“We are the biggest creepers on earth.” Those are the words of Andrew Bermudez, CEO and co-founder of Digsy AI, an AI powered prospecting automation platform for commercial real estate. He is always good for a colorful quote and during his presentation at a recent CRE.tech LIVE broker marketing seminar in New York City, he was full of them.
What he was referring to is the way his software is able to automate and guide the brokers using it to follow best practice sales techniques to turn more prospects into clients. Sales tools like CRMs are in a war right now to see which can provide the highest value. Digsy AI differentiates itself as a simple prospecting platform that can work alongside any CRM to help brokers save hundreds of hours prospecting and generate more clients. Andrew thinks that this is accomplished by creating a simple and easy-to-use digital assistant tasked with the job of reducing the amount of work an agent encounters during prospecting, reminding agents to do the things that result in the highest probability of sales, and automating that workflow. In order to do that, he has to determine which interactions return the best results. That is where the Digsy AI intelligence engine kicks in.
“No one has ever been able to truly quantify what precise prospecting activities make a broker successful,” he continued. Generally associate brokers learn how to close deals from the older brokers. But the success of the new generation depends on whether or not the old guard is using the best techniques or not. Plus, with so much technological change the older generation are finding themselves taking cues from their younger, more inherently tech savvy co-workers.
So Digsy taught their machines how to identify which activities provide the best return in turning more prospects into clients. In order to do that, they attempt to automatically collect every bit of data about a broker’s daily processes. They make it easy for brokers to prospect in one place without having to toggle between our growing system of inputs such as Outlook, CRM, phones, etc. To do that they must document every text, email and phone call through their system and collect every bit of data they can. Even little data points like number of times an email is opened can result in some great insights.
For example, Digsy has noticed that when an email is opened by a recipient three times or more, there is a much higher chance of conversion. This makes sense, the person is obviously earnestly considering the contents of the email a broker has sent. So, they are building an alert system that tells the proper sales agent when that third open happens and suggests that they call right away. Andrew told us in a separate conversation that this feature will be available to its users in the next 45 days.
The Digsy team is using this technique on their own potential customers and had some great results. They have seen that people that are called right after the third email open will talk to the sales associate for an average of 35 minutes and have a 65% chance of becoming a user. Here he is explaining how the process can be compared to being single at a concert (I told you he had a way of adding color to the conversation):
They can take this even further, if you can believe it. If the email is opened on the prospect’s phone, they will then push the call to that particular device because they know that have that device in front of them.
One of the best quotes I got from Andrew was this: “Software can create efficiencies, but AI can find where the efficiencies can be created.” That, to me, really sums up the leap we are all about to make into the world of machine learning. Now, our computers are finding new ways that they can help us. But in order for that to happen, they need to be given the bird’s eye view of, well, everything.
Here Andrew gives a practical guide to implementing data analytics in a brokerage:
The reason that utilizing these new technology platforms like Digsy, or even Tableau for that matter, is staying ahead of the competition. Brokers can’t wait until a technology becomes commonplace, by that time it is too late and the adoption will not yield much competitive advantage. Andrew used the evolutionary term to describe his phenomenon: the “Red Queen Problem.” This edict states that a new evolutionary advancement will generate returns only for a finite time. A some point the competition will adapt as well and the playing field will return to parity, until the next advancement takes place.
So even if you are not trained in data science or software development, it is important to understand and begin to implement these data collection and analysis techniques in your brokerage. There are plenty of companies, like Digsy, that can help automate that effort and make it simple enough a 3rd grader can use it. Just don’t wait until everyone else has started to takes these steps because by then, it will probably be too late.