A customer had a database of hundreds of thousands of e-mail addresses. However, not all of them were active. We analysed historical opening and click-through-rate data for e-mails involving the store’s sales campaigns.
3 factors indicate the usefulness of e-mail addresses: the historical activity of the e-mail, its appearance, and the domain in which the address was located. Thanks to these conclusions we determined the probability of opening the message and clicking on the sent content.
We reduced the e-mail address database by 34%, removing addresses with a very-low probability of clicking.
10x
We’ve received a 10x return on investment compared to previous campaigns.
37%
We increased the conversion to 37% with the assumed 10%.
We saved 30% of the mailing budget.
30
We will analyse the historical results of e-mail-derived shipments and check how effective they were.
Our model will evaluate what and to what extent testifies to the quality of your database records. Each record will receive a quality rating. The lowest-rated ones will be omitted in the next steps.