Beyond surveys: Innovative techniques in modern market research

When I used to mention I worked in market research, the response tended to be, ‘Oh! You’re one of those annoying people who ring me when I’m having dinner to ask my favorite brand of toothpaste!’ 

That was in the days of landlines and the ubiquitous telephone survey. Traditional market research was associated with focus groups and the serious-looking interviewer carrying a clipboard and a pencil, asking questions, and ticking boxes.

[Image: Person holding a clipboard conducts a survey.]

Indeed, there’s nothing wrong with traditional market research. It’s worked for decades, helping businesses understand their audience, develop new products, and align with consumers’ needs, wants, and expectations.

However, like almost every other category under the sun, the relentless evolution of technology, digitization, and online has given rise to many new techniques going beyond these conventional approaches, offering faster and more efficient ways to get answers.

Want to predict customer behavior? AI and machine learning can do that.

Are you curious about subconscious reactions to your product? Neuromarketing can help. 

VR and AR let you simulate real customer experiences, and social listening tools give you instant insights into how your brand is perceived online.

This article explores these emergent approaches and how they’re rapidly changing the face of market research.

You can’t have a conversation today about the future of market research without discussing AI, so it’s worth tackling this first. 

I’ve taken a deep dive into AI in a series here, so I’ll keep it brief. 

In short, AI and machine learning (ML) increasingly allow researchers to automate key areas of the research process, especially data analysis of large qualitative datasets, and generate near-instant thematic analysis of raw research outputs. 

Given the correct data, AI technology can model future consumer behavior, identify broader market trends, and even automate marketing content creation tailored to specific audiences. 

Undoubtedly, AI algorithms will improve over time, learning from new data to refine their predictions and recommendations, enhancing the accuracy and relevance of market research outputs.

It’s worth highlighting that, even if it may not look great, all is not lost for human market researchers. Again, I’ve discussed this in detail here (AI can't do it all just yet). Its analysis and recommendations still need expert oversight to check and tweak them, ensuring they are meaningful and actionable in real-world situations. Human oversight of data quality and collection remains critical.

The growth in big data analytics (closely linked to the rise of AI, which leverages vast amounts of big data) is changing how market research is delivered. By integrating extensive big datasets with conventional insights (those generated by traditional surveys or qualitative data), researchers can triangulate findings around consumer understanding, uncovering deeper patterns and trends that smaller datasets (or traditional methods) alone might miss.

Big data typically leverages diverse data sources. For example, an extensive dataset may include 

• Customer transaction records (bank statements, purchase history)

• Social media behavior (likes, shares, comments)

• Mobile usage (call length, data usage )

• Increasingly, the range of IoT-connected devices (wearable fitness trackers, smart speakers, connected appliances)

This comprehensive collection provides a more holistic view of the market and customer behavior, leading to richer insights and better-informed business decision-making. 

Netflix is an example of big data analytics in action. It integrates extensive datasets with traditional surveys and qualitative data to enhance its content recommendation systems, ensuring every viewer gets the best recommendations for their taste. Netflix tracks and analyzes a vast array of big data, including customer transaction records (viewing history, subscription plans), social media behavior (ratings and reviews), mobile usage (watching times and locations), and IoT devices (smart TVs, streaming devices). 

Machine learning and AI algorithms analyze these vast datasets to predict user preferences and recommend content users are likely to enjoy. Finally, this data gets triangulated with reported consumer data—qualitative, quantitative, and UX data to generate the most informed snapshot of consumer behavior and allow Netflix to make the most informed decisions possible.

Businesses look to neuromarketing research to gain a deeper understanding of their customers, often when the return on investment (ROI) for other forms of research has been exhausted. 

Neuromarketing research combines neuroscience and marketing to study how a person's brain responds to marketing stimuli. Researchers may employ EEGs (electroencephalography) and, less commonly, fMRI (functional magnetic resonance imaging) to measure changes in brain activity when subjects see advertisements or brands. 

EEG measures electrical activity in the brain by placing electrodes on the scalp. It provides real-time data based on brainwave patterns, which helps researchers understand how consumers respond to marketing stimuli, such as brand logos, advertising, or product placements. 

fMRI tracks changes in brain activity by measuring variations in blood flow. When a specific brain area activates, it requires more oxygenated blood, which fMRI can detect. It allows researchers to observe which brain regions are involved in processing marketing stimuli, providing insight into emotional and cognitive responses. 

While EEGs are more commonly used (because it's costly to hook a consumer up to fMRI equipment), both EEG and fMRI can reveal emotional and subconscious behavioral drivers, providing deep, more nuanced insight into consumer decision-making than typically gained from survey or focus group findings.

For example, in 2019, Asahi Breweries employed EEG and eye tracking to renew their Asahi Mogitate (shochu-based beverage) pack design. This technology revealed which design elements participants found most appealing or elicited strong emotional responses. 

The new design, featuring prominent text and a large green image, effectively captured consumer attention and drew them to one of the main selling points. Mogitate means “freshly picked” (referring to the featured citrus juice), and participants gravitated to a callout stating, “extracted within 24 hours of harvesting.”

[Image: Heat map using EEG and eye tracking. Sourced from “Case Study: How Asahi Breweries Used EEG and Eye Tracking for Pack Design Renewal.” This image is used under fair use for educational and commentary purposes to highlight key insights. All rights to the image and case study remain with the original copyright holders. Please refer to the original case study for more details.]

A simple but critical truism in product retailing is, “If the customer can’t find it, they won’t buy it.” 

This fact has driven billions of dollars of investment from brands for product branding, design, point of sale (POS) materials, and messaging, all to aid consumers with locating products and impressing them sufficiently so that when they do find them, they pick them up and put them in their cart.

[Image: VR helps test shelf placement for new Pop-Tarts products. This image is used under fair use for educational and commentary purposes to highlight key insights. All rights to the image and case study remain with the original copyright holders.]

Before AR and VR, evidence gathered through market research was required to justify costly investment in packaging changes or in-store messaging, especially if a store owner had to absorb some of the wayfinding or signage expenses. 

The traditional approach to gathering this data was to do a shop-along with consumers in real-world stores or smaller test stores—buildings mocked up to look like supermarkets—to test brands, layouts, and messaging. 

However, these conventional methods have limitations: actual stores were often reluctant to allow research on behalf of specific brands, and setting up mock stores was time-consuming and expensive (requiring purchasing products to make the environment semi-convincing). 

VR and AR have, therefore, become increasingly commonplace within market research to create immersive testing environments to test new products, innovation, messaging, and branding without the costs and restrictions of real-world or mocked-up environments.

For example, VR can simulate a virtual store where target customers can visit. Using data from this virtual experience, market researchers analyze how customers navigate the store, interact with products, and make purchasing decisions, all in a controlled yet realistic setting. 

AR, conversely, can overlay digital information onto the real world, helping to test product placements, advertisements, new product logos, or designs in context without needing physical prototypes.

Kellogg’s, for instance, uses virtual reality to improve in-store merchandising. They used VR to build virtual store layouts, allowing them to experiment with different product placements and store designs without setting up physical locations or booking time within retail partners’ stores. By doing so, they could test how different SKUs look and perform in various scenarios. Overall, it gave them insight into what product mix and layout mix worked best, allowing Kellogg’s to tweak their strategy to make products more eye-catching and boost sales.

Market researchers are increasingly tapping into the metaverse to give them further options for gathering insights and understanding consumer behavior. The metaverse has features similar to standard VR, offering immersive experiences in individual virtual environments, like gaming or simulations. 

However, as visualized by leading tech companies like Meta, the metaverse takes it beyond one-off, discrete VR experiences to a vast, interconnected digital universe where users can move between different virtual worlds, socialize, work, and interact continuously.

For brands and businesses, the metaverse offers a rich, interactive platform for testing new product concepts, layouts, and advertising campaigns, providing valuable data on consumer preferences and behaviors. Similar to VR and AR, this approach cuts costs and logistical challenges associated with physical testing and opens up new opportunities for deeper, more immersive research experiences and novel forms of insight and data collection. 

Nike, for example, has tapped into the metaverse to enhance its market research and customer understanding through Nike Virtual Studios. In this virtual world, users can design, buy, and sell digital sneakers, allowing Nike to track consumer preferences in real time. By offering customizable virtual products, Nike gains valuable insights into style trends, purchase behavior, and what resonates with its audience—all within the metaverse.

This new testing option allows Nike to explore product concepts and gather feedback without the constraints of traditional research methods. The data collected from virtual sneaker interactions helps shape real-world product decisions, strengthens engagement, and uncovers emerging trends in digital fashion and ownership.

Social media platforms that gather freely shared consumer thoughts, opinions, and attitudes are treasure troves of consumer data, capturing views on various topics, including brands, products, and companies. 

Companies can increasingly monitor real-time discussions about their brand, product, or service through social media listening. Social media listening uses advanced analytics tools to sift through vast amounts of unstructured data to gauge public opinion, track emerging trends, and respond to consumer feedback promptly. The immediacy of social media listening allows companies to stay agile and adjust their strategies based on current sentiments.

L'Oréal has used social listening to quickly identify and respond to emerging beauty trends. They track consumer preferences and feedback in real-time by monitoring online conversations across social media and digital platforms. This approach helps them stay ahead of trends and swiftly adapt their product development and marketing strategies. L'Oréal’s social listening enables them to keep pace with the speed of culture, ensuring that their offerings remain relevant to consumers' rapidly changing needs and interests. This strategy has allowed them to maintain their position as a leader in the beauty industry.

Thematically tied to the previous tool (social media listening), sentiment analysis interprets and classifies emotions within text data using natural language processing (NLP) tools. 

It helps companies quickly understand consumer reactions and adjust their strategies accordingly. It is particularly useful in analyzing customer reviews, social media posts, and other forms of customer feedback to determine overall sentiment towards a product, service, or brand. It automates the once tedious task of manually reviewing vast consumer posts and online feedback to classify the general sentiment of consumer feedback toward a business or brand (positive, negative, or neutral).

For example, through sentiment analysis, the record label Big Machine Label Group (BMLG) effectively monitors public reactions to its artists and campaigns. By tracking positive and negative feedback in real-time, BMLG identifies potential issues early, allowing them to adjust messaging and strategies before backlash occurs. This approach also helps manage internal conversations and flag sensitive topics, ensuring quick responses. 

The market research landscape is evolving rapidly, influenced by technological advancements and changing consumer expectations. While traditional methods like surveys still hold value, integrating newer, more innovative techniques can provide deeper insights and a competitive edge in today's data-driven market. As businesses adapt to these changes, harnessing and effectively using these advanced research methodologies is critical for staying ahead in the marketplace.