How Causal Analytics Can Drive Your Research Narrative

Market researchers have long been aware that consumer sentiment and behaviors are prone to change. Brands slow to adapt to their consumers' needs find themselves replaced and forgotten. This condition has never been more accurate, and the change has never been more rapid since COVID has become an integral part of the consumer landscape. Since March 2020, consumers have shifted their purchasing behaviors, product choices, and means of purchasing to match the state of the world.

These changes happened quickly, and few would hesitate to say the changes were drastic. But as we move toward what might be our "new normal," sentiments and behaviors will change again. While some of the events of 2020 may have permanently affected certain aspects of consumerism, if history and research tell us anything it's that these changes will eventually shift over time too. As a market researcher, the ability to gather, analyze, and act on these changes is absolutely vital.

Causal Analytics Provide Insights to Predict Consumer Behavior

True causal analytics methods can revolutionize the use of data in decision-making by revealing how different choices affect the probabilities of various outcomes. Working within the limitations of time and budget creates a challenge for data analysts, but innovations in AI/ML build a path to new solutions.

Longstanding analytic approaches like correlation and regression provide a limited view into how two variables are related to each other - it's a single coefficient. One number. What causality shows is why perceptions (scores) for one variable drive another. The ability to understand the directionality of influence, and automate it, is what provides the researcher with a better story in mere minutes. This type of data analysis is far superior because it factors in the forces driving the decision-making process. This explanatory power, coupled with visual outputs, transforms the way we view research and understand outcomes.

Big Budget Outcomes When the Budget isn't Big

It is true that COVID changed the consumer landscape in 2020. Overnight, companies found their existing consumer research was outdated. Those left standing were compelled to do more with slashed budgets and fewer team members. Post-pandemic shopping habits will take time before they stabilize into practices recognizable to our pre-pandemic selves and therefore, brands need high-quality, high-ROI analysis with speed. 

Inguo provides enlightening, causal analytics with speed and accuracy not seen with correlation and regression. Additionally, the causal graphs are superior in their ability to help the analyst tell a more compelling research story. Causal analytics will help you find the WHYs in your datasets, and answer questions about how consumers think and behave in a way you've not likely seen before. And because graph creation is automated, these insights can be achieved very quickly, helping researchers formulate a narrative without pouring over the entire set of crosstabs.

In this post-COVID economy, the only thing quicker than changing customer demands is your ability to formulate an ROI-driven research story for your clients through the use of causal discovery. Use this solution to truly understand consumers' beliefs and behaviors, and reap reporting efficiencies at the same time.

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