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.)

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.

“A data enablement strategy in marketing research is all about making data usable, accessible, and actionable for the people who need it—like marketing teams, researchers, and decision-makers. It’s the bridge between having good data and actually getting value from it—by equipping the right people with the tools, training, access, and processes to turn data into decisions.”

Data integration is the process of combining multiple data sources into a single, comprehensive representation of information. In the context of market research, data fusion can be used to combine different data sets collected from different sources, such as customer surveys, sales data, and social media analytics, in order to gain a more complete and accurate understanding of consumer behaviour and market trends.

Data management strategy is a plan for how an organization handles data throughout its lifecycle—from collection and storage to organization, protection, and use—so it can support accurate, efficient, and ethical marketing decisions. It’s all about making sure that the right data is available, accessible, clean, and secure for researchers and marketers to use effectively.