As we come to the end of 2024, RFIDJournal writers and experts who contributed their knowledge are taking both a look back at this year and what is ahead in 2025. This entry is from Anton Timashev, Wayvee Analytics CEO.
Why is customer satisfaction more than just a metric for brick-and-mortar retailers? It’s a key differentiator that directly impacts both lifetime value (LTV) and customer retention, which are essential for profitability.
Research shows that increasing customer retention by just five percent can lead to a 25 percent–95 percent rise in profits, as retaining customers is more cost-effective than acquiring new ones. Repeat customers also spend 67 percent more than new ones, often with higher average order values, making them essential for sustained success. While acquisition fuels growth, focusing on retention ensures long-term profitability.
For brick-and-mortar retailers facing competition from e-commerce and other big players, maximizing customer satisfaction (C-SAT) is essential, as losing a single customer can be a costly setback in such a competitive landscape. In the U.S., even when people love a company or product, 59 percent will walk away after several bad experiences, 17 percent after just one bad experience. (PwC)
Customer Satisfaction (C-SAT) Current Measurement Tools: Are They Enough?
Survey tools and mystery shoppers have long been effective tools for retailers seeking insights into customer satisfaction. Surveys allow retailers to gather direct feedback and track customer satisfaction over time, and mystery shoppers provide valuable perspectives on in-store experience consistency. Both methods have proven beneficial in understanding customer needs and identifying areas for improvement.
However, these traditional tools face limitations. Surveys often depend on customers’ recall and may capture only the perspectives of a small percentage of customers. This limited data could lead to biased findings, and by the time feedback is processed, critical insights may already be outdated. Research by Accenture suggests that nearly one-third of consumers feel surveys fall short of capturing their real experiences, leaving retailers with partial or delayed insights. Similarly, while mystery shoppers offer a useful snapshot, they capture only one perspective, rather than the experiences of a full customer base.
Moreover, processing this feedback often takes weeks or longer. As a result, the data is not provided in real-time, creating an insight gap — especially as retail environments become more dynamic and require faster responses to customer needs.
How People Make Decisions and Form Memories: Beyond Rational Thinking
Understanding customer decisions requires recognizing the complexity of human behavior, which can’t always be captured through direct feedback alone. Insights from neuroscience, psychology, and behavioral economics indicate that humans are far from purely rational decision-makers. Instead, our brains use cognitive shortcuts — known as heuristics — that shape our judgments and memories.
Memory is selective, capturing “snapshots” of key moments rather than every detail. This selectivity leads to biases like the peak-end rule (where we judge experiences based mainly on their most intense moments and the end), duration neglect (overlooking time spent), and recency effects (favoring recent information). These biases shape how customers remember experiences, often leading to judgments that don’t fully reflect the entire event (XM Institute).
Consumer psychology studies also show that much of this decision-making is intuitive and emotional rather than rational. Emotional factors, which influence up to 95 percent of purchase decisions (Harvard Business School), play a substantial role in shaping customer experiences and expectations. This underscores the need for businesses to go beyond rational surveys and surface-level feedback, instead tapping into the emotional drivers that guide customer choices.
Emotion Data: The Key to Better Customer Experiences
Imagine this: emotions drive 95 percent of purchase decisions, yet brick-and-mortar retail — a $22 trillion industry — operates without a clear way to manage them. What could be achieved by implementing an emotion-driven system?
These insights provide a more nuanced understanding of the in-store experience and can be actionable with proper analytics and real-time delivery. For instance, if a customer’s emotional signals indicate frustration, employees can intervene before the customer leaves unsatisfied, turning the encounter into a positive one. This proactive approach has tangible business benefits: McKinsey’s research shows that companies prioritizing emotional connections with customers see a 20-40 percent boost in revenue growth.
These insights also help create more personalized experiences by adjusting in-store media and promotional content in real time. Digital ads or offers can be tailored to match customers’ reactions, making the messaging more relevant and impactful. This approach allows retailers to deliver engaging interactions that boost customer experience and potentially drive sales.
Emotion Recognition and Privacy: The Next Step in Customer Experience for Physical Retail
To gain these insights, retailers increasingly turn to emotion recognition or Emotion AI technology that analyze nonverbal cues by analyzing facial expressions, body language, and other behavior patterns. The technology uses AI or Machine Learning (ML) to interpret customers’ emotional responses following interactions with products online or in-store.
In e-commerce, emotion recognition technology often relies on analyzing data like facial expressions, text sentiment, voice tone, and body language, made possible by an easy access to users data. However, these methods often raise privacy concerns due to the personal nature of the data being collected.
For physical stores, where privacy concerns are even more pronounced and direct data collection is challenging, traditional methods can be ineffective or invasive from a customer perspective. Recent advancements, however, provide more effective solutions, such as analyzing physiological signals and using highly-sensitive radio waves and AI. This approach offers valuable insights into customer emotional responses while respecting privacy, making them ideal for in-store environments.