How Gen AI is Changing Consumer Research

Consumer research elevated during the ecommerce boom. Now, Generative AI is taking it into an entirely different stratosphere altogether.

Thanks to Gen AI implementation across all aspects of consumer behavior and preferences, the methods and systems to conduct consumer research — to simulate and synthesize it even — are now faster and more effective than ever before. And that will continue to be more true with each passing day as the technology itself and its dynamic capabilities reach new peaks all the time. 

There is now an abundance of information, data and insights available with Gen AI tools and capabilities extracting enormous amounts of data. With integrated systems and knowledgeable companies improving their own abilities to refine information and empower well-informed actions, consumer research as we know it has been fundamentally changed forever. 

More Robust & Refined Consumer Research 

Generative AI’s impact on consumer research, and therefore its transformative effect on the CPG industry, is influencing every facet of companies. Consumer research has new layers of depth coming in, shining a light on bigger trends and changes as a whole while also providing a new path to granular insights on local and regional levels as well as better segmented groups and even individuals. 

Where is Gen AI impacting consumer research, and how can companies utilize these new capabilities? As you can see below, the answer is: everywhere.

Source: Pilot44

It starts with researchers being able to collect the data they need faster, and being able to make sense of it all and refine it more rapidly as well. 

Next, companies are able to enact changes, make data-driven decisions, and even alter entire product approaches on the fly based on the new information they’re receiving. That allows researchers to continue the loop, finding ways to deploy more informed, improved AI to conduct consumer research faster yet again, and the cycle continues. 

Companies can make increasingly quicker steps toward how they operate, being more data-driven across the spectrum with built-in agility to be flexible, all while increasing the accuracy of the research and effectiveness of deployment. 

From product development to packaging and branding, marketing and ad campaigns, even sales and delivery are intertwined in how evolved consumer research is informing emerging consumer insights.   

The next wave, Research 3.0, will alter the course for marketers as well as brands, with innovations to demand generation and brand awareness in almost real-time, with an AI arms race to stay ahead of competition as access to consumer demands amplifies. 

Research 3.0 (Re)defined

Where research used to gobble up uncountable hours and effort, Generative AI such as Chat GPT, has made the availability of information in organized manners much easier to access, and continue with. With the right prompts and approach, the first bits of research feed into being able to refine the research in the moment, digging toward nuggets of insights in record time. 

That’s just one slice of the marketing side of how consumer research is evolving. New capabilities are being integrated in many ways to enhance the input and output revolving around Gen AI consumer research, providing fertile grounds for actionable insights to use in enhancing personalization, for instance. 

Source: Pilot44

Research 3.0 is simply the acknowledgement that AI solutions are already changing things and there are no next steps toward better research-insight-action loops that don’t involve AI. 

Shift From Data Modeling to AI-powered Insights

With direct and nondirect research being deployed with less human intervention, the entire research process – including consumer research – will increase the focus and investment in insights management. 

That doesn’t necessarily mean only investing in insights teams, but investing in upskilling all employees to be better discerning and able to digest and use the new information spawned by Gen AI consumer research. 

There is a huge opportunity for companies to make a seamless shift from statistical data modeling to dynamic, integrated approaches that have robust, reliable insights management, fueled by partnering with and even developing their own AI tools in the process. 

For instance: 

• Opportunity analysis is amplified by Gen AI predictive analysis on the market, trends, competitors, and behaviors based on real and synthetic consumer research data 
• Ideation to prototyping is accelerated based on the research from the last stages, as well as deployment of Gen AI tools to refine the idea to inform the most likely to succeed option, then accelerating the ability to prototype and even gain feedback almost instantly 
• Branding for the new (or improved) product is fine-tuned through deep audience analysis and sentiment analyzation to match needs and wants, turning guesswork into confident decision making that makes an impact and engages consumers 
• Launching the new product is faster than ever as well because the dynamic feedback loop of consumer research empowers a company to understand when and where to launch as well as how

Here’s a small snippet of Gen AI capabilities that can fuel parts of this process: 

Source: Pilot44

Gen AI-powered market intelligence and market research from companies like Pecan, Remesh and Crayon, is augmenting human-powered intelligence and insights to create a 360-degree view of the audience, both holistically and in categorized and personalized segments. 

Furthermore, Gen AI-powered machine learning is helping catalog and categorize the new era consumer research for better access and actionable insights, from companies like Appen. New capabilities are on the way, too, as progress and new tools are developed by emerging companies and innovating corporations. As you can note above, all five of these examples are built with or building off of OpenAI. 

That’s one way Gen AI is transforming the approach to consumer research from the inside. The process of gathering, of refining, of augmenting, and building off of the new consumer research loop — the very thought process and approach to consumer research — is transformed in incremental bits and as an overarching whole. 

Companies that adopt new techniques and capabilities will mark the biggest changes as they innovate new methodologies and ways of viewing the world through consumers’ eyes in the future to come. 

Overcoming the “More-more-less” Dilemma

Improved efficiency, increased access to data, and on top of that, an uptick in the sheer amounts of refined data that a company can seek and explore all lead to a possible paralyzing situation. 

More and more data flows in, faster than ever, and in lockstep there is increased stakeholder pressure to have faster, more effective answers coming from all this rich data. And as we mentioned moments ago, CPG companies in the present lack insights people to expertly handle this new consumer research. 

That’s how we end up with more-more-less, with the opportunity right now to invest in the less to better address the more(s). 

There will be a need for insights people to also have excellent AI tools at their disposal, to help automate so they can keep up with the inflow, and to better be able to scale as things progress progressively faster. 

Generative AI is the binding that can keep this whole loop tied together. Its many forms and functions are a must to help insights people develop and communicate and fulfill stakeholders' (and consumer) demands faster. Faster means multiple things, too, because the path to action will happen quicker than a company is used to. Improving your own efficiency isn’t enough in a vacuum, though, as you also need to be faster than the competition. 

Faster can lead to its own dilemmas as well, increasing pressure to have the consumer research, data understanding, and actionable advice all increasing in speed simultaneously, which can allow for unnoticed mistakes, and things accelerating too fast to ensure the highest level of quality in all steps. 

Market Research Will Rely on Gen AI 

Anyone doing market research now relies on Gen AI. Being able to have a high level of discernment in analysis of what AI provides is increasingly important. Anyone who has progressed their use of Chat GPT, for instance, has learned that it’s just a starting tool for research, and the user must still know how to differentiate what is useful and trustworthy, and what is not, what needs to be explored further, and what is just noise. 

When automation helps negate time-consuming tasks, there is more time to devote to analysis and to explore previously overlooked insights, including people, markets, and niches that might have fallen through the cracks before. 

That’s why being able to discern, and having discerning AI, is so crucial, to avoid moving faster than necessary and over pursuing all that is available with Gen AI providing better market research.

Richer audience understanding

Tedious surveys and focus groups are giving way to synthesized customer profiles with less inherent bias, giving quick snapshots into simulating controlled consumer research that can paint a picture and provide direction on next steps so much faster. 

This access to simulating consumer research will also allow for real consumer research to be more refined, and tailored in a way that can unearth real answers and better understand an audience in a holistic way. 

Source: Pilot44

Specific customer profiles can be built and studied, and details can be updated in sophisticated ways, leading to better opportunities for segmentation and targeting in unique ways. 

How to Leverage Gen AI When Everyone is Using Gen AI

As the focus is on a word we’ve repeated a lot so far – faster – there will be natural opportunities to be more effective and better and refining your own consumer research and feedback loop to gain an advantage in the market. 

Customizing research plans, and using the newfound time to critically research the methodology, the tools, and invest in improvements all along the way is a great start toward that end. 

Consumer insights expertise

Source: ZS

• Market research teams will need to evolve and adapt, with some changing roles, and some needing to enhance cooperation and collaboration at every stage from collection to analysis to suggestions to action to be better engaged. In that way, companies can keep a step ahead of the competition by relying on their internal expertise and ability to have sharp yet flexible perspectives. 
• Spending resources and energy to better understand Gen AI is all but necessary right now, and that is only set to increase. Your teams need to get the full picture about what data sets to pull from, what tools operate in which specific and general ways, and of course keeping an eye toward the fickle nature of people to still be able to make real life sense of all the endless data and insights. 
• Consumer insights (CI) expertise will emerge as a stronger and more sought after specialization. And CI experts themselves will seek to better their understanding of Gen AI and the marketplace, with increasing specialization in the field themselves. Having a team of CI experts that are engaged and interactive with each other and with emerging AI technology is another way to gain a big leg up on competition right now. 
• Actionable insights will be counted on to be more accurate than ever, since the data is coming in faster and more refined, so the teams providing those suggestions must be innovative as well. 

Gen AI is actively transforming how companies act in each stage of the loop we’ve mentioned, meaning it is already a fundamentally transformed process entirely, with no option of competing with what are now archaic approaches that ignore Gen AI. 

A new generation of insights 

Gen AI’s impact on insights is enormous and far-reaching. There is now a new dynamic access to influence changes happening right now, replacing linear progressions, and this will continue to further shape the future landscape through current usage and innovation. 

This new access and research in the new Gen AI approach unveils ways to improve and identify opportunities and challenges 

• Analytical efficiency:
  • AI tools analyze vast datasets in record-time, producing numerous diverse reports with seemingly endless variables
  • Marketing managers and insights teams can collaborate and converge to act on emerging trends, preferences and market opportunities in real-time and periodically
• Resource efficiency:
  • AI all but eliminates the time & energy formerly spent on meaningful data processing
  • A comprehensive view of consumers can emerge by forming centralized, integrated processes, enhancing targeting, personalization & fulfillment.
• Challenges:
  • Real-time insights can still be iffy because dynamic market changes, trends, conditions & competition need to be considered with a discerning mind
  • Fleeting fads leave companies susceptible to unstable natural human tendencies and short-term behaviors, so systems need to be in place to separate the noise 
• Risk management:
  • To minimize challenges & setbacks you need to be able to understand & process core data sources & methods 
  • With data processing acceleration there is always a risk of lower accuracy, which can be mitigated by reinforced strategic feedback loops & analysis
• Quality assurance:
  • Inaccuracies, outdated information, unverified sources & reliability can blemish quality, which is where human interaction with the data is still vital
  • It’s all about refining and defining your approach & goals to choose the right tools & capabilities for reliable analysis and forecasting to boost confidence with leadership & decision-makers
    
    
    

Emerging Gen AI Advantages 

Source: Pilot44

There are already quite a lot of ways companies can start (or continue) incorporating Gen AI into their research and insights. Let’s run through some prominent ones. 

Consumer profiling and targeting 

AI can provide a comprehensive view of customers and potential customers with the creation of detailed profiles that include specific preferences and behaviors. That way, target markets can be more effectively identified and assessed. 

Research planning and efficiency

This is where customizing and automating comes into play. Outreach can be improved by designing customized research plans and automating data analysis, feeding each other and allowing more time and access for experts to engage with. 

Enhanced data integrity 

Although AI can be influenced unknowingly by the people that design or program it, overall accuracy is improved because AI removes biases with synthetically generated data and algorithms. The methods can also account for changing preferences, demographics, and more to better increase accuracy too. 

Insights & forecasting

From predictive modeling to pattern detection, a more holistic view is available through AI. Future market scenarios can be better predicted and planned for, while patterns emerge and are detected in real-time. Forecasting models and approaches are enhanced as well since things from the past, present, and future can be considered at once in anticipation of market shifts. 

Rich data synthesis & analysis

Since simulations can be conducted quicker, that means more of them can occur in a way that leads to more realistic data and consumer insights. Synthetic customer research based on real audiences can give accurate, quick feedback. Even “twin” mirroring of audience segments can create a synthetic audience to simulate feedback and reactions from realistic audiences specific to your brand, products and market. 

Source: Gartner

Adding simulated consumer research and audience insights to real life data isn’t a far away affair, as 93% of marketers are already using synthetic data in some shape or form. And that’s not in isolation, as those consumer insights are being shared and integrated with other teams and tools, fueling strategy and development, as well as informing other stages of product development and venture building. 

Open-ended responses can be qualified for quantitative analysis, and AI bots can gather customer feedback, with social listening granting a way to turn emotions into actionable data as well. It all adds up to deeper analysis of consumer’s behavior for your company and for the market at-large, for easier access to understanding their preferences and even sentiments. 

Surveys & reporting

When deployed with the right AI, surveys can be automated and even open-ended answers can be analyzed to provide data on trends, themes, sentiments and emerging narratives from combining factors. 

Enhanced productivity tools 

As we’ve alluded to throughout, increased efficiency in some areas will mean that there are other avenues where the gained time and energy can be harnessed. Increasing productivity is possible, but it will also need to be viewed differently as that productivity shifts from one area to another. 

Creating centralized systems for management and sharing across disciplines will allow companies to feel that they’re informed on every level from the inside and out. As consumer research reels in more information and data, teams can work together to share and even refine in real-time to make better sense of what’s coming in, and how they’ll use it. 

The efficiency possible with AI tools will allow teams to have more robust and yet refined repositories for insights and to summarize information effectively. Conversational search queries and intelligent interviewing are another step toward having the subjective become more objective. 

The process itself is changed by the AI used, and the people involved on both sides of Gen AI – deployment, adapting, iterating, understanding, and acting – will continue on a larger-scale feedback loop that impacts smaller-scale opportunities for advancing and intertwining internal consumer research and insights in ways uniquely beneficial to them. 

Ever-Adaptable Consumer Research is Within Reach

An already heavily data-driven process, CPG consumer research will only become more and more reliant on that. The changes will come from how companies align themselves to be more agile in ways that allow them to continuously become more agile, while also maintaining a commitment to high-level accuracy. 

Knowing the what, how and why of customizing and catering toward consumer insights will allow brands to differentiate themselves when every competitor is customizing as well. Exploring the avenues to more engaging marketing campaigns that feel genuine to consumers means having teams that can authentically connect with each other while constantly adapting their methods to AI. Consumer demand will be more known in real-time, giving access to faster marketing campaigns and product launches to increase brand awareness and sales. 

Gen AI gives CPG companies a distinct advantage opportunity, but it’s still up to the people on the inside to make the newfound efficiencies worthwhile, repeatable, and adaptable to market landscapes that will only dynamically increase into the move to Research 3.0, and beyond. 

Plenty of AI-powered companies are out there providing tools and access for brands. 

Pairing the right AI tool and approach is paramount, and finding the intersection of opportunity and innovation is significantly enhanced by partnering with people and companies who are already immersed in the landscape, the technologies, and the future forecasts can give a company an incredible advantage.