KEY TAKEAWAYS:
Understand why emotional interpretation, not the review itself, is usually what sends a retail decision off course.
Learn a four-step method for documenting your read on customer feedback before you act on it.
Apply a ready-to-use AI prompt that argues the opposite case against your own interpretation.
Identify the gap between what a review says and what you’re assuming it means.
How to Use AI to Fact-Check Your Customer Reviews
You reread the text three times. “Had a great time, we should do this again sometime.” Sometime. Not Friday, not next week. Sometime. By the fourth reading, you’ve decided this means they’re not interested, and you’ve already started drafting the casual, low-stakes reply that protects you from ever finding out you were wrong. If this feels like something you did when you were dating, you’re right, and we can’t help but take our personal self-doubt or assumptions into our businesses.
Retailers do this with reviews. A customer writes, “staff was nice, a little pricier than I expected”. And by the second read, it becomes evidence of a pricing problem, possibly a competitive threat, or a reason to offer a discount. The review states one thing. The story built around it says something else, and the “imagined story” in your mind is what gets acted on.
The Read You Get Wrong Doesn’t Stay Small
A team at an international eCommerce retailer noticed the average order value was sliding and went to their analytics group with an ordinary-sounding question: Which promotions would bring the average order value back up fastest? The resulting mix of advertising, bundled discounts, price cuts, and upsell offers produced real short-term spikes in average order value, while customer engagement and profit margins kept eroding underneath it. Half a year later, customer surveys revealed the actual problem: shoppers had stopped trusting the product’s quality and delivery reliability, and no promotion would ever fix that.
The team wasn’t wrong that something needed fixing. They were wrong about what—and they didn’t find out until the wrong fix had already cost them six months.
This isn’t a one-time example. Cognitive bias is a well-known, recurring influence on how organizations make decisions, affecting what leaders observe, how they interpret it, and the actions they take. Independent retail isn’t immune just because decisions are smaller and faster. A quick judgment based on a few reviews or casual comments from customers poses the same risk as a boardroom reading a spreadsheet.
This year, it’s more important than ever. Deloitte’s 2026 retail outlook highlights data-driven insight and AI as two key capabilities that distinguish retailers who grow from those who don’t, and customers aren’t waiting for retailers to catch up on their own. A global study of over 18,000 consumers found that 45% now turn to AI at some point in their buying journey, and about a third use it specifically to interpret reviews before deciding whether to trust a retailer at all. If your customers are already doing a more careful, structured review of your reputation than you are, your own process needs to catch up too.
Larger retailers have already begun building real-time, direct feedback loops into how they make customer experience decisions. Independent retailers don’t need a customer data platform to do the same thing at a smaller scale. They need four steps and about twenty minutes.
“Every retailer thinks they know their customer. Most have never written down why.”
– Jennifer Shaheen
President and Founder, Technology Therapy® Group
Write Down What You Think You Know
Before you review any feedback or social mentions this week, write down, in simple language, what you honestly believe your customers think of you. Not what you wish they thought. What you would bet money on if someone asked.
For most retailers, this belief has never been documented. It exists as a feeling, formed from a few memorable comments, a couple of tense customer interactions, and whatever came up in the last team meeting. That’s not a criticism. It’s just how assumptions develop when no one is forced to state them clearly. The problem is that unstated assumptions aren’t challenged. They simply get acted on.
Keep this first step to a few sentences. “Customers love our staff, but think we’re overpriced compared to the competition.” “People are switching to us because service elsewhere has slipped.” Whatever it is, get it in writing before moving to the next step.
Map Out Why You Believe It
Now find the evidence for the belief you just wrote down. Collect the actual reviews, comments, and messages that influenced it, not just a vague feeling that people have said something, but the specific words that support your point.
This step does one of two things. It either confirms the belief with real, citable examples, which is useful information on its own, or it reveals that the belief was built on far less than it felt like. Three comments from the same week can feel like a trend. Written down next to the dates, they might just be three comments. (I’m a girl who appreciates data and a spreadsheet 🙂 )
This is the step most retailers skip, because it’s the one most likely to be uncomfortable. Writing down the evidence forces you to notice when there isn’t much of it.
“Confirmation bias doesn’t feel like bias. It feels like being right.”
– Jennifer Shaheen
President and Founder, Technology Therapy® Group
Decide What You’re Going to Do About It
Name the action your belief is about to justify. Be specific. Not “improve pricing perception,” but “create comparison posts for social” or “add pricing FAQs to product detail pages to educate the customer on the journey.” Not “address the staffing complaints,” but “hire two more sales associates with strong personal skills that align with our values for the holiday season.”
This step matters because vague beliefs lead to vague actions, and vague actions are hard to evaluate later. A specific action, written down, is something you can check the results of later. It’s also the point where the cost of being wrong becomes concrete.
Let AI Make the Counter-Argument
This is the step that catches what the first three miss. Take the belief, the evidence, and the planned action you just wrote down, and hand all three to an AI tool with instructions to argue against you.
A starting prompt:
(fill in the blanks for your business)
Context:
We are a retail store specializing in ___.
Role:
Execute the tasks below as an expert in ___ retail and customer behavior and retail growth strategy.
Task:
Review my read on our customer reviews, the evidence I’m using (include list) and the action I’m planning to take.
Objective:
Play devil’s advocate following the role defined above within the context of my business.
Explain:
- What else could this feedback mean?
- What would have to be true for my planned action to be the wrong one?
- What am I not considering?
The goal isn’t to seek AI’s approval before acting. Instead, it’s a valid counter-argument that your own reasoning must withstand before committing money or staff time. AI is especially useful here because it can analyze a large amount of scattered feedback that would be too overwhelming for anyone to read or debate. A recent informal poll showed that about six in ten jewelers already use AI in their businesses, so this isn’t a new tool for an unfamiliar task. Rather, it’s a tool you’re likely already using, now focused on a specific question.
If doing this four-step process on your own feedback isn’t feasible most weeks, it’s exactly the kind of audit or mentoring we help clients with. You don’t have to develop the habit alone to gain the benefits.
The Business Case
The McKinsey case discussed earlier is worth revisiting. The team’s mistake wasn’t the promotion itself; it was launching it without verifying if their understanding of the problem was correct. That’s precisely the issue the four-step habit above aims to fix, and it’s no longer optional. The IBM-NRF study revealed that a third of consumers already use AI to analyze reviews before deciding whether to trust you. If your customers are conducting a more thorough assessment of your reputation than you are, it’s something to consider before it costs you the six months that team lost.
That text message you reread four times might have meant exactly what you decided it meant. Or it might not have. The only way to know is to ask, not assume, and the same is true for the review sitting in your inbox right now. Write down what you think it means before you act on it. Then let something else make the case against you.
Get a Second Opinion on Your Read
Catching your own bias is hard to do alone. If you want an expert take on how you’re reading customer feedback, or any part of your marketing strategy, a Technology Therapy Group coach can help you spot what’s easy to miss before it costs you time and money.
