New-to-brand measurement: what store-level data can show

New-to-brand is one of the most requested metrics in retail media, and store-level data can't measure it directly. A new to brand metric counts purchases by shoppers who hadn't bought the brand in a set period before the campaign. That requires knowing which shopper made each purchase, over time. Store-level data describes what sold in each store. It doesn't follow individual shoppers.

That's not a flaw. It's a design choice with privacy benefits. It does mean new-to-brand questions need different, clearly labeled measures.

What does new-to-brand usually mean?

In retail media built on loyalty programs or online accounts, new-to-brand typically means a purchase by a customer ID that hadn't bought the brand in a lookback window, often several months or a year. The metric shows how much of a campaign's sales came from new buyers versus existing ones.

It's useful because growing a brand usually requires new buyers. A campaign that only prompts existing customers to buy a little sooner looks different from one that brings in people who hadn't tried the brand.

But it depends on identifying shoppers across purchases. Without that, true new-to-brand can't be calculated.

Why does store-level data measure something different?

Because NRS data is aggregated at the store, UPC and transaction level. It shows what sold, where and when. It doesn't track individual shoppers or build profiles of them.

So store-level data can answer questions about stores and products. Did the brand sell in stores where it hadn't sold before? Did a new item gain sales in advertised stores compared with similar stores? It can't answer whether the people buying were new to the brand.

There's a benefit to this design. Because no individual shopper is tracked, the privacy questions that come with shopper-level matching don't arise in the same way. The trade-off is that some questions, including true new-to-brand, need a different kind of data to answer.

Being clear about this matters. Presenting a store-level measure as if it were a shopper-level one would overstate what the data shows.

What store-level proxies hold up?

A few measures get at related questions honestly.

New-store sales. The number of advertised stores where the brand or item recorded sales during the campaign after recording none in a prior period. This measures new sales in stores, not new buyers, and should be labeled that way.

New-item trial. For a newly launched item, sales in advertised stores versus comparable stores. Since the item is new, all its buyers are new to the item, though not necessarily to the brand.

Brand share of category. If the brand's share of category sales rises in advertised stores compared with similar stores, the brand gained relative to competitors. Some of that may be new buyers, some may be existing buyers buying more.

Store-level repeat patterns. For an item new to a set of stores, steady sales in the weeks after a campaign suggest demand is continuing, though the data can't say whether the same people are coming back. Reported as a store-level trend, it's a useful signal of whether early trial turned into ongoing sales.

Each is useful. None is new-to-brand in the shopper-level sense. Store-level data, like the data analyzed by NRS Insights, supports these comparisons.

How should these measures be reported?

With accurate names. Call a new-store measure a new-store measure. Don't relabel it new-to-brand.

With the comparison. Every proxy should be measured against similar stores that weren't advertised, over the same period.

With context. Explain what the measure can and can't show, in a sentence, alongside the number. The broader discussion of retail media measurement increasingly distinguishes store-level and shopper-level metrics, and readers benefit from knowing which one they're seeing.

To discuss which measures fit a campaign, contact the NRS Digital Media team, or visit the NRS Digital Media site.