Find out why Amazon shoppers are not buying your product

It reads your Amazon reviews, finds the complaints costing you sales, and ranks them by revenue impact in a sheet.

How the work actually flows

It branches. Every path runs; runs once per each review.

Pattern: Sequence (1) · Parallel Split (2) · Multiple Instances without Synchronization (12)

flowchart TD trig(("request to analyze reviews")):::human s0["pull reviews from product listing"]:::svc s1[["AI scans each review for issues"]]:::mi s2["AI scores issues by revenue impact"]:::task trig --> s0 s0 -->|"one per each review"| s1 s1 --> s2 gx{"+ which report"}:::gate s2 --> gx p00["log to checkout report"]:::task gx -->|"checkout issues"| p00 p10["log to delivery report"]:::task gx -->|"delivery issues"| p10 p00 --> out p10 --> out out[/"two prioritized issue reports"/]:::out pay{{"know which complaints cost the most sales"}}:::pay out --> pay classDef task fill:#e7f6fe,stroke:#34b8f0,color:#2c2a29 classDef svc fill:#f6f8fa,stroke:#7c8795,color:#2c2a29 classDef mi fill:#e7f6fe,stroke:#0079a8,color:#2c2a29,stroke-width:2px classDef human fill:#fff,stroke:#0079a8,color:#0079a8 classDef store fill:#f6f8fa,stroke:#0079a8,color:#2c2a29 classDef trig fill:#00a4eb,stroke:#0079a8,color:#fff,font-weight:bold classDef trigtime fill:#00a4eb,stroke:#0079a8,color:#fff,font-weight:bold classDef trigdata fill:#8ad4f5,stroke:#0079a8,color:#06314c,font-weight:bold classDef gate fill:#fff,stroke:#e8a23d,color:#6b4708,font-weight:bold classDef out fill:#1f9d6b,stroke:#167a53,color:#fff,font-weight:bold classDef pay fill:#06314c,stroke:#021f33,color:#fff
A stepAn outside serviceRuns once per itemA personEvery pathResultPayoff
Build size
Advanced

A larger build with multiple systems, AI reasoning, and custom rules.

Business functions
AI Agents & Autonomous SystemsSpreadsheet & Database Ops
Connects
Bright DataOpenRouterGoogle Sheets

The problem it solves

You know customers are complaining in your Amazon reviews, but reading through hundreds of them to spot patterns is not realistic. Meanwhile, the same delivery, sizing, or defect issues keep costing you sales. You need to know which complaints matter most, not just that they exist.

Who it fits

Ecommerce managers or product teams selling on Amazon who want to fix the issues actually hurting sales.

How it works

  1. Bright Data collects reviews from your Amazon product listing
  2. AI scans each review for friction signals like delivery problems, sizing issues, or defects
  3. AI scores each issue by how much it likely costs you in lost sales
  4. The results are split into a checkout optimization list and a delivery and returns report, both logged to Google Sheets
What you get

Sales-losing complaints ranked by revenue impact

You get your Amazon reviews broken down into the specific complaints costing you sales, ranked by how much they matter.

What you get

Two prioritized reports in Google Sheets: one for checkout fixes, one for delivery and returns risks.

What you need

A Bright Data account, an OpenRouter API key, and a Google account for Sheets.

We can build this. But should you?

The hard question is not how to build it. It is whether this is the right thing to build first.

That is what a Fractional Chief AI Officer figures out with you, before anyone writes a line of code.

Let's Talk Strategy

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