Every comparison on MyAwesomeBuy follows the same rigorous process. We don't pick favorites — we let the data decide. Here's exactly how we do it.
Our Process
Data Collection
For each product category, we collect data from multiple independent sources: Amazon verified purchase reviews, Reddit discussions from relevant subreddits, YouTube review transcripts, and professional review sites. We look for products that appear consistently across sources with strong buyer satisfaction.
Review Aggregation
We analyze hundreds to thousands of individual reviews per product, looking for patterns that a single reviewer would miss. When 300 Amazon buyers all mention the same issue with a product's handle design, that's a more reliable signal than one reviewer who didn't notice it. We weight verified purchase reviews more heavily than unverified ones.
Scoring
Each product receives a composite score based on: average star rating across retailers, total volume of reviews (more reviews = more confidence), consistency of positive feedback across different sources, price-to-value ratio compared to competitors, and frequency of specific complaints or praise in buyer feedback.
Ranking & Categorization
Products are ranked by their composite score and assigned badges: Best Overall (highest composite score), Best Value (best price-to-performance ratio), Premium Pick (best regardless of price), and Budget Pick (best under a specific price threshold). These categories help you find the right product for your specific needs and budget.
Verification & Updates
Before publishing, we verify that all products are currently available at the listed retailers and that pricing is accurate. We regularly revisit published comparisons to update pricing, check for discontinued products, and incorporate data from new reviews. Each page shows its last-updated date.
What We Don't Do
- We don't accept payment for rankings. No brand can pay to be ranked higher. Our revenue comes from affiliate commissions on sales, not from advertisers.
- We don't fabricate reviews. Every data point in our comparisons comes from real buyer feedback or verifiable product specifications.
- We don't hide bad products. If a popular product has serious issues, we report them. Our "Skip if" sections on product cards explicitly tell you when a product isn't right for certain use cases.
- We don't use a single source. Relying on Amazon reviews alone can be misleading (fake reviews, review manipulation). Cross-referencing with Reddit, YouTube, and professional reviewers catches products that game any single platform.
Our Data Sources
Amazon Verified Purchase Reviews
Our primary data source. We focus on verified purchase reviews, which Amazon marks to confirm the reviewer actually bought the product. We look at overall star ratings, review count (as a confidence metric), and the most common themes in both positive and negative reviews.
Reddit Discussions
Subreddits like r/BuyItForLife, r/Tools, r/Cooking, r/HomeImprovement, and category-specific communities provide unfiltered, real-world user experiences. Reddit users have no affiliate incentive and tend to be honest about product failures. We analyze these discussions to identify products with strong community endorsement or recurring complaints.
YouTube Reviews
Professional and enthusiast reviewers on YouTube provide in-depth testing, teardowns, and long-term use reports that written reviews rarely cover. We reference these for durability testing, real-world performance, and build quality assessments.
Professional Review Sites
Sites like RTINGS, Wirecutter, Consumer Reports, and category-specific review publications provide standardized testing methodologies. We reference their findings as an additional data point alongside crowd-sourced reviews.
A note on AI: We use AI tools to help aggregate and analyze large volumes of review data across sources. The AI helps us identify patterns across thousands of reviews faster than manual analysis would allow. However, all final rankings, product selections, and editorial decisions are made by our team. AI assists with data processing — it doesn't make our recommendations.
How Affiliate Links Work
MyAwesomeBuy earns money through affiliate links. When you click a product link on our site and make a purchase, we earn a small commission — typically 1-8% depending on the product category. This commission comes from the retailer, not from you — you pay the same price whether you use our link or go directly to the store.
Critically, our affiliate relationships do not influence our rankings. A product with a 4% commission rate will rank above a product with an 8% commission rate if the data shows it's a better product. Our reputation depends on accurate recommendations — sending you to a bad product would hurt our long-term business even if it earned a slightly higher commission.
For complete details, see our Affiliate Disclosure.
Limitations
We believe in being honest about what we can and can't do:
- We rely on review data, not hands-on testing. Unlike Wirecutter or RTINGS, we don't purchase and test every product in a lab. Our strength is aggregating the experiences of thousands of real buyers who have tested products in their own homes and workshops.
- Review data can be manipulated. Despite our multi-source approach, fake reviews exist across all platforms. We mitigate this by cross-referencing sources and flagging products where review patterns look suspicious (e.g., a sudden spike in 5-star reviews).
- Prices change constantly. We update pricing regularly but the price shown on our site may differ from the current price at the retailer. Always verify the final price before purchasing.
- Personal preferences vary. Our recommendations are based on aggregate data. Your specific needs, preferences, and use cases may differ from the average buyer. Use our "Best for" and "Skip if" sections to find products matched to your situation.
Questions About Our Methodology?
We welcome feedback on our process. If you think we've gotten a recommendation wrong, or if you have data that contradicts our findings, please contact us. We take corrections seriously and update our comparisons when presented with better data.