Product analytics in the regions
Posted: Sun Feb 02, 2025 7:13 am
Case 2.
Client. Steel door factory.
Problem: We didn't know in which regions certain types of doors and colors were in demand, where the demand and the size of demand were higher.
Our actions. We implemented a filtering system on venezuela consumer email list the site and set up a report on it. Based on the information from the report, we developed an automatic module for holding promotions on the site.
194_2.pngFilters in the catalog, with the help of which we identified the most popular product parameters
194_2.jpg
Unloading data on consumer preferences. At the top is the distribution of preferences for certain product models by gender and age of the buyer. At the bottom are functional and aesthetic needs not related to specific models
Before implementing end-to-end analytics, the client's website was visited by 26.4 thousand people per month. With an average check of 25 thousand rubles, they left orders for 19.8 million rubles. Accordingly, the conversion was 3%. We implemented end-to-end analytics, and after making decisions based on data, the conversion increased to 4%, respectively, a third more orders began to come through the website.
Client. Steel door factory.
Problem: We didn't know in which regions certain types of doors and colors were in demand, where the demand and the size of demand were higher.
Our actions. We implemented a filtering system on venezuela consumer email list the site and set up a report on it. Based on the information from the report, we developed an automatic module for holding promotions on the site.
194_2.pngFilters in the catalog, with the help of which we identified the most popular product parameters
194_2.jpg
Unloading data on consumer preferences. At the top is the distribution of preferences for certain product models by gender and age of the buyer. At the bottom are functional and aesthetic needs not related to specific models
Before implementing end-to-end analytics, the client's website was visited by 26.4 thousand people per month. With an average check of 25 thousand rubles, they left orders for 19.8 million rubles. Accordingly, the conversion was 3%. We implemented end-to-end analytics, and after making decisions based on data, the conversion increased to 4%, respectively, a third more orders began to come through the website.