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For brands hunting for customer insights to drive decision-making, improve customer experience (CX), and ultimately spur growth, market research has long been part of the toolkit.
Whether it’s actually helpful 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, competitors’ prevailing differentiators. While this research can yield useful insights, it usually fails to recognize the wants of potential customers, or adequately correlate data that reveals exactly why customers are with competitors.
Brands can easily miss the forest for the trees when relying on traditional market research. They get bogged down in addressing complaints while missing out on the fundamental reasons for why a customer chooses one brand over another. At the same time, market research projects are prohibitively expensive to repeat with regularity, and offer limited insights that begin to go stale from the moment research is completed.
Some marketers instead leverage social listening platforms for more continuous analysis of customer behavior (and customer engagement with specific features or brand offers). This strategy can collect useful customer reviews and feedback, and tends to be much more affordable than commissioning one-off market research studies.
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However, this approach still leaves marketers blind to competitive activity and the adjustments that are best poised to win over those potential customers. Social listening platforms also require largely manual processes to sift insights from firehouse data. Talented data analyst teams doing this time-consuming work may very well identify correlations across 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 agilely improve customer experiences regularly go undiscovered.
The answer to legacy market research and incomplete social listening platforms—as it is across the broader technology landscape — might very well be artificial intelligence (AI) and automation.
With AI deployed to round up continual marketing insights from the right data sources, brands can remove the guesswork from 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 that would otherwise budget out expensive research projects periodically — and adjust their customer-facing practices only that often — should be seeking real-time, always-on insights that show clear correlations.
If traditional market research is like deciphering meaning from a still photograph taken at one moment in time, bringing AI and automation into this marketing practice is like allowing brands to leverage a continuous live video feed of shifts in customer needs and sentiment. Smart use of AI also curbs the need for expensive data teams, enabling marketers and business managers to directly implement insight-based improvements.
Simply put, analyzing customer sentiment data with AI reaches beyond the human capacity for recognizing correlations and customer trends. By collecting continuous marketing intelligence — including customer feedback across social media, review sites, surveys, service interactions and other touchpoints — a smart, AI-driven approach enables brands to be far more responsive and confident in aligning business practices with what customers actually want. Deploying an AI-centric strategy can then also perform the same analysis on competing businesses to discover useful insights, such as identifying practices that win those competitors’ positive customer sentiment and may be worthwhile to emulate.
For example, a hospitality business that implements AI-based customer sentiment data analysis might find that a direct competitor’s customers make many positive mentions calling out 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 that competitor’s, the brand has a likely path to a more satisfying customer experience, improved ratings, and long-term customer and revenue growth.
In the same way, a coffee chain might discover that competitors are winning positive customer sentiment for their variety of alternative milk options, and adapt their offerings to capitalize on that clear opportunity.
When harnessed correctly, small findings like these hidden within noisy data can nevertheless transform a brand’s competitiveness in their market.
Stas Tushinksiy is the CEO at Instreamatic.
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Author: Stas Tushinksiy, Instreamatic