Long tail patterns in online reviews for search and experienced goods: Evidence from Amazon

Online reviews have become one of the most visible forms of electronic word-of-mouth in e-commerce. Beyond informing potential buyers, the number of reviews a product receives can also indicate how consumer attention is distributed across products on a platform. This study examines such review-distribution patterns on Amazon. in, with a particular focus on the long-tail phenomenon. Using review data from 267 product categories, we classify products as mainstream or niche through the Pareto-based 80/20 principle and as search or experience goods according to established product evaluation criteria. We then fit power-law distributions, following and comparing the results with alternative distributions, including lognormal and exponential models. The findings suggest that the power-law model is not supported for experience goods, where lognormal distributions provide a better fit, showing weaker evidence, which reflects the more subjective evaluation of the products by the consumers. For search goods, the power-law model cannot be rejected, although the lognormal model provides a comparable fit. The findings, therefore, suggest that review activity for search goods is more plausibly consistent with long-tailed or heavy-tailed patterns than review activity for experience goods. These results are interpreted cautiously, as a non-rejected power-law fit does not establish that the power law is the only or best-fitting distribution. The study treats review volume as a proxy for consumer attention and product visibility, rather than as a direct measure of purchase decisions, sales, or revenue. This study contributes to research on online reviews, electronic word-of-mouth, and long-tail dynamics by showing how the basis on which products are evaluated is linked to the concentration of review attention in digital markets. The findings also suggest cautious implications for e-commerce platforms and sellers, especially in improving the visibility of lesser-known search products and encouraging more informative reviews for experience goods.