Launched in 2012, SendinBlue was created to become the simplest, most reliable and cost-effective marketing platform. This all-in-one solution now powers marketing campaigns for more than 50,000 companies around the world. With offices in North America, Europe, and Asia, the SendinBlue team supports the product in six languages. Their platform integrates with the top e-commerce and CMS tools, and their system delivers over 30 million emails and text messages per day.
SendinBlue is a relationship marketing SaaS solution that allows customers to execute email and SMS campaigns. Because the core of its service is the successful sending of customers’ messages, SendinBlue must maintain exceptional delivery scores and therefore cannot allow the use of stolen, false, or purchased email databases that would decrease its email reputation. So when customers upload databases, SendinBlue must ensure all contacts on the list are opted in. To be considered as opted- in, these contacts must have been in touch less than two years ago. They must be able to unsubscribe and can only receive emails related to their initial queries.
SendinBlue used to rely on manual validation of new customers, checking (among other things) the quality of their databases, but it was a painful and long process that required a large workforce. It also severely delayed account validation for customers and was therefore detrimental to SendinBlue’s reputation. Faced with a growing customer base, SendinBlue could not continue down the path of manual validation and feasibly scale.
To move away from manual validation and allow for rapid growth, SendinBlue turned to Dataiku, leveraging their expertise in the area of automated fraud detection. With Dataiku, SendinBlue built a scalable solution using historical data coming from more than 1 billion emails and associated events (clicks, opening rates, bounces, etc.), thousands of blocked accounts, and hundreds of fraud criteria (IP addresses, history, behaviors, etc).
SendinBlue uses Dataiku to:
- Analyze new customers and automatically classify them as “good,” “bad,” or “uncertain.”
- Once the new customers are classified, an algorithm determines the customer’s credibility by taking into account sending volume, the scoring of the contacts, etc.
- Depending on the customer’s risk score, they may be blocked, validated, or sent to customer care for manual analysis.