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. 

Data mining is the process of discovering patterns and knowledge from large amounts of data, by using techniques such as statistical analysis, machine learning, and database systems. The goal of data mining in market research is to extract useful insights and make data-driven decisions that can inform marketing strategies, product development, and other business initiatives.

Data strategy is a structured plan for how an organization collects, manages, analyzes, and uses data to support smart marketing decisions. It ensures that the right data is being used in the right way to generate valuable insights, improve customer understanding, and drive growth.

The two main variables in an experiment are the independent and dependent variable. A dependent variable is the variable being tested and measured in a study. The dependent variable is ‘dependent’ on the independent variable. As the independent variable changes, the effect on the dependent variable is observed and recorded.

Derived Importance is a calculated importance measure based on how the respondent answers multiple questions (for example, when overall satisfaction is low, which other measures are also low?). Statistical tools are used to determine whether or not there is a strong relationship between multiple measures and calculates a derived importance value.

Direct Drivers are states, experiences, or circumstances that have a direct relationship with the measurement of interest. For example, if NPS is the measurement of interest (or Dependent Variable), one’s interaction with a customer service rep would likely have a direct relationship with the NPS measure and be a direct driver of that rating.