Customer Spotlight
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William Huffman: A Strategy For Reaching Out To Potential Customers

BROKERAGE

RE/MAX Results: A Good Life Group

CUSTOMER SINCE

July 2018

William Huffman is the kind of real estate agent that gets rave reviews from his customers. “It’s like you’ve known him forever after just a few minutes,’’ wrote one of Huffman’s customers about William and his A Good Life team at RE/Max Results in Edina, Minnesota.

It’s probably because Huffman regularly goes the extra mile. Handwritten notes, extra attention to customers with special needs, and rental suggestions for people who aren’t quite ready to buy a house. It’s part of who he is. “We work by referral. Well over 80% of our business comes from referral,’’ Huffman says.

But staying in touch with the customers and potential customers is a daunting task. “We’ve got a database of 15,000 people,’’ Huffman says. He’s got a CRM but needed a solution to help him focus on the customers most likely to need his team’s services in the next year. “They say if you deep dive into your database you should be pulling out 6% of them in transactions each year.”

The question was how to do that.

He looked at one solution that promised to identify top prospects, but they also charged to send the marketing materials on his behalf to the people they identified. “I didn’t want that,’’ Huffman says.

So after hearing about First from a broker he respects, he decided to give it a try.

Set-up is a snap

His first task was to get all 15,000 contacts into First. “It was a heck of a lot easier than I thought,’’ he says. Huffman uses the Conversion CRM database. “I was dreading it because the files were everywhere. You’ve got your CSV files, they’re on your phone, they are in your Gmail. They’re in your MLS. I thought it was going to be brutal and it literally took me 12 to 15 minutes because you’ve got the step-by-step instructions on how to get them in there. It was pretty nice.’’

Although he didn’t have issues getting his contacts into First’s system, anytime he’s had a question about anything else he’s been thrilled with First’s customer service. “Everyone’s been very diligent about getting back to me.”

No more throwing spaghetti at the wall

First’s model combs through an agent’s network to identify the hottest opportunities among people they already know. The intent is to predict who will sell by tracking 700+ signals across 214 million people nationwide. Each contact gets a seller score, the higher numbers meaning they are more likely to list a property in the coming months.

“It really allows me to focus on those 100 people, those 200 people vs. all 15,000. It’s better than throwing spaghetti against a wall and seeing what sticks. You can do that, but who wants to send 15,000 mailers? That’s ridiculous. Now I can send 250 handwritten personalized mailers, cards, notes, emails or video.’’

And Huffman can see that it works. In the first month using First, five people flagged as possible listees had bought or sold in the last 60 days. He had two of those listings. “That is powerful stuff. If I had this tool a few months ago I might have gone 5 for 5! Heck even adding one more would have been awesome,’’ Huffman says.   

Using Facebook for customers with high seller scores

Huffman uses targeted Facebook ads quite frequently. With seller score information he can zero in on a smaller portion of his database, saving money. “We can touch those 250 people six, seven, eight times for less than $500. A phone call is easy, a text message is easy. A Facebook message or handwritten note is extremely easy.”

His team never mentions anything about a client’s seller score when they contact them. “We just write, ‘Hey, I just wanted to see if you got that note that I sent you. I haven’t seen you in a while, just been thinking about you.’ It’s just to start that conversation organically vs. jumping right in and going to the sale.’’

The process often leads to conversations that help enrich his database, or help him get some of his services in front of customers. “We’ll have people tell us they aren’t selling now but they are looking to do so in the next few months.’’ He then adds those notes to his database, “and then we can check back because they said they might need some help in three months.’’

Spreading the word about his unique services  

One of the advantages of the First’s machine learning approach to surfacing potential clients is that it gives Huffman the opportunity to pitch his unique home fix-up program.

Recognizing that many people feel trapped into selling a house “as is” because they can’t afford to remodel it or fix serious issues before putting it on the market, Huffman offers a program to front the money for the fixes with the money paid back at closing. He calls it owner-occupied flips and uses his own do-it-yourself background coupled with the services of a staff handyman and, if necessary, a licensed contractor.

“If they use a service that just buys as-is they’re just going to get the initial price,’’ Huffman says. The way his group does it, “If we can get them an extra $30,000 that is going to change their whole outcome going forward. Everyone truly wins at the end of the day.’’

And it also means a lot of referrals which need to go into his database – and then need to be organized, and scored so that Huffman and his team aren’t writing out 15,000 handwritten notes each month.

A definite win for efficiency.

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