Private Label Grocery Trends, Measured

Private label grocery trends come down to two measurements per category, tracked over time: what share of units the store's own brand takes, and how far its shelf price sits below the national brand it competes with. Share tells you where the shopper landed. The gap tells you why, and whether it will hold.

The definition of the term lives on private label. This page is the measured version: share and price gap by category in a named store set, which categories the gap is widening in, and the four ways this analysis produces a confident wrong answer.

Key takeaways

• Measure share in units first. Store brands price lower, so they win unit share before dollar share by construction, and a dollar-share read lags the actual shift.
• Track the gap, not the price. A store brand that held its price while the national brand cut has lost ground even though nothing on its own tag changed.
• Run the gap on everyday and promoted price separately. In heavily promoted sets the promoted gap is the one the shopper actually faces.
• Equivalize before comparing. A 5 oz store-brand bag against an 11 oz national-brand bag produces a gap that is mostly packaging.
• Any share number without a store set behind it is unverifiable. If you publish a cut, name the stores.

The published private label grocery trends, and their period

The industry-level figures below come from PLMA's release on Circana Unify+ data for calendar 2025, and they are the cleanest public numbers on the category. Quote them with the period attached.

One consequence of that share pair is worth doing the arithmetic on, because it is the price gap showing up at national scale. Unit share, 23.5%, runs 2.2 points above dollar share, 21.3%, which means the average store brand unit rings lower than the average national brand unit. Index it: store brand dollars per unit come to 0.906 while national brands come to 1.029, so the store brand's average ring sits about 12% below.

Treat that 12% as a blended artifact rather than a shelf gap. The two sets are not distributed the same way across the store, private label is heaviest in categories with low absolute prices, and a category-level read is the only one anyone can act on. Which is the rest of this page.

The measured read: unit share by category

The worked example below covers 46 full-format supermarkets operated by four independent grocers, 52 weeks against the prior 52, computed from scanned units in that set. It is not a projection, not a regional panel, and not weighted to anything. Naming the store set is the whole point: a private label share figure without one is a number nobody can check, and most of the ones in circulation do not have one.

Two categories went backwards, and they are the two most heavily promoted sets in the store. Hold that thought, because the price table explains it.

Where the price gap is widening

Same store set, same periods, everyday shelf price per equivalized unit. The gap is how far the store brand sits below the leading national brand in the same set, so a larger negative number means a cheaper store brand.

Sort the eight categories by direction rather than by rank and they agree: every category whose gap widened gained unit share, and both categories that lost share are ones whose gap narrowed. Eggs widened most, at 3.6 points, and gained most, at 4.2 points of unit share. Carbonated soft drinks narrowed most, at 3.8 points, and lost share. Salty snacks narrowed 2.7 points and lost share. The ordering inside each direction does not track, though: frozen entrees widened 2.5 points for 0.8 of share while milk widened 1.8 for 2.3. Read the sign, not the rank.

The exception is bagged salad, where the gap narrowed 1.5 points and share still rose 0.8. That one is not a price story: the operator added two own-brand items to the set, from 4 per store to 6, so the share moved on facings. Which is the reminder that share can be bought with shelf space and that a price read alone will misattribute it. Check items per store before crediting anything to price.

The mechanism behind the two losing categories is the one most private label reporting misses. Carbonated soft drinks and salty snacks carry the heaviest promotional calendars in the store. When the national brand runs a multi-buy, the gap the shopper faces at the shelf is the promoted gap, not the everyday gap in the table above. An everyday-price read on those two categories would have shown a store brand holding its position in a week it was being beaten by a $2 price cut. See discount pricing for how the deal types differ, and run share separately for promoted and non-promoted weeks.

The categories where the gap widened have the opposite structure. Milk, eggs and canned vegetables are commodity-input categories with little promotion, where the national brand's price follows its cost and the store brand's follows a different supply agreement. In a period when the national brand's input cost moved faster, the gap opened without either party running a promotion, and unit share followed within a quarter.

Four ways this read goes wrong

• Comparing prices without equivalizing. Store brands frequently sell a different pack size to the national brand they sit next to, and comparing tag to tag builds the packaging difference into the gap. Normalise to a common unit first. See equivalized volume and case pack.
• Mixing the everyday gap and the promoted gap. Both are real and they answer different questions. The everyday gap sets the shopper's price image for the store brand; the promoted gap decides individual weeks. Reporting one blended number gets you a figure that predicts neither.
• Reading share on dollars when the question is demand. Because private label prices below, its unit share always leads its dollar share, so a dollar-share series reports the shift late and understates it. Use dollars only when the question is actually about revenue or margin mix.
• Averaging across categories. A store-level private label share is a mix artifact: it moves when milk has a big week, with no change in shopper behaviour anywhere. Every conclusion in this analysis belongs at category level.

Why these trends carry no year and no region

Two modifiers are missing on purpose. There is no year in the title, because a share read written to a year needs rewriting every year while the method behind it does not change. Read the PLMA figures above as what they are, a dated citation inside an undated method.

And there is no region, because the numbers come from four operators in one data feed rather than from a geography. A regional private label claim needs the store list behind it, and a set of 46 stores is a set of 46 stores, not a market. If you build a regional cut, publish the store count, the formats and the period alongside it, or expect the first buyer who reads it to ask a question you cannot answer.

How to run this in your own data

• Define the competitive set per category first: the store brand item, the leading national brand item, and the pack sizes each actually sells. The set is the analysis; everything after it is arithmetic.
• Compute unit share and dollar share side by side. The spread between them is your own version of the 12% national figure above, at a level you can act on.
• Compute the gap on everyday price, then again on realised price including promotion, and chart both against share.
• Hold items per store in the frame. If store brand items grew, part of the share gain is a shelf decision rather than a shopper one.
• Look at what happens after the gap moves, not just alongside it. In this set share responded within a quarter, which is the lag worth testing in yours.

Where Scout fits

Scout reads store-level point-of-sale and holds unit share, everyday price, promoted price and item counts in the same model, so both tables above are cuts rather than a quarterly project, and the gap can be charted against share by category over any window.

The boundary worth stating: Scout reads transactions, not people. It will tell you which categories moved units to the store brand, at what gap, and in which weeks. It will not tell you which shoppers switched or why they say they did, because that needs loyalty-linked demographics or a survey, and a POS file has neither.

Related: private label, category pricing and price architecture, and the retail price index.