Product search engines allow searchers to find products offered by online merchants.<\/p>\n
And while product search engines have a specific mission, the challenges they face are similar to those faced by web search engines.<\/p>\n
For instance, those who design product engines must return the best set of results they possibly can for a search query, ordered by relevance.<\/p>\n
The quality of a search engine is evaluated by using HRS (Human Relevance System) and user-engagement metrics like click-through rate, last-click rate, first-click rate, abandonment rate etc.<\/p>\n
The same metrics are used by other vertical engines, like job search engines, image search engines, local search engines etc.<\/p>\n
From a consumer standpoint, the important metrics around product search engines are similar to the important metrics for most vertical search engines and web search engines:<\/p>\n
HRS based metrics<\/a> like NDCG, DCG, Win-Loss Ratio<\/p>\n<\/li>\n Click-through rate (clicks\/visit), conversion rate (views\/visit)<\/p>\n<\/li>\n<\/ul>\n From a merchant perspective, we consider different metrics \u2014 like revenue per visit (RPV), revenue per search session (RPS), margin per visit (MPV) and also search participation rate (searches\/visit).<\/p>\n The more revenue the user brings in and the more engaged the user is, the better for the merchant.<\/p>\n As we know most search engines use multiple signals, which they eventually combine with a machine-learning\/hand-tuned model.<\/p>\n While the models are important, the set of features the models use to rank results is equally as important.<\/p>\n\nFeatures That Are Unique to an Ecommerce Product Search Engine<\/h3>\n
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