Innovative Workflow for Boosting Amazon Sports Brand Sales

I wanna share one very interesting workflow I've built for one of my clients. My client helps Amazon sports brands grow sales through category & product optimization, amazon SEO, etc you get the point. We have a super targeted list and this is a workflow I've built. The Challenge: We had 1,500 Amazon sports brands with varying performance data (pulled from RainforestAPI with http api) - some had great rankings but few reviews, others had lots of reviews but poor rankings, some had tons of product variants but weak images. Generic messaging wasn't going to work. The Solution - Hierarchical Conditional Logic: Step 1: Helper Columns Created classification columns for:

Rank_Category (TOP_500, MID_TIER, POOR_RANK, NO_RANK)
Review_Volume (HIGH_REVIEWS, MED_REVIEWS, LOW_REVIEWS)
Image_Quality (HIGH_IMAGES, MED_IMAGES, LOW_IMAGES)
Variant_Complexity (HIGH_VARIANTS, MED_VARIANTS, LOW_VARIANTS)

Step 2: Master Conditional Logic Single waterfall formula that prioritizes the strongest data point: clay

IF(
  [Specific category Rank] <= 500, "STRONG_RANK",
  [Image_Quality] = "LOW_IMAGES", "IMAGE_GAP",
  AND([Review_Volume] = "LOW_REVIEWS", [Rating] >= 4.0), "REVIEW_MOMENTUM",
  [Review_Volume] = "HIGH_REVIEWS", "REVIEW_LEVERAGE",
  "BASIC_OPTIMIZATION"
)

Step 3: Personalized Content Snippets Built separate formulas for each message component:

Opening_Hook (customized by optimization category + secondary data)
Problem_Statement (specific to their situation)
Case_Study (real client names matched to similar situations)
CTA (relevant to their opportunity)
Subject_Line (product-specific but natural)

Instead of trying to mention everything, I focus on one primary angle per lead (ranking, images, reviews) but enhanced it with secondary data points for deeper personalization and relevant case study 🙂

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