How Connecting Support to Data Lifted Trustpilot from 3.8 to 4.6
By organizing support complaints into usable signals, the team could act on customer experience and focus on the right relationships—lifting Trustpilot from 3.8 to 4.6.
When customer complaints pile up on an e-commerce platform, the natural instinct is to escalate tickets and patch issues one by one. But if your support team has no visibility into the data — no refund rates, no churn signals, no merchant quality metrics — every complaint is just noise.
The real problem is not individual bad experiences. It is a broken feedback loop between the people hearing from customers and the people who can actually act on the data.
3.8
Rating
Feedback loop disconnected
An e-commerce platform operator was watching its Trustpilot score slide to 3.8 as merchant-related complaints stacked up. The support manager was fielding an increasing volume of negative reviews — but had no tools to understand what was driving them.
Three compounding gaps made this impossible to fix:
No refund visibility.
No access to refund data by merchant, so no way to know which sellers were generating the most friction.
No pattern recognition.
No way to distinguish seasonal complaint spikes from structural quality problems. Every issue looked equally urgent.
A silo between data and support.
The support team knew what customers were saying, but not why. The data team had the metrics, but no operational connection to the problem. Neither could act without the other.
The fix started with a conversation — not a dashboard.
Define the right metrics.
Working directly with the support manager, we mapped out what would actually change how the team operated: refund percentages by merchant, churn broken down by year and season, and performance benchmarks across the merchant base.
Build for decisions, not reporting.
We built dashboards that surfaced those metrics in an operational format. Not a report to read — a tool to act from. This bridged the data-support gap for the first time.
Shift from reactive to strategic.
With visibility came a new approach. Instead of chasing bad merchants through escalation chains, the team could identify and invest in high-quality merchants and strong customer relationships. The focus moved from damage control to platform health.
Trustpilot rating: 3.8 → 4.6 — reputational recovery influencing merchant acquisition and buyer trust
Support team gained data visibility for the first time — structured triage instead of reactive firefighting
Negative feedback loop broken — bad merchants no longer went undetected
Repeatable model established — metrics defined jointly, dashboards built for action
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