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.

A small group of people selected from a wider population, engaged in an open (yet guided) discussion about their opinions or emotional responses to a particular subject or area.

Forced Choice means that the respondent cannot proceed with the survey/exercise/interview until making a selection. These questions are not optional and there is not an “N/A” option.

Gamification, the process of adding game-like elements to a research activity, is increasingly being used as a way to engage participants and collect data in a more interactive way. For example, a survey is likely to be more engaging with respondents and the data will be gathered in a more natural and less intrusive way, if the survey is engaging, interactive and fun. This should lead to better quality data.

The use of data analysis tools and techniques to understand trends, behaviors, and outcomes within the healthcare industry, specifically to guide marketing strategies and decision-making. It bridges healthcare data (like patient behavior, physician behavior, patient journeys, demographics, etc.) with marketing goals (like targeting, segmentation, messaging, and outreach effectiveness).

An assumption or proposition that a researcher puts forward about some characteristic of the population being investigated.