Drip Mailing Campaign


Mattermind Labs proposed to build a drip mailing campaign that would send personalized listings recommendations to new clients, to bring them back to the website. These are clients that we know are actively looking for a property, and we know from their activity exactly what they are looking for. This is a chance to please customers, so it's a very effective and positive form of email marketing.


This project makes use of two existing systems previously built by Mattermind: the Property Listing Recommender and the Email Marketing platform.

The property listing recommender system is driven by user interactions: Showing Requests, Favorites/Saved Listings and clicks on listings in emails. As we gather more interactions, we learn more about what properties the user is searching for.

The Email marketing platform is used to send a large variety and volume of email campaigns. Responsive HTML emails are designed to look good on desktop and mobile email clients. For many years all mailings were sent using our own servers, but we have now migrated to SendGrid for faster delivery, better tracking and availability.

After presentation of the idea and approval by Nest Seekers we built a process system to send emails are sent to new clients with personalized recommendations. Initially clients get these emails daily, tapering off as they stop clicking on offers, and eventually stopping entirely.

Clients and contacts can always easily manage their email preferences, subscribing or unsubscribing. The system is fully CANSPAM and GDPR compliant.

  • 267k emails sent to 18k contacts
  • 27k clicks: 10% Click Through Rate

We believe the high CTR is because these emails are fulfilling a known need; these are qualified leads that have already submitted showing requests and are actively looking for properties to buy or rent.

Non-personalized marketing emails have a much lower click through rate, more in line with the Real Estate industry average CTR: 1.8%.

Drip Mailing Campaign


  • Email
  • Sendgrid
  • Django
  • Pandas
  • sklearn
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