Situation:
Throughout life, we all see a lot of massive buildings being built, some of us even get to see them in all areas of the world. Apartment complexes like these are built by what is known as development companies. In this case study, we will focus on the Swedish development company Ikano Bostad which is owned by the IKEA-founding family.
Ikano Bostad won’t start the property development before +60-70% of all apartments in a specific complex have been sold. Based on this you can probably imagine how important it is to activate their leads, as in, no leads, no projects, no future company.
Fair, that was a bit dramatic but you get the idea.
Now, Ikano Bostad doesn’t handle the full sales process but relies on Housing Estate Agencies to sell on its behalf. Nevertheless, they need to take a lot of initials to get projects off the ground.
Complication:
The lead conversion rate in this industry is rather low. I mean a lot of us have been there, daydreaming about a new apartment or similar – so if it’s easy to sign up, then why not?
Anyway, To stimulate conversions Ikano Bostad was visualizing a nurturing program for their leads to accelerate conversions.
Why you might ask? Well… Picture having a plot set aside, planning permissions, architects hired, etc.. There are a lot of “bricks” that go into a project like building 100+ apartments. So the faster the project gets off the ground, the better. The slower the progress, the higher the cost.
This means that investing in a faster conversion is a no-brainer, but resources allocated to nurturing leads were still limited, so they had to be smart in their approach.
Opportunity:
This narrowed down their options to two.
They’d either have to apply business logic and set up rules to figure out who to go after. Option 2 would be using Allyy.io, to sniff out the right scents and scope the hunt based on data (writing this while there is a dog at the office, hence the reference).
Resolution:
Ikano Bostad decided to use Allyy.io because a data approach is almost always smarter than logic based on human bias.
At this point, you might be wondering (or not) based on what data, and what were the results?
First things first – we based the Machine Learning models on data from historical buyers, where we could then extract trends and similar traits to predict the audience of future buyers. This means that Ikano is now able to allocate the most estate agent resources to the leads that are more likely to purchase.
Now, to the results. With Allyy.io, Ikano was able to identify 70% of hot leads, within 20% of the total leads, as well as have an overview of the lead quality per project.
So not only were they able to pinpoint who to focus on but also able to decide if a specific project needed further marketing investment to bring up the lead quality.
