How AI and automation change brands’ approach to market research
AI and automation are changing brands brands’ approaches to market research. Market research has long been a component of the toolset for brands looking for consumer insights to inform decision-making, enhance customer experience (CX), and ultimately fuel development.
Another question is whether or not it is truly useful. In a typical market research project, brands invest (sometimes substantially) in gathering data that amounts to a one-time snapshot of the attitudes of current customers and, possibly, the key differentiators of rival brands.
While this research can provide insightful information, it frequently misses the needs of potential consumers or fails to properly correlate data that would indicate the precise reasons why customers are with competitors.
When relying on conventional market research, brands run the risk of missing the forest for the trees. They become mired in handling complaints and neglect to consider the underlying factors that influence a customer’s decision to buy one brand over another.
In addition, market research projects are too expensive to repeat frequently and only provide restricted insights that start to become outdated as soon as the research is over.
Instead, some marketers use social listening services to conduct more frequent analyses of consumer behavior (and customer engagement with specific features or brand offers).
In comparison to hiring a market research firm to conduct one-off studies, this technique typically results in far more affordable consumer reviews and feedback collection.
This strategy, however, continues to blind marketers to rivalry and the changes that are most likely to win over those potential customers.
In order to extract insights from firehouse data, social listening systems also generally rely on manual processes.
Correlations between the data may very well be found by skilled data analyst teams performing this time-consuming effort, but such talent is not inexpensive.
Read also Batman Takes Center Stage In The Flash’s First Poster
Due to the flaws in both conventional market research and social listening platforms, there are often
untapped chances to significantly and quickly improve consumer experiences.
Artificial intelligence (AI) and automation may very well be the solution for outdated market research and
insufficient social listening platforms, as they are for the entire technology environment.
Brands may eliminate the guesswork involved in locating and connecting pertinent data about customer
experience by deploying AI to gather ongoing marketing insights from the appropriate data sources.
The major drawbacks of conventional market research are directly addressed by AI-driven automation,
which transforms the cost, cadence, and quality of insights gathered.
Marketers should be looking for real-time, always-on data that demonstrate clear correlations rather than
budgeting out expensive research studies on a sporadic basis and changing their customer-facing tactics only that frequently.
Adding AI and automation to this marketing approach is like letting firms use a constant live video feed
of shifts in customer demands and sentiment, whereas traditional market research is like extracting meaning from a still snapshot captured at one specific moment in time.
By reducing the need for expensive data teams through the intelligent use of AI, marketers, and business managers can immediately put insight-based changes into practice.
Read also Meta unannounced VR headsets
Simply said, using AI to analyze consumer sentiment data goes beyond what humans are capable of doing in terms of identifying connections and customer trends.
A clever, AI-driven approach enables brands to be much more responsive and confident in aligning business practices with what customers actually want by collecting continuous marketing intelligence, such as customer feedback across social media, review sites, surveys, service interactions, and other touchpoints. Using an AI-centric strategy, competitive organizations can then undergo the same study to gain beneficial insights, such as identifying techniques that help those competitors attract loyal customers and may be worth copying.
For instance, a hospitality company that uses AI-based consumer sentiment data analysis may discover that many of its direct competitors’ customers are complimentary of the hotel’s excellent breakfast offerings.
The automated analysis would then summarize this actionable insight into one succinct key takeaway: by making an investment in a breakfast menu that is on par with or better than that of the competition, the brand is likely to improve customer satisfaction, ratings, and long-term customer and revenue growth.
In the same way, a coffee chain may learn that its rivals’ selection of different milk options is receiving favorable consumer feedback, and it may then modify its products to take advantage of that obvious potential.
Small discoveries like these that are concealed in noisy data, when properly tapped, can nonetheless drastically alter a brand’s competitiveness in its market.