The loyalty penalty: what regulators and researchers actually found

The loyalty penalty is the extra amount existing, long-standing or disengaged customers pay for a product or service compared with new or switching customers buying the same thing, often because prices quietly rise the longer someone stays.

About this evidence review

This page is a structured, sourced review of the primary evidence on the loyalty penalty: the pattern of existing customers paying more than new ones, sometimes called price walking, tenure-based pricing, inertia pricing or a loyalty tax. It also includes closely related evidence on personalised pricing and on the switching costs and status quo bias that let the penalty persist.

Scope and inclusion criteria. We include substantial primary sources: regulator market studies and policy statements, official consumer-body super-complaints, and peer-reviewed papers. Every row is a source we opened and read; where a source gives a number, we report it exactly as the source states it, for the market and period the source studied. We do not include news articles, blogs or encyclopaedias as sources, and we do not compute, convert or combine numbers of our own.

Dataset details. Data compiled 27 October 2026. Version 1.0. Licence: Creative Commons Attribution 4.0 (CC BY 4.0). You are free to reuse the dataset with attribution to Coin & Cortex.

Download the full dataset (CSV) — all 24 rows with every column, including method, caveats, source URLs and the verification date.

The evidence table

A compact view of all 24 sources. The full dataset, including method and caveats for each row, is in the CSV download. Magnitudes are quoted exactly as each source reports them; currencies and units are not comparable across rows.

What the evidence shows

The sections below separate what the sources say from our reading of them. The first paragraph of each point sticks to the sources; the sentence marked "Our interpretation" is ours.

The penalty is largest and best-measured in insurance

The clearest, most consistent findings are in insurance. The UK FCA (LP04, LP05) documented systematic price walking in home and motor insurance, with 6 million policyholders paying high prices in a single year. The Central Bank of Ireland (LP16) put concrete tenure figures on it: customers of nine years or more paid 14% more on car insurance and 32% more on home insurance than a first-time renewal customer. EIOPA (LP17) described the same mechanism across the EEA. These sources use comparable methods (pricing data by tenure) and point the same way.

Our interpretation: insurance is where the penalty is both large and easiest to measure, because renewal pricing by tenure is explicit and regulators obtained firm-level data.

Where regulators intervened, the gap narrowed

Two sources let you see before and after. The FCA banned insurance price walking in 2021 (LP06), requiring renewal prices no higher than the equivalent new-business price. Its own 2025 evaluation (LP08) found the home-insurance gap between existing and new customers almost halved, from £95.38 to £49.17 on average. In energy, the CMA's investigation (LP09) and Ofgem's default tariff cap (LP10) addressed overpayment on default tariffs.

Our interpretation: the insurance evidence is the strongest case that a targeted pricing rule can shrink the penalty, though the FCA notes this played out against unusually high insurance-price inflation, so the raw gap is not a clean experiment.

Across markets, the common thread is inertia

Energy, telecoms, mortgages and savings all show a version of the same thing: default or long-tenure customers pay more (LP07, LP09, LP11, LP12, LP14, LP15). The academic papers explain why it persists. Samuelson and Zeckhauser (LP22) established status quo bias; Handel (LP21) estimated an average family switching cost of about $2,032 in health insurance; Giulietti and colleagues (LP23) and Wilson and Waddams Price (LP24) showed UK energy customers switch little and, when they do, often capture only part of the available savings.

Our interpretation: firms do not need to force the penalty on anyone; ordinary inertia and switching costs do most of the work, which is why disclosure alone has rarely been enough.

Where the evidence is weaker or points the other way

Online personalised pricing is the clearest counter-case. The European Commission's mystery-shopping study (LP18) found no price difference in 94% of 34,403 matched product pairs. The US FTC's surveillance pricing work (LP20) and the OECD note (LP19) describe the capability and incentives for individualised pricing, but the FTC findings are initial and qualitative, and the OECD's number comes from a stylised experiment, not a real market.

Our interpretation: the tenure-based loyalty penalty in regulated essential services is well evidenced; widespread individualised online price discrimination is not yet, though the data infrastructure for it clearly exists.

Limitations

• This is a structured evidence review, not a systematic review with meta-analysis. We did not register a protocol or statistically pool results.
• There is selection and publication bias in any list like this. Regulators tend to study markets where they already suspect a problem, and papers with clear findings are more likely to be published and cited.
• The numbers are not comparable across rows. They cover different markets, countries, years and currencies, and use different methods (firm pricing data, surveys, structural models, mystery shopping). We never convert currencies, rebase to a common year, or add figures together.
• Magnitudes carry their source's own caveats. Several are averages, maximums ("up to"), or aggregate estimates that may double-count customers across markets; the CSV records each caveat per row.
• The review is a snapshot compiled on the date shown. Regulators update figures and introduce remedies over time.

Methodology

Search strategy. We searched the publication libraries of consumer regulators and competition authorities (Competition and Markets Authority, Financial Conduct Authority, Ofgem, Ofcom, Australian Competition and Consumer Commission, Australian Productivity Commission, Central Bank of Ireland, EIOPA, European Commission DG Justice and Consumers, OECD, US Federal Trade Commission) and academic indexes (DOI resolver, journal publisher pages, NBER and RePEc) for terms including "loyalty penalty", "price walking", "tenure-based pricing", "differential pricing", "standing offer", "default tariff", "switching costs", "consumer inertia", "status quo bias" and "personalised pricing".

Inclusion and exclusion. We included substantial primary sources: regulator market studies and policy statements, official consumer-body super-complaints, and peer-reviewed papers whose finding we could read in the open abstract or an accessible full text. We excluded news coverage, blogs, encyclopaedias, and any source we could not open to confirm the specific number.

Verification. Every row was opened and checked against the source text. For each row we recorded the exact URL read and a verbatim excerpt containing the reported figure or finding. We report magnitudes exactly as the source states them, with the source's own units and caveats, and we do not compute, convert or combine numbers. Where a figure appears in a working-paper version rather than the published abstract (Handel, LP21), we say so in the caveats.

Update and corrections policy. This is version 1.0, compiled on the date shown. If you spot an error, a broken link or a figure we have misreported, email coinandcortex@gmail.com and we will check it against the source and correct confirmed errors.

How to cite this page

@misc{coinandcortex_loyalty_penalty_evidence,
  author       = {{Coin and Cortex}},
  title        = {The Loyalty Penalty: What Regulators and Researchers Actually Found},
  howpublished = {Evidence review and dataset, version 1.0},
  year         = {2026},
  note         = {Compiled 27 October 2026},
  url          = {https://coinandcortex.com/research/loyalty-penalty-evidence/}
}

Related reading

• The loyalty penalty: why staying put costs you
• Personalized pricing: why you and your neighbour pay different prices
• The Fair Price Protocol: a routine for checking you pay a fair price