Client Overview

In the aggregator market, small data discrepancies can have outsized commercial consequences.  Our client is a large, established UK general insurer that relies heavily on Price Comparison Websites (PCWs) as a primary distribution channel for its motor and home insurance products. For a company of this scale, operating in the highly competitive aggregator marketplace, the accuracy of the data flowing from a customer’s quote journey into their own pricing and underwriting systems is not merely an operational detail;  it is a strategic necessity. However, the client was facing significant challenges in maintaining data integrity due to the inherent complexity of the PCW ecosystem.

The Challenge

In the insurance aggregator channel, data integrity is fundamental to profitability. The journey of customer information from a PCW interface to an insurer's final quote is extremely complicated, creating multiple opportunities for data fields to be incorrectly mapped. These errors can have severe consequences, directly impacting pricing accuracy, risk selection, and the overall performance of the distribution channel. An insurer could be losing desirable business due to inflated quotes or, conversely, writing unprofitable business based on flawed risk information.

The client's core problem was a lack of visibility into this critical data transfer process. They could not be 100% certain of the reliability of their data feeds, preventing them from fully optimising their customer journey and pricing strategy. Their objective was to gain clear, actionable insight into these potential data mapping errors. They needed to identify exactly where discrepancies were occurring, understand their impact, and determine whether they were inadvertently losing valuable customers or writing business they didn't actually want. The client required a definitive diagnostic solution to move from a position of uncertainty to one of informed control.

Our Approach

The analysis revealed that the insurer’s perceived “ideal” premiums for core profiles were no longer competitive in the live market. Their pricing strategy, while analytically sound in isolation, did not reflect the real-world behaviour of brokers and the variability introduced through differing commission structures.

Our team demonstrated how adopting a multi-segmented pricing approach, aligned to specific broker strategies and customer profiles, could improve competitiveness and panel share. We also modelled the likely outcomes of broker-level adjustments, helping the client quantify the trade-off between competitiveness and profitability.

The Outcome

• Critical Pricing Inaccuracies: The diagnostic found significant premium mismatches on quotes that should have been identical. In one startling example, there was an average difference of £206 on otherwise identical risks. Furthermore, the analysis revealed huge, unexplained differences in Compulsory Excess (Comp XS), directly affecting the final price presented to the customer and the insurer's competitiveness.
  
  
• Flawed Risk Assessment: Fundamental risk data was being incorrectly mapped, leading the client to misjudge the business they were writing. For example, some customers who were "Part Time employed" were being recorded as "Full Time". More alarmingly, the diagnostic found that the client's system was systematically failing to record home insurance claims for major perils. For customers who declared a claim for 'Escape of water', the data was incorrectly mapped nearly 70% of the time. For 'Theft', the failure was absolute: 100% of declared theft claims were missed, with the client’s system incorrectly showing 'No' claim had been made. This exposed the insurer to a catastrophic underestimation of risk on the business it was writing.
  
  
• Inconsistent Quoting Across PCWs: The analysis demonstrated that the insurer's own underwriting and pricing rules were being applied differently across various comparison sites. A clear example was found in No-Claims Discount (NCD) rules, where "NCD capping" was misaligned between major sites like Confused.com and Money Supermarket, resulting in inconsistent pricing for the same customer profile.

So what? (Why it matters)

In the hyper-competitive PCW marketplace, what an insurer thinks its price is based on can be fundamentally different from the data it is actually receiving. This data integrity gap represents a critical, and often overlooked, strategic vulnerability. For this insurer, that gap meant they were blind to 100% of declared theft claims, fundamentally breaking their risk assessment model. A single mapping error can ripple through a portfolio, leading to inaccurate pricing, adverse selection, and ultimately, reduced profitability.

By eliminating this data blind spot, the insurer gained the confidence and control to truly optimise its distribution strategy. They could now ensure their pricing was accurate, their risk assessment was sound, and the business they were writing aligned perfectly with their strategic appetite.

This case highlights a common vulnerability for any insurer that relies on the aggregator channel. It demonstrates that data integrity is not just a technical concern but the essential foundation for effective performance. Without 100% certainty in the reliability of data transfer processes, even the most sophisticated pricing strategies can be rendered ineffective.