E-commerce & Digital
Review moderation and fake review detection
Moderate customer reviews for policy breaches and detect incentivised or fake reviews using language patterns, reviewer behaviour, and timing anomalies before they publish.
50–80%
reduction in manual review moderation effort
Opportunity assessment
Minor improvement. Small efficiency gain with limited effect on overall turnover or bottom line.
Relatively straightforward. Off-the-shelf tools handle the core task. Implementation is well-understood with good examples to follow.
Light data requirements. Uses straightforward inputs — documents, product descriptions, customer records — that are usually accessible with minimal prep.
Moderate risk. Some customer or external exposure. Errors create rework or reputational impact but are recoverable.
Light people impact. A new tool slots into an existing workflow. Minimal training needed. Most people will adapt quickly.
Tooling required
Things to consider
The Digital Markets, Competition and Consumers Act made publishing or commissioning fake reviews a specific enforcement risk in the UK, which turns moderation from a quality question into a compliance one (CMA).
Suppressing genuine negative reviews is both a compliance breach and commercially self-defeating. Restrict automated rejection to clear policy breaches and route anything ambiguous to a human.
Behavioural signals beat text analysis. Review timing, reviewer history, and device patterns identify coordinated activity that reads perfectly naturally sentence by sentence.
Experiment starter: Run detection over twelve months of published reviews. Have a moderator assess the top hundred flags, and separately check whether flagged reviewers show clustering across products.
Go deeper in the playbook
Section — Opportunity Identification, including the AI Opportunity Assessment ScorecardSection — The Tooling LandscapeThis is an automation opportunity — see the relevant playbook section for how to approach it.More in E-commerce & Digital
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