Buying a property to live in is often the biggest financial commitment a person will make. Financial technology company Trussle aims to make this process easier by matching consumers to the ideal mortgage or remortgage product to finance the property through a recommendation engine. Robert Fairfield, the VP of platform, and his team learnt four big lessons during and after creating the engine.
The recommendation engine works by taking 3,000 to 4,000 discrete data points from each customer. These data points will be hard or soft. The hard ones are about the person’s age, income, location and things that are easy to quantify.
The soft data points are about the customer’s preferences as well as their attitude to risk, and the likelihood of their circumstances changing such as an increase or decrease in income or starting a family. The engine also takes into account facts about the UK housing market, on a macro and a micro level.
Fairfield said: “We take all that information and mash it all up to try and end up with a single recommendation for a customer and say ’of all of that uncertainty and all of that information, this is the right product for you’.”
"They know they are going to sell, they are built on demand."
The most important lesson he and his team learnt, after ironing out some of the bumps, is that there will always be some people for whom a product cannot be found. In that case, build a product for them. This, for him, is an opportunity. He said: “That is incredibly exciting. The next wave of product innovation is going to come from those those companies that have identified that and gone out and built specific products for those customers. They know they are going to sell because they are almost being built on demand.”
The customers that are currently underserved by the mortgage market are the self-employed and the older generation. “We know they aren’t getting a great deal so we are building products with lenders to service that.”
He added that more and more technology companies will start to do this and gave the example of a tech giant that has followed this model of product development. “When Netflix create content, they know it is going to be consumed because they are identifying areas where the recommendation engine comes up short.”
Another important lesson learnt on this journey is to be transparent. “Don’t build a black box,” he said. Fairfield saw that in the past, the recommendation engine was working well and Trussle was presenting mortgage products to customers with a high degree of confidence that it was the right one for them. But the customers often would not take it. “We realised that, especially for quite complicated decisions with serious consequences like a mortgage, people wouldn’t just believe us and take our recommendation.”
This was because it was not clear how the ultimate decision had been reached. Mortgages can be fairly complex. Some mortgage products with higher interest rates and lower fees can end up being cheaper that superficially more attractive products that have lower interest rates and higher fees. When Fairfield and his team started to explain the decisions made by the recommendation engine to the customer, they started to get a much better rate of conversion.
"To buld trust, educate and empower your customer."
He said: “We began to show the next best alternative and explain why that was not recommended to the customer. Trust is incredibly important; it takes a lot of time to build. The best way to do that is to educate and empower your customers.”
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