Mental models, Decision models

Insights Judgment under uncertainty, e.g.., modeling, communication,

Marketing introspection Engineering Decisions Financial, human, and other organizational resources Implementation Implementing Marketing Analytics

Germann, Lilien, and Rangaswamy (2013) Implementing Marketing Analytics

Data analysis ▪ Familiarize yourself with the questionnaire and the data (i.e., make sure you understand what each variable means); ▪ Compute descriptive statistics and check for coding errors, recode variables if necessary, pay attention to missing values, identify unusual observations, etc.; ▪ Develop clear research questions, formulate an analysis plan, and then use available software to answer your research questions; ▪ Carefully study the results and interpret the findings in light of your research questions; Implementing Marketing Analytics

Response models in the decision loop

Marketing actions Competitive actions Observations (inputs) (outputs)

Product design, Awareness Response , , Preferences Model Selling effort, etc.

Environmental Conditions

Objectives Control, Adaptation Implementing Marketing Analytics A simple (linear) response model

Actual and predicted sales as a function of advertising spending 13200000

13000000

12800000

12600000

12400000 Sales 12200000

12000000

11800000

11600000

11400000 700000 800000 900000 1000000 1100000 1200000 1300000 1400000 1500000 Advertising spending

Predicted sales Actual sales Implementing Marketing Analytics

A nonlinear response model Implementing Marketing Analytics

The profit equation

Profit = Revenues − Costs

Sales Volume × Price Variable Costs Fixed Costs (Advertising, Distribution) (Other Fixed Costs)

Industry sales × Share Implementing Marketing Analytics

STP – Segmentation, Targeting,

All consumers Product in the market

Price Target Target marketing market and positioning segment(s)

Communication Marketing Distribution

Marketing strategies of competitors Implementing Marketing Analytics

Discriminant analysis Response Who’s this? Segment B

B2

A2 Segment A A1 B 1 Who’s this? marketing variable x1 x2 Implementing Marketing Analytics

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? Implementing Marketing Analytics

Positioning map with perceptions and preferences

Chewy

R2 R1 Nutrine Chlormint

Cooling Effect Mint-O-Fresh Exciting I (50.2%) Flavours Fresh Mentos

Long Lasting

Hard

Mahalacto R3 II (26.5%) II Implementing Marketing Analytics

The company’s profit chain

Choice models

Customer Customer Customer Company value satisfaction loyalty profitability

Analyzing and managing CLV customer satisfaction Implementing Marketing Analytics

The digital revolution

▪ 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 ▪ digital marketing provides many new opportunities for interacting with customers and exploiting the traces of these interactions □ search analytics