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A system and method for determining an attractiveness value of a product displayed on a website. The method includes receiving sales data on at least one product displayed in a zone included in the webpage, determining at least one key performing indicators (KPI) on each of the at least one product from the received sales data, comparing the KPI of the at least one product displayed in the zone, determining the attractiveness value for each of the at least one product displayed in the zone, and an insight based on the comparison and the determined attractiveness value, and displaying an image of the at least one product, the KPI, and the insight on the display.

A computing system generates, for each of a plurality of sessions, a session representation indicating the order through which a user navigated through a plurality of webpages during the session, and generates for each pair of session representations, a score indicating how similar the session representations in each pair are to each other. The computing system further generates a plurality of clusters by clustering the session representations based on the score for each pair of session representations, determines an optimal number of clusters based on key performance indicators corresponding to each cluster of the plurality of clusters, and generates a natural language description for each cluster of the optimal number of clusters based on the key performance indicators associated with each cluster.

A computing system generates, for each of a plurality of sessions, a session representation indicating the order through which a user navigated through a plurality of webpages during the session, and generates for each pair of session representations, a score indicating how similar the session representations in each pair are to each other. The computing system further generates a plurality of clusters by clustering the session representations based on the score for each pair of session representations, determines an optimal number of clusters based on key performance indicators corresponding to each cluster of the plurality of clusters, and generates a natural language description for each cluster of the optimal number of clusters based on the key performance indicators associated with each cluster.

Systems and techniques may be used for providing information related to an underperforming product. An example technique may include determining, for products in a particular zone of a website, an underperforming product based on pageviews or interaction metrics of users visiting the website. The technique may include identifying a product type of the underperforming product, and outputting a recommendation for display. The recommendation may include at least one replacement product of the product type, other than the underperforming product, to replace the underperforming product in the particular zone.

The subject technology receives session data from a set of users associated with a retail website. The subject technology generates, using at least one machine learning model, a purchase intent prediction based at least in part on the session data. The subject technology determines a set of sessions that correspond to conversions on the retail website based on the session data. The subject technology generates a combination of data using first data from the purchase intent prediction with second data based on the set of sessions that correspond to conversions. The subject technology generates information related to a missed purchase segment based at least in part on the combination of data. The subject technology provides the information for display on a client device.

Systems and techniques may be used for providing information related to an underperforming product. An example technique may include determining, for products in a particular zone of a website, an underperforming product based on pageviews or interaction metrics of users visiting the website. The technique may include identifying a product type of the underperforming product, and outputting a recommendation for display. The recommendation may include at least one replacement product of the product type, other than the underperforming product, to replace the underperforming product in the particular zone.

The subject technology receives session data from a set of users associated with a retail website. The subject technology generates, using at least one machine learning model, a purchase intent prediction based at least in part on the session data. The subject technology determines a set of sessions that correspond to conversions on the retail website based on the session data. The subject technology generates a combination of data using first data from the purchase intent prediction with second data based on the set of sessions that correspond to conversions. The subject technology generates information related to a missed purchase segment based at least in part on the combination of data. The subject technology provides the information for display on a client device.

A system and method for determining an attractiveness value of a product displayed on a website. The method includes receiving sales data on at least one product displayed in a zone included in the webpage, determining at least one key performing indicators (KPI) on each of the at least one product from the received sales data, comparing the KPI of the at least one product displayed in the zone, determining the attractiveness value for each of the at least one product displayed in the zone, and an insight based on the comparison and the determined attractiveness value, and displaying an image of the at least one product, the KPI, and the insight on the display.

A system and method for identifying cross-selling effectiveness. The method includes receiving transaction data on a plurality of products, identifying a product from the plurality of products that are sold together with another product from the plurality of products in a same transaction across a plurality of transactions, determining a Key Performance Indicator (KPI) of the identified product, based on the received transaction data, comparing the KPI of the identified product with the KPI of the other product from the plurality of products, ranking the identified product based on the comparing of the KPIs, determining an insight based on the comparing of the KPIs and the ranking, and making a recommendation based on the insight.