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ECommerceReviewer Review & VOC analysis

Turn product reviews into evidence-backed fixes.

ECommerceReviewer reads the reviews, ratings, follow-ups, and support feedback you supply, groups them into VOC themes with counts and quotes, separates root causes from hypotheses, and ranks product, page, support, and creative actions.

Deliverables VOC themes Root causes Action priorities
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ECommerceReviewerExample
What it can finish

Three review-analysis tasks with traceable evidence.

Use it when you need to know why customers complain, how often, and what to fix first.

Review exports → a VOC theme map

Deduplicate exports, keep SKU and period groups apart, and count each theme against a stated denominator.

Negative reviews → a root-cause sheet

Separate product defects, expectation gaps, listing confusion, logistics, service, and misuse, each with a confidence level.

VOC findings → ranked actions

Rank product, page, support, and creative actions by severity, frequency, and confidence, and name the evidence still needed.

How it works

From one request to a reviewable delivery.

01

Share the feedback

Upload review, rating, or support exports and state the decision the analysis should support.

02

Check the corpus

Sources, period, counts, duplicates, and missing fields come first, so you know what the themes cover.

03

Let the analysis run

Themes, pain clusters, severity, and root-cause hypotheses are built with quotes attached.

04

Act on the priorities

Review the ranked actions, the evidence behind each, and which findings still need validation.

Start your first task with ECommerceReviewer.

Install Orkas, choose a model, then add the task and source material.

Download Orkas
1

Install

Download the build for your computer and complete the normal system prompts.

2

Choose a model

Use an available official model or connect a supported provider.

3

Add the review data

Attach review or support-feedback files and describe the question the analysis should answer.

FAQ

Questions before you start.

What feedback can I upload?

Review, rating, follow-up, or support-ticket exports in CSV, TSV, Excel, JSON, TXT, Markdown, or PDF. Public social posts can be added as a supplementary sample.

Does it scrape reviews from marketplaces?

No. It analyzes the exports you supply and ships no review scraper. Findings stay within that corpus, and a small or negative-only sample is not presented as overall prevalence.

Will it rewrite my product page?

No. It turns validated findings into prioritized product, page, support, and creative suggestions; full listing or creative production is a separate task.

Give the next batch of reviews to ECommerceReviewer.

Share the feedback, check the corpus summary, and get VOC priorities you can trace back to quotes.

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