Real Amazon Problems. Practical Fixes.

How ASINLYTICS approaches the issues that quietly cost brands the most. These are illustrative walkthroughs of our method — structured, evidence-led and built to prevent recurrence.

Note: These case studies describe our approach and method. To respect client confidentiality and avoid unrealistic promises, they use professional placeholder narratives rather than invented figures.
Vendor Central

Reducing Amazon Vendor Central Chargeback Risk

  • Problem

    A brand was receiving repeat compliance chargebacks across multiple purchase orders, steadily eroding net invoice value each cycle.

  • Root cause

    Routing, ASN and labelling gaps between the brand’s warehouse process and Amazon’s specific requirements were triggering the same deduction types repeatedly.

  • Action taken

    We categorised every deduction by type and value, assembled the evidence each dispute needed, prepared the cases in Amazon’s expected structure, and documented a prevention SOP for the warehouse team.

  • Result

    The most recoverable disputes were prepared and submitted first, and the operational SOP targeted the defects causing the highest-frequency deductions.

  • Learning

    Most chargeback loss is operational, not accidental — fixing the root process matters more than disputing each deduction in isolation.

How we approached it

1

Review the evidence

Gather the data and quantify the real business impact.

2

Isolate the root cause

Separate symptom from cause before acting.

3

Fix & prevent

Resolve it and document a process so it does not repeat.

Catalogue & ASIN

Fixing Catalogue and ASIN Data Errors

  • Problem

    Several ASINs were suppressed or duplicated, limiting discoverability and splitting sales and reviews across competing detail pages.

  • Root cause

    An EAN/SKU mismatch and a broken parent-child variation family were feeding Amazon inconsistent product data.

  • Action taken

    We rebuilt the correct data mapping, prepared the correction cases, consolidated duplicates and restored the variation family to a single, accurate structure.

  • Result

    The catalogue was returned to an accurate, consistent structure with the correct family relationships and reduced duplication.

  • Learning

    Clean catalogue data is the foundation — ranking, content and advertising all rest on getting the product record right first.

How we approached it

1

Review the evidence

Gather the data and quantify the real business impact.

2

Isolate the root cause

Separate symptom from cause before acting.

3

Fix & prevent

Resolve it and document a process so it does not repeat.

Content

Improving Amazon Listing Content for Better Conversion

  • Problem

    Strong products were seeing flat detail-page performance despite steady traffic.

  • Root cause

    Weak titles, thin bullet points and A+ modules that failed to answer common buyer questions and objections.

  • Action taken

    We mapped keywords and buyer questions, rewrote titles and bullets around search intent, restructured A+ Content to address objections, and produced a clear image brief.

  • Result

    The detail pages were rebuilt around what buyers actually search and worry about, with content structured to support the buying decision.

  • Learning

    Traffic is wasted if the page cannot answer the buyer’s real questions — content should be built around intent and objections, not features alone.

How we approached it

1

Review the evidence

Gather the data and quantify the real business impact.

2

Isolate the root cause

Separate symptom from cause before acting.

3

Fix & prevent

Resolve it and document a process so it does not repeat.

Have a Similar Amazon Problem?

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