How Amazon Marketplace combines AI models to evaluate A+ Content

Introduction to A+ Content on Amazon Marketplace

Amazon Marketplace’s approach to evaluating A+ Content combines a vision-language model, optical character recognition and a classifier trained on expert judgments. In Amazon’s reported evaluation, this combination identified high- and low-quality content more accurately than either model’s signals alone. Its central idea is to assess how an entire page works together, then supplement that judgment with measurements of text readability.

A+ Content is the rich media widget on an Amazon product detail page. Brands use its visual assets to explain product features and brand value, helping customers make purchase decisions. A typical project combines text, images and video into a product story. Amazon describes considerable creative freedom, without strict specifications such as image ratios or text word counts.

That flexibility also creates a difficult evaluation problem. Established brands with internal design teams and smaller brands with limited resources both use A+ Content, producing substantial variation in composition and quality. A system must account for different layouts and product categories while giving sellers useful guidance about what to change.

The approach described in Amazon’s technical account renders the full page as one snapshot. A vision-language model, or VLM, assesses its composition and meaning. Optical character recognition, or OCR, supplies measurements of text and spacing. A CatBoost classifier combines those signals to predict an expert-derived quality label, while explanation tools help turn the prediction into recommendations.

The evidence supports this as a content evaluation and improvement workflow. It does not establish that the reported classification gains caused a corresponding sales increase. Keeping that distinction clear is essential to understanding both the system’s technical contribution and its commercial relevance.

Impact of high-quality content on sales

Amazon reports that selling partners using high-quality A+ Content increase sales by 8–20 percent compared with those without it. That is the commercial motivation for improving content quality: richer product information can help customers understand what they are buying and why its features matter.

The figure is an Amazon claim, and the supplied account does not explain the sales comparison’s methodology. It does not provide the sample size, measurement period or controls needed to isolate the contribution of content quality. Sellers should therefore read the range as a reported comparison, rather than a guaranteed return from adopting the evaluation pipeline.

There are also two distinct outcomes in the account. One is the relationship Amazon reports between high-quality A+ Content and sales. The other is the evaluator’s ability to reproduce expert quality judgments. A classifier’s accuracy measures the latter; it does not measure incremental revenue. Likewise, the rubric dimension named Conversion Optimization is a content assessment criterion, not a reported conversion-rate experiment.

Amazon provides a more direct result for the improvement workflow. After selling partners revised their content using explanation-based recommendations, the average quality score rose by 1.4 points on a 10-point scale. The account describes the sample as statistically significant, but does not supply its size, a control comparison or a detailed statistical procedure in the available evidence.

That result supports a narrower conclusion than the brief’s broad sales thesis: Amazon reports that recommendations were followed by better content scores. It does not quantify sales attributable to those revisions. For sellers, the practical value described here is guidance that makes content more coherent, readable and relevant; the size of any resulting revenue benefit remains unestablished by this evaluation.

AI’s role in content evaluation and improvement

The VLM provides the first layer of judgment. Amazon explains that these models pair an image encoder with a language model, allowing an image and a written evaluation rubric to produce an assessment. Here, the input is a complete page snapshot rather than an isolated product image.

The rubric covers four dimensions: Visual Storytelling, Feature Presentation, A+ Layout and Conversion Optimization. For each, the VLM returns a score from 0 to 10, a confidence value and a natural-language rationale. It can lower scores for issues such as repeated information, insufficient module diversity or missing lifestyle imagery, and explain its reasoning.

A pretrained model’s judgments do not necessarily match ecommerce reviewers’ standards. Amazon addresses that gap by keeping the VLM and rubric fixed and training a CatBoost classifier on the resulting features and expert labels. CatBoost is a gradient-boosted decision tree library; its role is to learn how the evaluation signals relate to the human quality decisions.

This design separates generating an assessment from calibrating the final decision. Amazon says retraining only the classifier allows updates through a short CPU-only job as annotations accumulate or criteria change. The alternative discussed in the account, fine-tuning the VLM, would require GPU training infrastructure and risk overfitting with only a few thousand labeled examples.

The labels came from one ecommerce marketing expert and four professional annotators. They independently rated 5,478 product detail pages across 10 categories as high or low quality using the same criteria. Majority votes produced the consensus labels, with 49.6 percent of pages labeled high quality. Categories included Furniture, Shoes, Grocery, Beauty and Apparel.

Amazon reports Cohen’s kappa scores of 0.845–0.915 for individual annotators against the consensus. Kappa measures agreement after accounting for chance. These figures help establish consistency in the labeling exercise, although the consensus remains the evaluation standard rather than a direct measurement of purchasing behavior.

To explain decisions, the system uses SHapley Additive exPlanations, or SHAP. Each feature receives a contribution indicating how it pushed a prediction toward high or low quality. This puts different signals on a common decision scale: a visual storytelling score and a character-density measurement otherwise cannot be compared directly.

For a page classified as low quality, the system ranks negative feature contributions and passes the strongest ones, the product category, ecommerce best practices and the VLM’s rationales to a language model. The model turns that information into prioritized actions. In Amazon’s example, a layout contribution of -1.2 and a visual storytelling contribution of -0.3 lead the system to prioritize layout restructuring and text-image balance before imagery changes.

These explanations identify what influenced the classifier. They help organize improvement work, but do not by themselves prove that changing a particular feature will cause a sales gain.

Holistic evaluation vs. individual asset assessment

The reason for evaluating a full snapshot is that individually acceptable assets can create a confusing page when combined. Sharp images and grammatical text do not guarantee that the product story is consistent or that the layout directs attention effectively.

Amazon illustrates this with a blender page containing a hero image, feature bullets and a lifestyle photograph. Each asset could pass its own quality check. Yet the bullets might describe a 64-oz pitcher while the lifestyle image shows a single-serve model. Alternatively, the lifestyle photograph might appear above the hero image, putting the kitchen setting ahead of the product itself.

This is an explanatory example in Amazon’s account, rather than a documented customer incident. It shows what a holistic evaluator is intended to detect: relationships among content elements that isolated checks miss. The meaning of an asset depends partly on its position and surrounding information.

The snapshot supplies that shared context. It lets the VLM evaluate the page’s overall visual composition against the rubric. Amazon argues that grading assets separately adds little beyond conventional checks for problems such as blur or grammatical errors. The supplied benchmark, however, compares scoring and feature configurations; it does not quantify a separate full-page-versus-individual-asset experiment. The rationale for holistic assessment and the measured gains from model fusion should therefore be kept distinct.

A whole-page view also has limits. Amazon says VLMs are less reliable at fine-grained readability issues, including crowded text and line spacing. Their language-model foundation can favor long, detailed descriptions even when customers would benefit from concise information they can scan.

OCR addresses this gap by extracting text blocks and their positions. The system calculates Text Density, the share of page area occupied by text; Character Density, characters per unit of page area; Spacing, the consistency and sufficiency of whitespace; and Overall Readability, a combined signal from the preceding measurements. Because these depend on layout, identical wording can receive different readability scores on different pages.

The resulting division of work is specific: the VLM assesses meaning and composition, while OCR contributes spatial text measurements. The classifier learns how those complementary signals relate to expert labels.

Performance of combined AI models in content evaluation

Amazon evaluated four configurations on the same 20 percent holdout set, approximately 1,100 pages, drawn from the 5,478-page dataset. The baseline averaged the four VLM rubric scores and applied a fixed threshold, giving each dimension equal weight. The other configurations trained CatBoost classifiers on OCR features, VLM features or their combination.

Accuracy measures agreement with the reference labels overall. The account specifies the high-quality class when describing the baseline and VLM-only F1 results. These are Amazon’s reported results against its expert consensus labels, rather than an independent benchmark of commercial performance.

The comparison separates two improvements. First, learning from the VLM scores increased accuracy from 0.80 to 0.89 and F1 from 0.84 to 0.90. Amazon attributes that gain to the classifier learning the importance and interactions of quality dimensions instead of treating them equally.

Second, adding OCR measurements raised accuracy from 0.89 to 0.94 and F1 from 0.90 to 0.95. The final fusion model uses eight features: four VLM rubric scores and four OCR scores. Its advantage over the VLM-only classifier supports the value of readability measurements within this dataset. Against the fixed-threshold baseline, the total improvements were 0.14 in accuracy and 0.11 in F1.

SHAP analysis offers another view of that complementarity. Across the holdout set, the leading features by mean absolute SHAP value were OCR Character Density at 1.1, OCR Overall Readability at 1.0 and VLM Visual Storytelling at 1.0. These values describe influence on predictions, not sales effects or percentage improvements. Both types of input contributed strongly to the classifier’s decisions.

Amazon also reports a separate experiment using a lighter VLM on 499 content projects from one category. The baseline achieved 0.74 accuracy and 0.72 F1, while the fused model reached 0.90 for both. That smaller, single-category experiment has a different scope and should not be pooled with the main holdout comparison. The supplied source ends during discussion of another VLM variant, so it does not support a complete comparison of backbones.

The clearest supported finding is that calibrated VLM judgments and OCR readability signals worked better together in Amazon’s evaluation. The recommendation workflow extends that finding into content revision, with a reported improvement in quality scores. Whether those gains produce a particular sales uplift remains a separate question that the available evidence does not answer.