Insights from our fourth feedback masterclass

In this masterclass – Closing the Feedback Loop – we… closed the loop on our feedback best practice masterclass series.

In our final session, Feefo's Customer Success Manager Bonnie Bakoh and Data Product Manager Charlie Newman were joined by Stephen Teeuw from Peter Nyssen. They discussed how to use reviews to build ongoing relationships with customers, and how to use the data within reviews to improve products and services over time. Here are the highlights.

Key takeaways

• Actionable data beats volume: the most successful businesses don't just accumulate reviews – they build scalable processes to listen, learn and act on what customers tell them.
• A loop is more than a reply: true closed–loop feedback is an ongoing operational cycle – collection, understanding, action, communication and measurement.
• One size doesn't fit all: tailoring outreach channels (email, In Mail, or personalised channels for VIPs) to specific customer segments increases response rates, producing richer insight and clearer root–cause analysis.
• AI scales consistency, not just speed: Peter Nyssen use AI Replies to increase their reply rate from around 50% to 75%, reaching thousands of customers a year who previously went unacknowledged – turning a one–person task into a team–wide process.
• Sentiment is your smoke detector: text sentiment and topic categorisation catch operational friction long before your average star rating, or your bottom line, takes a hit.

What “closing the feedback loop” means in practice

Bonnie opened the session by clearing up a common misconception: closing the loop isn't just replying to a review. It's an end–to–end operational strategy – collecting, understanding, acting, communicating and measuring, on repeat.

Why context is what makes feedback usable

Data collection must be intentional. To turn feedback into business decisions, you need contextual detail attached to each review – who the customer is, what they bought, and whether they're new or repeat. Without that context, feedback is hard to act on.

Smart segmentation

Rather than sending a single default request, map your outreach to the audience:

• New customers: email, to establish a baseline relationship.
• Repeat customers: InMail, to reduce inbox fatigue.
• VIPs: a more personalised, direct touchpoint – such as WhatsApp or SMS

Bad isn’t always bad

Negative feedback is free consultancy from your customers. The businesses that improve fastest aren't afraid of it – they actively seek to understand it. Listening to and learning from unhappy customers is a quick way to know where to make improvements.

Peter Nyssen: how to scale authenticity with AI Replies

Our guest speaker, Stephen Teeuw (Marketing at flower bulb specialist Peter Nyssen), shared how a small, lean team scaled customer engagement using Feefo's AI Replies.

Peter Nyssen operates in a seasonal space: customers buy bulbs in seasonal windows but don't see them flower until months later. This creates two very different review cycles – an immediate service review (largely about the courier), and a delayed product review sent once the bulbs have flowered.

Before AI Replies, Stephen personally handled every negative review (3 stars or below), while the 97% of customers leaving 4– and 5–star reviews heard nothing back.

By introducing AI Replies, the business have seen clear results:

• Wider coverage: reply rate rose from around 50% to 75% of reviews – reaching an estimated 2,558 more customers a year, roughly 53 a week, who previously received no response at all.
• Team consistency: response management moved from a single–person bottleneck to a shared process, with anyone on the team able to check and send consistent, on–brand replies in seconds.
• Securing lifetime value: acknowledging a 5–star review takes under 30 seconds, but it reinforces the trust and loyalty needed to secure repeat seasonal purchases.

Macro insights: turning sentiment into strategy

Review management requires using both individual, one–to–one replies and utilising macro–level insight.

The smoke detector analogy

Average star rating is objective; text sentiment is descriptive. Sentiment tends to dip when customers are mildly frustrated – acting as a smoke detector well before that frustration shows up in your star rating or your revenue.

The limits of generic AI

Dumping thousands of reviews into a generic AI tool produces a "summary illusion" – a vague overview that lacks business context. A genuinely useful approach needs a structured, multi–layered framework:

Macro fixes vs micro fixes

Charlie shared a practical way to think about acting on this data. If sizing is found to be hurting your rating in a specific market, the macro fix – redesigning the product or the size matrix – could take months. The micro fix can go live in hours: a banner telling shoppers in that market to size up. The rating is protected immediately, while the longer–term fix is worked on in parallel.

How reviews influence AI search visibility

A key theme of the session was how review data future–proofs visibility in AI search. Customers increasingly use natural–language prompts in tools like ChatGPT and Gemini – and the language they use in those prompts often mirrors the language they already use in reviews.

Feefo's website widgets are built to be crawlable by AI models, meaning reviews on your site can be picked up and cited directly by AI models. This means that if a feature or benefit is driving strong sentiment in your reviews (for example, "waterproofing" or "next–day delivery"), it's worth moving into your on–page headlines and copy – because that's the language AI tools are matching against when they build comparison results for shoppers.

Three steps to operationalise your feedback loop

Bonnie closed the session with 3 practical steps you can take away and apply to your business right now.

1. Prioritise context over volume: structure metadata (new vs repeat buyer, product line, region) before you collect feedback, not after.
2. Engage every voice, not just the loudest: use automation to respond consistently across the full range of ratings, rather than leaving the quiet majority unacknowledged.
3. Isolate and measure what's suppressing your score: use thematic tracking to pinpoint specific issues mathematically, and pair a fast micro–fix with the longer–term solution.

That’s a wrap

Over four sessions, we've covered how to collect, display, analyse and act on one of the most valuable assets your business has.

We ran the series to help businesses understand how to make the most of their reviews, UGC and the insights they’ve collected.

Refresh your knowledge by starting again with How to Collect Feedback that Matters.

Want to see what a closed feedback loop could look like for your business? Speak to us to see how we could turn your feedback into measurable growth.