A tutorial published July 16, 2026 by the YouTube channel Amazon Ads lays out a specific method for Amazon sellers managing multiple product variants to determine which ASINs merit additional advertising spend and which are quietly losing money.

The video, titled How to identify best & worst performing ASINs in your ad campaigns, features Siddharth Rao, founder of DigiBegin Consulting, walking through a framework built on two metrics that sellers can pull directly from Amazon's advertising dashboard and business reports. Neither metric is new to Amazon sellers. What the video offers instead is a structured way of reading them together, and a reminder that the advertising dashboard alone does not show the whole picture of a catalog's performance.

Rao frames the problem through a hypothetical seller who has launched four core products, each with 10 design variants, for a total of 40 SKUs. That seller has been running ad campaigns on roughly half of those SKUs for about two months. Some variants convert well. Others burn through budget without returning sales. Many sit in an ambiguous middle, neither clearly working nor clearly failing. This scenario, according to the video, describes where many newer sellers get stuck once they have moved past the initial product launch phase and need to decide where to concentrate limited advertising budget.

Two metrics anchor the framework

The video identifies impressions, clicks, and orders as the three raw data points the framework tracks for every ASIN and variant. From these three numbers, two calculated metrics emerge.

The first is click-through rate, or CTR, defined in the video as clicks divided by impressions, multiplied by 100. According to the video, CTR indicates how many people who saw an advertisement actually clicked on it, which in turn signals whether a product's main image and listed price are attracting attention within the search results or advertising placement where the ad appeared.

The second metric, described in the video as more consequential than CTR, is conversion rate, or CVR. Rao defines this as orders divided by clicks, multiplied by 100. This tells sellers how many of the people who clicked through to a product page went on to complete a purchase. A high CVR, according to the video, means the listing itself does its job once a shopper lands on it. That distinction between attracting a click and closing a sale forms the backbone of how the framework diagnoses problems later in the process.

Where the data lives

According to the video, this performance data can be found in two separate locations within Amazon's seller tools, and each location tells a different part of the story.

The first is the sponsored ads dashboard. Clicking on product tabs within that dashboard breaks down impressions, clicks, and orders by variant, with CTR and CVR percentages calculated and displayed automatically. No manual calculation is required at this stage.

The second location is the business reports section, specifically what the video refers to as the ASIN-wise report, also known as the detail page sales and traffic report. This report shows sessions, meaning the number of users who clicked through to a given product page, alongside the orders those sessions generated. The key figure here is what the video calls the unit session percentage, which functions as a conversion rate calculated from total site traffic rather than solely from advertising clicks.

The distinction between these two data sources matters because of what each one includes. The sponsored ads dashboard only displays data for ASINs that have actually been advertised. Business reports, by contrast, cover every ASIN in a seller's catalog, including variants that have never received a single advertising dollar. In the hypothetical 40-SKU catalog described in the video, this means a seller could discover that one of the 20 unadvertised variants is converting well through organic traffic alone. The video characterizes this as a hidden winner, a product performing on merit that has not yet received any paid support.

A four-step decision process

The video outlines a sequence for turning this raw data into a spending decision.

First, sellers are told to check the ads dashboard and review CVR for every variant currently receiving advertising spend. Variants with the highest conversion rates are identified as top performers, and the video suggests directing more advertising budget toward them.

Second, sellers move to the business reports and review the ASIN-wise data across all variants, advertised and unadvertised alike, over a comparable time window, in this case the same two-month period used in the hypothetical example. Checking the unit session percentage across this fuller dataset can reveal discrepancies between advertised and unadvertised variants within the same product line. The video's own example illustrates this: of 10 copper bottle designs, a seller might have advertised five, only to find that one of the remaining five unadvertised designs is converting better than any of the advertised ones.

Third, the video separates underperforming ASINs into two distinct diagnostic categories, each pointing toward a different kind of problem.

The first category covers ASINs that receive impressions and clicks but fail to convert into orders. According to the video, this pattern points to a listing problem. Shoppers see the advertisement, find it compelling enough to click, and then leave the product page without buying. Rao attributes this gap to factors on the product detail page itself: unconvincing images, pricing that appears uncompetitive against similar listings, or bullet points that fail to answer the questions a shopper is likely to have before purchasing.

The second category covers ASINs that generate few impressions in the first place. The video attributes this pattern to bids set too low or targeting configured too narrowly, meaning the advertisement rarely enters the auction for relevant search terms or placements.

Fourth, once this diagnostic work is complete, the video describes selecting the top two or three converting variants within each product line for further investment. Investment, in this context, extends beyond advertising budget. The video specifically mentions ordering additional inventory of the selected variants and distributing that stock across more fulfillment centers, a step intended to improve delivery speed and product availability once demand has been identified.

Deciding between product lines

The video closes by returning to the four-product hypothetical to address a broader allocation question: given four product categories (copper bottles, copper glasses, stainless steel bottles, and steel glasses), which one warrants the heaviest overall investment.

The video proposes answering this by stepping back from individual ASINs and evaluating each product category as a whole against three questions: which category contains the highest number of converting variants, which delivers the best return on advertising spend, and which shows the most room remaining to grow. According to the video, the category that performs best against all three questions is the one that should receive concentrated focus.

Why the distinction between CTR and CVR problems matters

The framework presented in the video rests on a distinction that recurs throughout advertising diagnostics on Amazon's platform: a low CTR and a low CVR point to different fixes, even though both can look identical on a surface-level dashboard showing disappointing sales. An ASIN with strong CTR but weak CVR is attracting attention that its product page then fails to convert, a problem rooted in content, pricing, or competitive positioning rather than in the advertisement's targeting or bid strategy. An ASIN with weak CTR, by contrast, may have a perfectly capable product page that few shoppers ever reach, a problem rooted in auction visibility rather than page content.

Amazon has continued expanding the reporting infrastructure that sellers rely on to make exactly these kinds of comparisons. A unified reporting experience announced in open beta in November 2025 consolidated data across sponsored ads and Amazon DSP into a single interface, extending historical data access up to six years for monthly reporting and 15 months for daily reporting. That expanded historical window matters for sellers running the kind of two-month comparison described in the video, since longer look-back periods make it easier to separate a genuinely converting ASIN from one that happened to have a good week.

The company has also built out attribution tools addressing a related but distinct measurement challenge. Conversion path reporting, which tracks the sequence of advertising touchpoints a shopper encounters in the 30 days before a purchase, addresses cases where a sale involves more than one ad format or exposure. The ASIN-level framework described in Rao's video operates at a narrower, single-touchpoint level, comparing clicks and orders for a specific ad rather than reconstructing a full customer journey, but the two approaches address complementary questions: one asks which ASIN converts, the other asks which sequence of exposures led to that conversion.

Amazon's own product changes elsewhere on the platform have leaned on the CTR metric that anchors this framework. When Amazon expanded benchmarks reporting to 18 marketplaces in a rollout completed in May 2026, click-through rate was one of eight metrics made available for comparison against category peers, alongside cost per click and metrics specific to new-to-brand customer acquisition. That benchmarking tool lets sellers compare their own CTR and CPC figures against a category average rather than only against their own historical performance, adding external context to the kind of variant-by-variant comparison Rao's video describes internally within a single catalog.

The broader trend of ASIN-level data becoming more central to Amazon's advertising tools also appears in changes to ad formats themselves. Amazon's shift toward product collections requires advertisers to feature a minimum of three ASINs within a single ad unit, with Amazon selecting the most effective combinations of ad variations and products automatically. That format change places more weight on having accurate, ASIN-level performance data of the kind Rao's framework is designed to produce, since an automated system optimizing across multiple ASINs within one ad depends on the underlying performance signal for each product being sound.

For sellers managing catalogs at the scale described in the video, roughly 40 SKUs across four product lines, the gap between what an advertising dashboard shows and what a full business report shows can represent a meaningful blind spot. A seller monitoring only advertised ASINs has no visibility into organic performance among the unadvertised half of the catalog, and the video's central claim, that a hidden winner can exist among products never advertised at all, depends entirely on checking a data source most sellers do not review as routinely as the advertising dashboard itself.

Timeline

  • July 16, 2026: Amazon Ads publishes the video "How to identify best & worst performing ASINs in your ad campaigns," featuring Siddharth Rao of DigiBegin Consulting

Summary

Who: Siddharth Rao, founder of DigiBegin Consulting, presenting through the Amazon Ads YouTube channel to an audience of Amazon sellers managing multiple product variants.

What: A diagnostic framework using click-through rate and conversion rate, drawn from Amazon's sponsored ads dashboard and business reports, to identify which ASINs deserve additional advertising investment and which show either a listing problem or a visibility problem.

When: The video was published July 16, 2026.

Where: The framework applies to Amazon's Sponsored Products advertising environment and the business reports section of Amazon Seller Central, without reference to a specific country marketplace.

Why: Sellers managing catalogs of dozens of ASINs across multiple product variants often lack a structured method for allocating limited advertising budget, and the sponsored ads dashboard alone does not surface organic performance among ASINs that have never been advertised.