Shopper packaged goods (CPG) and fast-moving shopper goods (FMCG) companies must use shopper research insights to drive brand recall, purchase intent, and even shopper retention. However, traditional methods of shopper research can sometimes be detrimental to a brand’s success.  

Here’s why.  

Challenges with Traditional Shopper Research

Slow Research

Traditional consumer research can be slow due to the time and resources required to conduct surveys, gather data, and analyze results. The process may also involve lengthy approval and decision-making procedures, leading to delays in implementing changes based on research insights.  

Lack of Actionable Insights  

Traditional shopper research often relies on surveys and focus groups, which can be limited in delivering deep insights and fail to capture the complex emotions and behaviors of shoppers. The data collected may not be sufficient to provide actionable insights that lead to meaningful changes in product development or marketing strategies.

Stated Response Bias  

Traditional research often relies on direct feedback and self-reported data, which does not capture implicit or unstated feedback. The feedback is then redundant in understanding consumers’ underlying attitudes, emotions, and behaviors that may not be consciously expressed. Traditional research methods may not accurately capture shoppers’ actual behaviors and preferences. Shoppers may say one thing in a survey, but their actual behaviors may be different.

Scattered Sources  

Traditional shopper research often involves multiple teams working on different aspects of the research process, from data collection to analysis and reporting. This can lead to scattered resources, with each team using different tools and techniques to gather and interpret data. Additionally, traditional research may not always involve a holistic approach that considers the entire shopper experience, leading to further fragmentation of resources and insights. Traditional research methods may not be able to build a connection between the brand and the shopper, which can limit a brand’s ability to create a strong emotional connection with its target audience

Costs Too Much

Conducting traditional shopper research can be an expensive process, which can be a significant challenge for smaller brands with limited budgets. Traditional methods like central location tests (CLT), computer assisted telephone interviewing (CATI), and in-home usage test (IHUT) are expensive to execute – they come with large logistical overhead, recruitments & training costs.  

Brands that rely solely on these methods may miss opportunities and make misinformed decisions, which can have a negative impact on their success. Brands that want to stay ahead of the competition should incorporate AI-led shopper research.  Insights are 4x faster.  Brands can capture shoppers’ emotions, and help build a strong emotional connection with the target audience by improving brand recall and purchase intent.  

Eliminating Challenges with AI-led Shopper Research  

The way forward is to adopt AI-led platforms to conduct end-to-end shopper research. With AI-led shopper research platforms, CPG/FMCG brands can eliminate all the drawbacks, mentioned above, posed by traditional shopper research.  

Agility  

AI-led shopper research platform empowers researchers with efficient end-to-end shopper research, and marketers with actionable insights in an agile manner. Researchers can break a research project into segments, conduct the segmented studies parallelly and receive actionable insights 4x times faster.

Platforms have built-in capabilities to set-up pre-survey for before presenting the stimulus (concept, creative, ad, or pack design that needs to be tested) to the respondents. There is a post-survey set-up; for after the respondents have viewed the stimulus. Researchers can generate a survey link and even share among their respondents. The feedback received from the respondents is intelligently categorized onto the dashboard for easy consumption and accurate decision making.  

Actionable Insights

AI-led shopper research platforms are to be praised for their actionability quotient. These platforms don’t just deliver shopper insights on their likes and dislikes – they also recommend edits or modifications on the stimulus. Decision makers no longer have to rummage through insights to figure out the next course of action, it will be suggested by the intelligent platform.  

In order to make insights more actionable, the platforms also come with AI technologies.  

AI Technologies  

AI technologies such as facial coding, eye tracking, and voice AI can are used in consumer research platforms to generate accurate and objective data on shopper behavior and preferences. These technologies can detect subtle facial expressions, eye movements, sentiment, and voice tone to uncover implicit responses, leading to more targeted and effective marketing strategies.  

Single Source of Truth

AI-led consumer research platforms can be a single source of truth as they use advanced technologies to gather and analyze data. With end-to-end shopper research conducted on the platform, there is absolute visibility into the research process.  

The shopper insights are then presented on an easily consumable dashboard, making it transparent to all stakeholders. Researchers and marketers can refer to shopper insights of previous projects within the platform instead of having to flip through folders and files.  

Lesser Cost  

AI-led agile consumer research platforms can save research costs with faster data collection, automating analysis, and targeting specific shopper groups. This can reduce time and resources needed to collect and analyze data and lower costs associated with planning and executing the shopper research.  

Online Panel  

AI-led shopper research platforms offer global online panels of respondents. This provides a diverse and representative sample of shoppers from around the world. Researchers and marketers can gather insights into how different cultures and regions interact with products and services. CPG/FMCG brands will have a clear picture of shopper behavior. Online panels can be accessed quickly and cost-effectively. Researchers can get feedback and insights from a large and varied group of participants.

Quant+Qual  

AI-led shopper research platforms come with both quantitative and qualitative capabilities.  Quantitative data allows for the analysis of numerical data at scale, while qualitative data provides in-depth insights into consumer attitudes and motivations. By combining both approaches, researchers better understand consumer preferences, behavior, and attitudes, leading to more accurate and insightful results.  

Collaborative Platform  

An AI-led consumer research platform is a collaborative platform that enables researchers, marketers, and decision-makers to work together. Teams can collaborate on projects, analyze data, and make data-driven decisions, leading to faster insights and more effective marketing strategies. The platform’s automated analysis features can streamline data processing and free up more time for collaboration and strategy development.

Adopt AI-led Shopper Research  

Adopting AI-led consumer research platforms can eliminate the drawbacks associated with traditional research methods. There are only bright sides to working with such a platform. Without an AI-led consumer research platform, CPG/FMCG brands have no scope of keeping up with the constantly shifting market.  

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