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Actowiz Solutions helped a global brand use consumer sentiment scraping from Amazon & Walmart reviews to predict product returns and improve design decisions.
With tens of thousands of monthly online orders, the client faced two major challenges:
1. High return rates for certain products—ranging from 15% to 35%—but lacked insight into why.
2. Limited access to structured consumer feedback beyond their own post-purchase surveys, which had a <5% response rate.
Returns were cutting into profit margins, bloating reverse logistics costs, and impacting customer satisfaction.
The client needed to proactively detect products likely to be returned, and adapt product features, size guides, and inventory strategies accordingly.
Actowiz Solutions proposed a consumer sentiment analytics solution, driven by real-time review scraping from Amazon and Walmart—two platforms where customers freely express detailed opinions on product performance, fit, material, and durability.
The solution involved 4 key phases:
Actowiz crawled reviews for over 2,000 SKUs across apparel, footwear, and accessories. Each review pulled included:
| SKU | Platform | Star | Review Text | Location | Date |
|---|---|---|---|---|---|
| M-TRK-BLK-L | Amazon | 2 | Too tight around the thighs. Returned it. | Houston, TX | 2024-08-12 |
| W-JKT-RD-M | Walmart | 5 | Color is vibrant. Fits well even after wash. | Atlanta, GA | 2024-08-14 |
| K-SND-YW-10C | Amazon | 3 | Strap broke in a week. Got a refund. | Phoenix, AZ | 2024-08-16 |
Using custom-trained Natural Language Processing (NLP) models, Actowiz tagged reviews into:
| Aspect | Positive (%) | Neutral (%) | Negative (%) |
|---|---|---|---|
| Fit | 54% | 22% | 24% |
| Fabric | 66% | 18% | 16% |
| Zipper | 39% | 40% | 21% |
| Return Flag | — | — | 13% (explicit mentions of “return”) |
This tagging enabled early identification of SKUs showing return-related dissatisfaction even before internal return reports flagged issues.
Actowiz built a machine learning model trained on:
The model categorized SKUs as:
| SKU | Avg Star | Return Flags | Sentiment Score | Risk Level |
|---|---|---|---|---|
| M-TRK-BLK-L | 2.8 | 15% | -0.45 | High Risk |
| W-JKT-RD-M | 4.6 | 0.5% | +0.81 | Low Risk |
| K-SND-YW-10C | 3.2 | 11% | -0.33 | Moderate Risk |
The client was given access to a custom BI dashboard with:
The client proactively paused promotion of 36 SKUs within 2 weeks of launch due to early return risk signals from Amazon reviews—saving an estimated $210K in reverse logistics costs.
Feedback around tight fits and poor zipper quality led to:
By identifying language in 5-star reviews, the client revised product bullet points to echo customer-preferred phrases like “lightweight but cozy” and “stretchy waistband”—improving conversion rates by 12%.
| State | SKU | Complaint Type | Suggested Action |
|---|---|---|---|
| Texas | M-TRK-BLK-L | “Too Tight” | Add Size Guide Pop-Up |
| Georgia | K-SND-YW-10C | “Weak Strap” | Reinforce Strap or Remove |
| California | W-JKT-RD-M | “Color mismatch” | Standardize color photos |
“Actowiz didn’t just give us review data. They gave us return prevention power. We now flag high-risk products within a week of launch—and act on them fast.”
— VP, Customer Insights, Global Apparel Brand
Actowiz is enhancing its return prediction solution with:
| Metric | Before Actowiz | After Actowiz (6 Months) |
|---|---|---|
| Average Return Rate | 21.3% | 14.1% |
| Return Cost Savings | — | $340,000+ |
| SKU Risk Classification Accuracy | — | 87% |
| Design Feedback Cycle | 3 months | 2 weeks |
| Marketing Copy CTR Uplift | — | +12% |
Returns aren’t just a logistics issue—they're a customer sentiment signal. With Actowiz Solutions, global retailers can proactively analyze, predict, and reduce returns by listening to the voice of the customer where it's loudest: in online reviews.