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In the UK, supermarket prices are measured using both direct price collection and aggregated retailer sales data. The Office for National Statistics (ONS) is introducing grocery scanner data alongside prices collected in stores and online. Those methods capture different things: a shelf check records a price at a particular time, while transaction data can show the average price shoppers paid across many purchases.
What grocery price surveys measure
A price survey gathers observations that help statisticians estimate how prices change over time. The observations are not, by themselves, the published inflation rate. Statisticians select and combine prices and quantities using an index method designed to represent spending across products and outlets.
The UK example below describes ONS methods and figures available through January 2026. It is not a universal description: statistical agencies in other countries use different mixes of sampled prices and transaction data.
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ONS uses two broad approaches during its transition. Price collectors continue to record prices in stores and online for parts of the grocery market. For other parts, retailers provide aggregated scanner records derived from sales. A record can summarize a product’s expenditure and quantity sold at a particular outlet over a period, often a week; it does not contain personal customer records.
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ONS’s 28 January 2026 overview said scanner data initially covered around 50% of the UK grocery market. It contrasted approximately 300 million scanner-derived price points, based on sales of over a billion units per month, with about 25,000 prices collected directly each month. Manual collection continues for the remaining market. These are ONS figures for the UK rollout, not a fixed scale used by every country or agency. See ONS’s January 2026 overview.
ONS had also described its regular scanner feeds in its 2025 methodology as representing over a billion units sold per month and about half of the grocery market. These dated descriptions indicate the scale of the UK transition, not a claim that every product, retailer, or transaction is covered. ONS’s 2025 methodology explains the approach.
How a scanner record becomes an average price
For a product, a basic transaction-based unit value is its observed expenditure divided by the quantity sold. If some shoppers buy it at full price and others use a promotion, the result reflects their average paid price over the data period. It is not necessarily a price any one shopper saw on a shelf.
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Pack sizes also matter. ONS adjusts for product size so that comparisons can be made on a common basis, such as price per gram, rather than assuming that every pack or can contains the same amount. Its method considers weight type, distinguishes supermarket from convenience-store observations, and includes processes for relaunches that may change recipe, packaging, quality, or quantity.
Why the collection period matters
ONS uses the first three full weeks of each month for grocery scanner data. Weekly records that cross a month boundary cannot be assigned cleanly, so using full weeks gives retailers a consistent monthly definition and allows time to scrutinize the data. This schedule is specific to ONS’s grocery method; it should not be assumed for other agencies.
How prices become an inflation measure
Statisticians aggregate price changes using an index, rather than taking a simple average of all observed shelf or transaction prices. ONS’s scanner-data method uses a GEKS-Törnqvist multilateral index over a 25-month window, with a mean splice for the published extension. In plain language, the multilateral approach compares products across overlapping periods and can accommodate products entering or leaving the market, reducing the need to make a manual replacement decision for every disappearing or new item. ONS explains the approach in its article on multilateral index methods and its documentation on alternative price aggregates.
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This distinction matters: a change in the average price level is not the same as the inflation rate, which measures how prices change between periods. The index weights and comparison method help determine how individual observations contribute to that rate.
Do loyalty discounts and multi-buy offers count?
They can be reflected in scanner-based average prices when the retailer data include those transactions. ONS says its traditional local collection can capture discounts when the collector knows the price paid, but it has not generally included loyalty-card or multi-buy offers when the proportion of shoppers paying each price is unknown. Aggregated sales data can show the average transaction price across purchases, including those offers.
Including more discounted transactions does not automatically lower measured inflation. The effect depends on how those transaction prices change relative to other prices, and scanner results also differ from local collection in timing, store coverage, and the breadth of product observations. These factors can affect results together, so a difference cannot necessarily be attributed to discount treatment alone.
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How other statistical agencies collect grocery prices
The UK approach is one design among several. In the United States, the Bureau of Labor Statistics (BLS) describes a sampled-item approach: it selects geographic areas, outlets based on where households shop, and representative items within categories. For example, a particular apple variety and grade may represent apples at a supermarket. Price changes are weighted by items’ importance in consumer spending; food-at-home prices are collected monthly, and specialists review changes that may reflect size or quality. This is a comparison with ONS, not a description of UK practice. See the BLS Consumer Price Index FAQ.
Australia’s Bureau of Statistics says supermarket scanner data are among the sources used for grocery prices. Its methodology describes monthly collection scheduling and weekly transaction data, but this does not mean scanner feeds capture every Australian grocery price. See the ABS Consumer Price Index, Australia methodology, May 2026.
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| Feature | Direct price collection | Retailer scanner data |
|---|---|---|
| What is observed | A recorded price for a sampled product at a collection point | Aggregated expenditure and quantity sold by product, outlet, and period |
| Price meaning | A price observed at a particular time; known discounts may be recorded | An average paid price across recorded transactions, potentially including loyalty and multi-buy offers |
| Coverage and scale | Sampled outlets and items; ONS reported about 25,000 directly collected prices per month in its January 2026 overview | Retailer feeds can cover many sales; ONS reported about 300 million scanner-derived price points and over a billion units sold per month in that same overview |
| Pack and product changes | Changes in size or quality require review and comparable-item treatment; BLS describes such review for its US system | ONS adjusts for size and has processes for product relaunches, including changes in recipe, packaging, quality, or quantity |
| Collection timing | Depends on the agency’s sampling schedule; BLS says US food-at-home prices are collected monthly | ONS uses the first three full weeks of each month for grocery data |
| Index treatment | Varies by agency; BLS describes weighting by spending importance | ONS uses GEKS-Törnqvist over a 25-month window, with a mean splice for the published extension |
The comparison reflects the methods described by ONS, BLS, and ABS in the linked documentation. The numbers in the ONS coverage row are UK figures from January 2026, not directly comparable counts of equivalent observations across countries.
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What the data do not settle on their own
Scanner data broaden the number of transactions represented, but their results depend on which retailers supply data, which products and outlets are represented, the time period covered, and how products are matched as they change. ONS identifies coverage, timing, discounts, and index method as factors that can jointly distinguish scanner estimates from locally collected prices.
Refund records are not consistent across scanner suppliers. ONS says refunds are a very small share of grocery expenditure and expects their effect on the index to be negligible, while noting that returned goods are outside the conceptual scope of a consumer price index. This is a qualification about data handling, not evidence that every supplier records refunds in the same way.
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