How AI and automation are changing brands’ approach to market research

For brands looking for customer insights to drive decision making, improve customer experience (CX) and ultimately drive growth, market research has long been part of the toolkit.

Whether it’s really useful or not is another question. In a typical market research project, brands invest (often heavily) in conducting research that amounts to a one-time snapshot of existing customer sentiment and perhaps prevailing competitive differentiators. While this research can yields useful insights, but usually fails to recognize the desires of potential customers, or adequately correlate data that reveals exactly why customers are with competitors.

Brands easily miss the wood for the trees when they rely on traditional market research. They get bogged down in dealing with complaints, while overlooking the fundamental reasons for it Why a customer chooses one brand over another. At the same time, market research projects are prohibitively expensive to repeat with regularity, offering limited insights that start to get old once the research is completed.

Some marketers are instead using social listening platforms for more continuous analysis of customer behavior (and customer engagement with specific features or brand offerings). This strategy can collect helpful customer reviews and feedback and is usually much less expensive than having one-off market surveys conducted.

However, this approach still leaves marketers blind to competitive activity and the adjustments best suited to winning over those potential customers. Social listening platforms also largely require manual processes to filter insights from firehouse data. Talented teams of data analysts who do this time-consuming work are very good at identifying correlations between that data, but that talent doesn’t come cheap. The shortcomings of both traditional market research and social listening platforms mean that rich opportunities to meaningfully and flexibly improve customer experiences regularly go undiscovered.

The answer to outdated market research and incomplete social listening platforms, as it is in the wider technology landscape, could very well be artificial intelligence (AI) and automation.

With AI deployed to continuously collect marketing insights from the right data sources, brands can take the guesswork out of researching and correlating relevant customer experience data. AI-driven automation addresses the biggest limitations of traditional market research head-on: transforming the cost, cadence, and quality of insights collected. Marketers who would otherwise periodically budget for costly research projects — and adjust their customer-facing practices only so often — should look for real-time, always-on insights that show clear correlations.

If traditional market research is like deciphering the meaning of a still photo taken at a point in time, adding AI and automation to this marketing practice is like enabling brands to leverage a continuous live video feed of shifts in customer needs and sentiment. Smart use of AI also reduces the need for costly data teams, enabling marketers and business managers to implement insight-driven improvements on the fly.

Simply put, analyzing customer sentiment data with AI goes beyond the human ability to spot correlations and customer trends. By collecting continuous marketing intelligence – including customer feedback on social media, review sites, surveys, service interactions and other touchpoints – a smart, AI-driven approach empowers brands to be much more responsive and more confidently align business practices with what customers really want. Deploying an AI-centric strategy can therefore also perform the same analysis compete companies to discover useful insights, such as identifying practices that win over those competitors’ positive customer sentiment and may be worth following.

For example, a hospitality company implementing AI-based customer sentiment data analysis may discover that a direct competitor’s customers make many positive mentions of the hotel’s high-quality breakfast options. Automated analysis would then present this actionable insight as an easily digestible key takeaway: By investing in a breakfast menu that matches or exceeds the quality of the competitor’s, the brand has a likely path to a more satisfying customer experience, improved ratings, and long-term customer and revenue growth.

Similarly, a coffee chain may discover that competitors are gaining positive customer sentiment for their variety of alternative milk options, and adjust their offerings to capitalize on that clear opportunity.

If used properly, little findings like this, hidden in noisy data, can nevertheless change a brand’s competitiveness in their marketplace.

Stas Tushinksiy is the CEO of Instreamatic.