Doing “statistics” strikes fear in the hearts of many, so how about if we talk about “algorithms” instead? It’s a safer word because most people in the worlds of business and market research never have to take (or fail) a course in algorithms.
Algorithms are central to the work that we do in business and market research. They are always top of mind for us at Versta Research because at any one time we are usually working on data-intensive projects that involve either (a) developing new algorithms for clients, or (b) tools that apply sophisticated algorithms to data in new and exciting ways.
What is an algorithm? It is “a mechanical or recursive computational procedure” (American Heritage Dictionary). New technologies, data capacities, data collection techniques, and speed have indeed made this the age of algorithms. Here are some examples, including a few that relate to our work:
- Recommendation engines used by Netflix, Spotify, and YouTube use algorithms to analyze viewing or listening history to suggest new content that users will enjoy.
- Voice assistants and chatbots such as Siri and Alexa rely on natural language processing (NLP) algorithms to interpret speech, generate answers, and trigger actions.
- Dynamic pricing algorithms are used by online retail stores, airline websites, and ride-share services to adjust prices based on demand, inventory, and competition.
- Wearable fitness trackers use signal-processing algorithms to track heart rate, sleep stages, and physical activity.
- Versta Research built a custom matching algorithm for a client that pairs future college roommates based on survey data with thousands of data points on each person and each potential match. The process involved mining the data for ways of matching that would optimize known predictors (from the survey research) of roommate satisfaction.
- We routinely use convergent cluster and ensemble analysis to explore patient segments using patient chart data, or consumer segments using primary research survey data. We rely on multiple clustering algorithms, generating hundreds of potential solutions and then select the optimal one based on a reproducibility algorithm.
What is interesting is that the “basics” of inferential and descriptive statistics are becoming a smaller and smaller piece of data analysis. More than ever, we combine them with procedures that involve data mining, predictive analytics, Monte Carlo simulations, and even (dare we say it?) AI.
What are the implications for research firms and the clients who employ us? The value of having extremely well-trained researchers who are fluent not only in statistics, but in mathematics, modeling, logic, and even algorithms, is essential. And if, on top of that, you have smart people who know how to use, interpret, and tell a story with that data, the value of your market research will really shine.
—Joe Hopper, Ph.D.
