In a recent interview from Campaign Monitor, 6 of the top digital marketers make insightful predictions about the future of marketing. Kath Pay, CEO of Holistic Email Marketing, explains how marketers are currently campaigned-oriented and focused on metrics that measure the success of past campaigns.

In the near future, however, Pay says predictive marketing metrics will be vastly improved upon, and more marketers will become aware of the benefits of predictive marketing metrics. The use of these metrics, Pay states, will provide more value to customers and give marketers more valuable insights.

This same assumptions about marketing rings true for the market research industry as well. The more AI and machine learning improve, the more possible it becomes for predictive analytics to positively influence market research business intelligence, telling researchers more about what to look forward to in the future and not just the past.

How does it work?

One of the main questions facing marketers and researchers alike is how predictive analytics even works. In basic terms, data scientists use big data regression analytics as the main tool in predictive analytics. Regression analysis spots strength of correlations between customer variables with behaviors (like product purchases). Then, analysts can see the degree to which each variable affects the behavior (regression coefficients), and then creates a score for the likelihood of future behaviors. Analyses like these can be used to create an intelligence software that automates current and future business logistics based on the different variables that affect customer behavior.

To give you a more concrete idea, here are some ways marketing and sales teams use predictive analytics to make better business decisions, strengthen B2B endeavors, and improve the customer journey. Uses of predictive analytics include:

  • Forecasting retail sales for accurate growth predictions (like Arby’s did)
  • Qualifying and prioritizing leads to reach the best customers first
  • Identifying high-value customers
  • Making decisions about which new products and services will succeed
  • Targeting customers more efficiently
  • And more!

What role does this play in market research?

Now that we have a basic idea of how analysts make use of big data to yield predictions, and some concrete examples of predictive analytics in use, let’s talk about how these predictions relate to business intelligence in market research and more specifically online research.

Market researchers can now, and will better be able to in the future, use predictive analytics to glean more insightful predictions from current and past online data and even go as far as enhance traditional research methods, such as focus groups, data from research communities, and gather the right respondents.

In short, predictive analytics can take data from a CRM, web data, social media data, unstructured data, mobile data, survey data, community data, software and any other type of telling information. Then, the predictive analytics model will use algorithms and cross-validation to sort through all data, toss out meaningless information, and pinpoint important variables.

Predictive analytics can be applied to any and all of the data market researchers gather—including online qualitative research, online quantitative research, texts, market research online communities, detailed participant information and more—and gives you insights about the future customer, market movements and qualitative markets rather than summations of past data.

Wrap Up

Predictive analytics isn’t yet where it will be in the near future, but it’s starting to gain speed in the business world and will benefit online communities and the market research industry as a whole.