Exciting Insight

Something for the weekend?

Written by Ian Hughes | 18/12/25 10:51

Many UK insurers now price differently on weekends than on weekdays. Some reduce premiums on Saturdays and Sundays. A smaller group increases prices on Sundays (when their call centres are closed) .
 
Pricing strategies vary, but the outcome is consistent: weekend shoppers see different prices than weekday shoppers for identical risks.
 
This creates a problem that extends beyond the weekend itself.

How conversion data gets contaminated
 
Weekend shoppers convert at different rates from weekday shoppers. This isn't surprising - they're seeing different prices, they're shopping in other contexts, and they're likely responding to different competitive dynamics.
 
But if your pricing uses quote-to-close ratios to automatically adjust rates, your system now treats weekend conversion data and weekday conversion data as interchangeable. Your Monday pricing reflects weekend performance. Your models are adjusting weekday prices based on weekend shopping behaviour that won't repeat until the following Saturday.  
 
The feedback loop takes time to correct itself. During that period, your pricing responds to patterns that don't reflect the market you're actually pricing into.
 
We have to assume that those companies that are adjusting rates on the weekend have built this into their models.  But if you aren't, then Monday is going to look odd (as is the weekend).  So if you have odd Mondays, well, now you know why!
 
 The market-wide effect
 

Our data shows weekend pricing strategies are now widespread across both motor and home insurance. As more insurers adopt these approaches, the conversion gap between weekend and weekday widens.

This affects everyone in the market, not just those running weekend campaigns. If your competitors are pricing differently on weekends, your weekend conversion rate changes even if your prices don't. Your pricing models now use data that reflects market-wide behaviour shifts, not just your own strategy.

The more common weekend pricing becomes, the less reliable blended conversion metrics become for either period.

The regulatory question

Consumer Duty requires insurers to demonstrate that customers receive fair value. This creates a specific challenge for weekend pricing strategies.

The risk being insured hasn't changed between Saturday and Tuesday. The customer hasn't changed. The cover is identical. The only difference is the day of the week.

If the same risk costs different amounts on different days, that difference needs justification through a Consumer Duty lens. Distribution costs, operational capacity, and claims handling availability may provide valid reasons for price variations. But if weekend pricing is primarily a competitive or conversion optimisation tactic, the justification becomes harder.

Our analysis suggests this question hasn't been widely addressed yet. But the FCA's focus on fairness and value means pricing strategies based on shopping day rather than risk factors merit careful review.

What this means for pricing teams

Three practical considerations emerge from our market tracking:

First, conversion analytics need to explicitly segment weekend and weekday performance. Blended metrics will increasingly mislead as weekend strategies become more prevalent.

Second, automated pricing systems using conversion data need to account for day-of-week effects. Models that don't distinguish between weekend and weekday shopping will adjust prices in response to mixed signals.

Third, the Consumer Duty implications of pricing the same risk differently based on the shopping day need evaluation. The regulatory position on this isn't yet settled, but the underlying principle—that customers should receive fair value regardless of when they shop - seems clear.

What Consumer Intelligence tracks

We continuously monitor pricing movements across the UK insurance market. Our intelligence captures not just individual insurer strategies but the cumulative market effects those strategies create.

Weekend pricing is now sufficiently widespread that its impacts extend beyond individual competitive positioning. It affects conversion patterns, model reliability, and regulatory risk across the market.

For pricing teams, understanding what competitors do on weekends is just the starting point. Understanding what it means for your own data, your models, and your regulatory position matters more.