Choice modeling (also called discrete choice modeling) is a quantitative research technique used to understand and predict customer decision-making. It analyzes how people make trade-offs between different product or service attributes when choosing among alternatives.

Churn survival analysis in marketing research is a statistical approach used to understand and predict how long customers stay with a company before they “churn” (i.e., stop using a product or service).

Code frames give organization to unstructured data or OE responses. For example, if the unstructured responses are ‘all the song titles you can think of’, the code frame could organize mentions by band, by genre, by mentions of colors, or by any other organization scheme that makes sense for the study. The code frame can be as general or specific as needed, and specific groupings can be nested under more general topics (like, “Colors” can have specific sub-groups for green, blue, red, yellow, etc., so you can look at colors individually or as an overall group).

Since OEs are unstructured free-form responses, one way to organize and make sense of the responses is to “code” them. This involves reading a response and assigning a code (or multiple codes) that represent the sentiment or topic that the response mentions. These codes form a code frame that organizes mentions into groups of sentiments or topics that go together. (Coding OEs allows unstructured responses to be included in data tables as organized responses.)

Cognitive Load refers to the degree of difficulty, amount of memory, or the mental calculations / gymnastics required of a respondent. (For example, if the task is to estimate the number of held accounts, account balances, interest earned or investment returns, and total household expenses for the year, the cognitive load for someone who doesn’t work in finance or accounting will be greater than the cognitive load for someone who spends most of their time thinking about these topics.)

Conjoint analysis is a quantitative research technique used in marketing to understand how consumers make decisions based on different product or service attributes. It helps identify which features are most important to customers and how they value trade-offs between them.

How individuals make decisions about purchasing, using, and disposing of products and services. It involves understanding the psychological, emotional, social, and cultural factors that influence consumer decisions. It’s about figuring out why people buy what they buy, how they go about buying it, and what drives their choices at different stages of the purchase process.

The process of collecting, analyzing, and interpreting data about customers to better understand their behaviors, preferences, and needs—so businesses can make smarter marketing decisions.

Customer journey analytics is the process of tracking, analyzing, and understanding how customers interact with a brand across all touchpoints—from first awareness to post-purchase (and beyond). It combines data from multiple sources (website, app, email, social media, customer support, etc.) to visualize and measure the entire customer experience—helping businesses see what’s working, what’s not, and how to improve the path to conversion. 

Data-driven insights in marketing research are the meaningful conclusions, patterns, or recommendations that come directly from analyzing quantitative or qualitative data—not just gut feelings or assumptions. They help marketers make informed decisions about what their audience wants, how campaigns are performing, where to invest budget, and how to improve strategy.