Post by account_disabled on Nov 26, 2023 3:41:50 GMT -5
That is companies should approach data analysis in an agile manner and change the parameters of this analysis if necessary. Below I will present a case study that visualizes the importance of analytics for business. This illustrates how to use the data already analyzed in the DMP to increase the profitability of your operations. Case study Big Analytics Big Data analytics case study in ecommerce Big Data in ecommerce Before using analytics the results of our client's mailing campaign were at the level of of email openings.
Of these only of users who opened the email went to the client's website. After introducing a completely simple even basic analysis which involved combining customer data and de facto reducing the number of emails sent previously there were two messages one for online Email Marketing List shopping and the other for offline shopping the campaign open results increased to . and page transitions up to . As a result it generated . more revenue. In the next step we introduced analytics which allowed us to combine customer data and analyze purchase history. Email opening results increased to and visits to the customer's website increased to.
At this stage we no longer sent mailings about promotions for products that the customer had recently purchased. The next step was to introduce customer segmentation and analyze purchase willingness and needs. This had an even greater impact on the open results increased to and website transitions increased to . With the right data we sent the customer only information about products that met his needs at a specific time. The last and most advanced stage of analytics introduced in this case was building predictions. It is predicting what and when the consumer will want to order. We did this based on historical data and correlations of individual products.
Of these only of users who opened the email went to the client's website. After introducing a completely simple even basic analysis which involved combining customer data and de facto reducing the number of emails sent previously there were two messages one for online Email Marketing List shopping and the other for offline shopping the campaign open results increased to . and page transitions up to . As a result it generated . more revenue. In the next step we introduced analytics which allowed us to combine customer data and analyze purchase history. Email opening results increased to and visits to the customer's website increased to.
At this stage we no longer sent mailings about promotions for products that the customer had recently purchased. The next step was to introduce customer segmentation and analyze purchase willingness and needs. This had an even greater impact on the open results increased to and website transitions increased to . With the right data we sent the customer only information about products that met his needs at a specific time. The last and most advanced stage of analytics introduced in this case was building predictions. It is predicting what and when the consumer will want to order. We did this based on historical data and correlations of individual products.