Case study: Separating self-competition from real competition

It's a blunt instrument. Discount broadly enough and quote volumes will move, but so will margin, including on business that was already won, in segments that never needed a single point of movement. For a large, multi-brand UK personal lines insurer writing Motor and Home through price comparison websites, that was precisely the trap to avoid.

The Client's portfolio spans several underwriting brands and tiers, all competing for the same PCW real estate. Before any pricing or renewal decision, its teams needed to untangle three things that had always been discussed as one: where the Client was really competing with itself, where it was competing with named rivals, and where, specifically, there was room to move price without giving margin away for nothing.

Sorting friend from rival

The first job was working out what a brand family's own footprint on a comparison panel actually means. When several of the Client's own brands and tiers turn up together in the same customer's top ten quotes, is that a shelf-presence win, or is it one arm of the business quietly undercutting another?

We built the analysis around a "P1 to P10" model: for every simulated risk, which brands appeared in the cheapest ten quotes, and in what order. Applied across the Client's competitor set as well as its own brands, the pattern differences were sharp. One rival's competitive share sat almost entirely with a single flagship product, its other, longer-established brands barely registering. Another had hundreds of distinct brand-order combinations, a sign that its tiers had never been designed to work together and that most of its stacking was incidental rather than intended. A third ran a near-identical entry, mid, premium order in more than nine cases out of ten: a "good, better, best" shelf strategy, built on purpose.

That mattered because, until this analysis, the Client had no way of knowing which camp its own brand family fell into. The answer settled a genuine strategic question rather than a hunch: its multi-brand set-up was, in places, working against itself, and the fix was a tiering discipline other groups had already proven out, not a fresh round of pricing changes.

The one comparison that actually mattered

Benchmarking against twelve-plus competitor groups gives an insurer a sense of where it stands in the market. It doesn't tell it what to do on Monday morning. For that, the analysis narrowed to a direct, two-way comparison between the Client's combined brand footprint and the single rival group it was most consistently quoted alongside.

That meant mapping every risk where both groups quoted: where their pricing already overlapped closely enough for a small, targeted move to change the result, and where each side was instead winning quotes the other never saw at all. Two entirely different problems, requiring two entirely different responses.

A "P1 to P5 versus P6 to P20" view sharpened this further, measuring how often the two groups already stacked together in the top five, how often one side won while the other sat just outside contention, and the average discount that would be needed to close that gap. Distance, in other words, wasn't just described. It was priced.

That price tag is what let the Client's commercial team move from "we're behind this rival" to a business case a board could actually approve: a specific gap, with a specific cost attached to closing it.

Turning overlap into a shortlist

This is where the analysis stopped being descriptive and became actionable. Every customer segment the two groups quoted on, split by driver age, vehicle age and value, no-claims discount, property age, rebuild value, and property type, was scored on three measures: how much overlap already existed, how strong the win rate was on each side, and the discount required to convert a near-miss into a genuine win.

The output was a short, ranked list of segments where the gap was both real and cheap to close, sitting next to an equally explicit list of segments to leave alone, where further discounting would only erode margin on business already secured. One segment that looked, on the surface, like an obvious target turned out to carry a far higher cost to win than a less visible one nearby, exactly the kind of distinction a market-wide rate cut would never have caught.

For the Client, that meant a pricing change it could actually defend internally: a handful of named segments, each with a quantified upside, rather than a blanket move it would have had to justify after the fact.

Checking the numbers against real people

Quote-level pricing data can show exactly where a competitive gap sits. It can't say why a customer actually switched, stayed, or walked away. So before any of this became a pricing decision, it was tested against an independent consumer behaviour tracker: the stated reasons customers left the Client's brands and its closest rival, how often shoppers-at-renewal were successfully retained on price, and prompted brand awareness and consideration by age group.

That gave the Client a second lens on the same question: confirmation of where a pricing gap was the whole story, and a flag for where brand recognition or customer experience, particularly among younger consumers, was doing at least as much work as price.

In practice, that stopped the Client from over-correcting on price in segments where the real issue, and the real fix, sat somewhere else entirely.

The result

What the Client received wasn't a single number or a broad instruction. It was a repeatable diagnostic, built on daily-refreshed data and a consistent structure across Motor and Home, showing exactly where its own brands helped or hindered each other, precisely how it measured up against the rival it competed with most closely, and a short, evidenced list of the specific moves worth making next.

No blanket discounting. No guesswork. Just the segments worth targeting, the ones worth leaving alone, and the numbers behind both, refreshable at every future pricing review rather than rebuilt from scratch each time.

Bring this precision to your own pricing

If you'd like to see how this kind of analysis could apply to your own portfolio, get in touch with Consumer Intelligence to discuss a bespoke benchmarking and portfolio strategy engagement.