Most insurers don’t realise that they are sitting on a data goldmine when it comes to their customers. When households take out motor insurance, often for multiple vehicles, and home cover, there can be a wealth of data generated. However, with each record sitting in a silo, priced in isolation, never connected, the link is often not made.
This results in a household data blind spot that is costing insurers on both sides of the ledger.
THE PROBLEM WITH POLICY-LEVEL THINKING
Insurance has always been organised around policies not people. This is due to how products are structured, how systems are built, and how pricing models are trained. However, customers don’t live at policy level – they live within a household, taking a holistic view of all their insurance products.
For example, a household might comprise a customer with three policies across two insurers, a partner who also holds cover, and an adult child who quotes separately.
These are connected relationships with a shared address, risk profile and potentially shared behaviour in the quote market.
By pricing them as unrelated individuals, you are missing vital information. You may be over-pricing the household’s best risks, under-pricing the worst, and losing retention battles you didn’t know you were in.
WHAT A SINGLE CUSTOMER VIEW CHANGES
A Single Customer View (SCV), or more precisely, a Single Household View, brings together these connected individuals and policies to create a holistic picture. SCV doesn’t just identify “who is this person?” but “who are they connected to, what cover do they hold, and how do they behave in the market?”
The value of this information flows in three directions:
Pricing accuracy: When you know a customer is part of a multi-policy household, you have more signal. Their household’s claims history, their quote shopping behaviour, their financial indicators all enrich the risk picture beyond what a single-policy view can see.
Retention intelligence: Customers don’t just leave policies – they leave insurers. If a household’s home policy is under review, the motor policy may be at risk too. A household-level view of renewal intent is materially more powerful than a policy-level one.
Opportunity identification: The household you’re retaining on motor cover, may hold home cover elsewhere. The quote signal that reveals shopping behaviour on one product might also flag cross-sell timing on another.
WHAT PERCAYSO INFORM DELIVERS
Percayso Inform’s Intelligence Hub is built around this problem, and the path to solve it is clear.
Starting with Policy Intelligence, Percayso’s entry-level lifecycle product, that enriches your policy book with external intelligence. It also monitors the market quote feed continuously, alerting you when policyholders are actively shopping for alternative cover. By being informed of policyholders’ intentions thirty to fifty days before renewal, there is still time to act.
The next step is Customer Intelligence – a household view built on the same data feed.
By taking the enriched policy data and resolving it into a Single Customer View, you can discover entity resolution across households and multi-policy relationship mapping. The result is a view of connected individuals, their full quote history, and their collective behaviour across the market.
With no additional data pipeline, the upgrade from Policy Intelligence to Customer Intelligence uses the same feed, making the operational lift minimal and the intelligence gain significant.
Underpinning both products is Quote Intelligence – the UK’s largest quote lake, with billions of full quotes across motor, home, and commercial lines. When Customer Intelligence searches the household cover, this search enrichment tool is scouring this dataset. It picks up every identity variation, every quote, and each behaviour pattern the household has left behind across the market. While identifying fraud is Quote Intelligence’s original function, it has now evolved as a tool to highlight the positive “Green” risk as well.
This is not a dataset any single insurer can replicate. It’s a network effect, and it becomes richer with every contributor.
PULLING AHEAD OF THE COMPETITION
There’s a pattern in insurance data capability where the gap between leading and lagging firms widens slowly, then suddenly expands. The insurers who built enrichment pipelines early have been pricing better for years. The ones who moved to full-quote enrichment will have better models. The next step in this evolution of information use is household resolution, and the timing is now.
The data is available. The integration path is straightforward. And the insurers who build the household view first will have a competitive advantage in pricing, retention, and customer lifetime value, creating a gap that will be very difficult to close.
FIND OUT MORE
Customer Intelligence and Policy Intelligence are both live on the Percayso Inform Intelligence Hub. If you’d like to see how the household view performs on your own book, our Evaluate service can quantify that, on your data, before you commit.
