Traditional marketing mix modeling is not dead, but it must be transformed by incorporating learnings from real-time, digital advertising experiments in a mathematically consistent fashion.

Calibration of survey-based choice models can make a substantial difference in predicted demand and revenue resulting from price changes.

A regression modeling approach in customer satisfaction and loyalty research using time series cross-sectional (TSCS) modeling, which incorporates both across-units and across-time variation in data variables.

Choice models are valuable in assisting marketers in decision making. This article explains the benefits choice analysis: Improved experimental design algorithms; Latent Class and Hierarchical Bayes models; and Model calibration.

Messaging and positioning choice modeling is recommended when the primary research objective is to obtain information that would allow a company to develop the most effective communications message to consumers, maximizing attraction to its specific brand, product line, store, or department within the store.

The focus of this paper is to examine the theories that underlie the decision processes used by consumers. The theories summaries consumer decision making theories (utility theory, Satisficing and prospect theory) and decision-making strategies

Marketing mix modeling can assist in making specific marketing decisions and tradeoffs, but it can also create a broad platform of knowledge to guide strategic planning.

What steps would one take to build such a branding model based on both structured data and unstructured textual feedback from customers? What kind of modeling techniques could be used?