I’m going to reveal in this case study how a Facebook Messenger chatbot helped our client generate and pre-qualify the leads required to pre-sell 3 apartments in 10 days.

Summary of results
Here’s a quick summary of the results so far (It’s still running but the bot has already earned back the initial investment 100-fold so we can draw some conclusions)

Adspend: £94
Link clicks: 1064
Messaging conversations: 243
Pre-qualified leads collected: 60
Apartment reservations made: 3 (during 10 day)
CPL (Cost per lead): £1.6

Background / Issue at hand
Usually, when a new apartment building or house is being built, the agency tries to sell out as many of the available apartments as possible, even before any of the actual construction has begun.
The sales process begins pretty much right away after approval from legislative bodies is received and before the actual construction of the building starts.
The agents get to work with the sales, usually, that means Facebook ads, utilizing their personal network, driving traffic to a landing page, and so on.

The goal is to get the contact information to take the communication with the potential lead to a real agent.
In all cases, what the real estate agents ultimately need is the contact information for a person who wants to know more about the development. Once they have that data, they get in touch with them and handle the leads one-on-one.

Naturally, a Facebook Messenger chatbot is a great solution in this situation. If you don’t know what a messenger bot is, you can check out one HERE
Advantages that a bot brings to the table:

  • Much better conversion rates than on a landing page; reduced cost per lead
  • Pre-qualifying leads and learning about their preferences before you even talk to them
  • Thanks to lead pre-qualification, the real estate agents spend time with only really interested leads.
  • Automated follow-ups

    What we wanted the chatbot to do:
  • Start a conversation with a potential buyer. We used Facebook ads to drive traffic to the bot
  • Introduce the specific real estate project to the potential buyer(location, price, rooms, etc with images). Understand if the potential buyer is interested in that specific real estate project and what exactly is he/she most interested in (1/2/3 bedrooms etc)
  • Collect the lead’s contact information (phone/email/preferred contact time)
  • Pass that information over to the realtors
  • Follow Up with the potential lead in case of non-conversion and understand why he/she didn’t convert

    Note #1. A chatbot is perfect in a way that it can interact with the person without being too pushy, if the user is ready to take the communication over to a real estate agent, he can do that. If he wants to get more information, he can do that too.
    Once the user is ready to talk to the agent, the chatbot asks the person about her preferred channel of communication (for example phone vs email). In our experience, people prefer to leave their email in 90% of the cases.

Note #2. Once the potential buyer’s contact information is collected, the bot can do a number of things:

  1. show the lead other real estates for sale
  2. take the user to a website
  3. provide financing options
  4. and so on.

    Imagination is pretty much the limit. In this case, we’re giving the user an opportunity to check out more pictures of the property as well as check out other developments.

What if the user leaves the bot without leaving contact
Not a problem. It means that either the person is completely uninterested in that specific apartment, accidentally clicked on the ad, that specific apartment didn’t match his/her needs, has some other real-estate needs, something else came up, etc.

This is where a chatbot is so much more powerful than a landing page. If someone comes to your landing page and leaves it without leaving their contact information, the only way to re-engage them is retargeting ads, however, that doesn’t tell you why they actually left the website.
Here’s where a bot has huge benefits. Even though the person didn’t leave their contact information, we have an opportunity to send them a follow-up message.

In this specific case, if the person leaves the conversation halfway – the bot sends them a follow-up message and gives them an opportunity to continue where they left off, or tell the bot if they’re not interested or would like to find some other real estate – the bot obliges 🙂
This is amazing because you will find out exactly why that person didn’t give their contact information and you can adjust based on that – and you can do that in an automated way. For free.

The Results
The bot in its nature is extremely simple and there’s lots of room for improvement but the results we’ve had with this are quite astonishing.

This bot has been actively running for 10 days. The ad spend has been 94 EUR (roughly 100 USD).
Link clicks: 1064
Conversations: 243
Contacts: 60
Apartments: 3

From a total of 1064 link clicks 243 have converted into actual conversations (leads that the bot can follow up with for example). From those 243 conversations, the bot has pre-qualified and collected the contact information of 60 people. The cost per pre-qualified lead is £1.6
(approx 2 USD). From those contacts already 3 people have made a pre-reservation for at least one apartment in the building that is going to be built. Not a bad return on an investment 🙂

So there you go, if you’re looking to sell some apartments and need someone to do the legwork of generating potential clients for you on autopilot — a chatbot is the way to go.
I hope this case study gave you some ideas on how you could use a chatbot in your own business.

Especially if you’re in the real-estate business, then we’re happy to help you get set up. Just shoot me a message on LinkedIn and we’ll take it from there.

Founder MVee Media
LinkedIn – www.linkedin.com/in/mark-valerio

Case Study created by MVee Media.

We build chatbot solutions ranging from simple lead generation bots to complicated Natural Language Processing bots with custom integrations. Companies we work together include small businesses, government organizations as well as large corporates.