Thomas Downing’s database was in such need of a thorough cleanup that he hired an intern for the summer to work on it. The intern didn’t show.
Conveniently, First had just inked a partnership with @properties to offer its services. First is known for its seller scores that help agents understand who in their contact lists are most likely to list a home in the coming months. Before it can do that it needs to scrub, dedupe and augment an agent’s database.
That’s just what Downing, of The Thomas Downing Group in the north shore suburb of Glenview, needed.
First’s database cleaning does not replace a CRM --those serve a different purpose. Instead, the goal is to provide First’s machine learning with the information needed to help determine who is likely to sell. To do that effectively First draws in contacts not only from a CRM but also from social media contacts and other databases.
So how did Downing’s contact list fair?
First’s automation flagged 236 junk contacts and 81,781 duplications. It added 3,405 property addresses to existing contacts along with 1,253 Facebook, 1,631 LinkedIn, and 900 Twitter profiles.
“You guys did a great job of going through and data scrubbing and then sending it back to me with updated addresses. That was excellent,” Downing says.
Now he is looking forward to the next step in First’s relationship-building process: Seeing which contacts are most likely to sell and work on building deeper relationships with those individuals.
“There are times when I'm really good at (staying in touch with people), and there are times when I just get bogged down with work and we are so underwater with the listings and just try to get everything through the day,’’ Downing says.
Getting direction on who he should set up a lunch with or even send a video email to is going to be very helpful.
“I think the technology side of determining who’s likely to sell is spot-on. I’m looking forward to exploring it,’’ Downing says.