SPINS vs. 84.51° Stratum vs. Circana for Kroger data

Why your choice of Kroger data source matters

A brand sells through Kroger and wants a clearer view of what's moving at the shelf and who's actually buying. When it comes to SPINS 84.51 Stratum Kroger coverage, there are three sources you can write a check for:

• SPINS is syndicated and third-party. It carries Kroger inside its MULO and MULO+ coverage.
• Circana is the big syndicator for conventional grocery, with deep MULO coverage. It estimates Whole Foods into its grocery total rather than receiving Whole Foods scan data.
• 84.51° Stratum is Kroger's own data and analytics platform, built by 84.51°, Kroger's wholly-owned data subsidiary, and sold since mid-2025 under 84.51°'s Kroger Precision Marketing division.

Each answers different questions, and none fully substitutes for the others. Buying all three is expensive. Buying the wrong one for the question in front of you is also expensive, just less obviously so. This page maps the question to the source.

SPINS, 84.51° Stratum, and Circana for Kroger data: what each source is

SPINS (syndicated, third-party)

SPINS measures Kroger sales as part of its conventional coverage, which it licenses from Circana. A total-store read is the baseline. How far down the banner hierarchy — Ralphs, King Soopers, Fred Meyer, Harris Teeter and the rest — you can actually read is a contract-scope question, and SPINS does not publish the answer, so settle it with your rep rather than planning a workflow around it.

The real strength is that Kroger sits in the same data model as every other retailer SPINS covers. Cross-retailer and cross-channel comparisons are native, so you can put Kroger next to Sprouts, Fresh Thyme and the rest of the natural channel in one report. Whole Foods is the exception, as always: it is not in SPINS, so a WFM read has to come from NielsenIQ and be reconciled alongside rather than dropped into the same cut. For a brand that spans natural and conventional, this is the best tool for category comparison and benchmark work. The SPINS product attribute layer, organic, plant-based, non-GMO, functional, applies to the Kroger data exactly the way it applies to natural-channel data.

What you don't get: buyer demographics, basket-level data, or loyalty-segment cuts. Banner-level detail depends on contract scope, and without it you only see the total Kroger aggregate. The cadence is weekly with a multi-week lag, and the conventional-channel Kroger data comes through the SPINS–Circana partnership rather than a direct Kroger relationship.

On cost: SPINS does not publish a rate card, and cost climbs with channel coverage and add-ons. Conventional and banner-level Kroger coverage are priced as extensions rather than included, so scope them explicitly in the quote.

Circana (syndicated, third-party)

Circana measures Kroger sales as part of its MULO and conventional grocery coverage. It has a direct data relationship with Kroger conventional, and it carries a Whole Foods estimate, which SPINS does not have at all. That combination makes Circana a reasonable single source when Kroger is the priority and a Whole Foods approximation will do; for an actual Whole Foods read, see NielsenIQ below. Standard cuts run brand × SKU × retailer × week, with banner-level and geo-level available under additional licensing.

Its strength is breadth. Circana has very wide conventional-grocery coverage, an estimated Whole Foods included, and it's a default for any pure-conventional MULO analysis. The history runs deep, the methodology is stable, and Circana Unify+ (the successor to the Liquid Data portal) has been the workhorse platform for conventional CPG work for years.

The weakness is the natural channel. Circana's natural-channel attribution is thin next to SPINS, and the attribute layer SPINS uses for natural products is either absent or much shallower here. There's no loyalty or household demographic data either. Circana does not publish pricing, so scope and channel coverage drive what you are quoted; budget from a scoped quote rather than a range.

When should you pick it over SPINS? Mainly when Whole Foods is a real part of the business. Circana folds an estimated Whole Foods into its grocery total and SPINS doesn't, so a brand doing $2M-plus a year in Whole Foods is walking around with a sizable blind spot on SPINS-only data.

84.51° Stratum (Kroger-direct)

Stratum measures loyalty-card-attached purchase behavior across Kroger and its family of banners. 84.51° describes it as drawing on transactions from one in every two US households, a loyalty-attached base far larger than anything a panel could recruit. The granularity goes all the way down to the household (anonymized, with demographic attributes), the basket (what else was in the cart), and the trip (the purchase occasion and trip type). That's a different kind of data than any syndicator produces.

Where it shines is the who. For demographic and household-segment attribution at Kroger, nothing else comes close. It shows basket behavior, what your buyer picks up alongside your product, and it tracks loyalty segments over time, so you can actually answer whether trial buyers are repeating and how often. Banner-level performance comes at a speed and granularity syndicated data can't touch. The platform spans Stratum's analytics dashboards, Data Direct raw feeds, and newer self-serve tools, Data Orders for ad-hoc custom pulls and the AI-driven weekly summary "Agent Monday" that launched in 2025.

The catch is that it's Kroger-only, so there's no cross-retailer comparison. The loyalty data only covers transactions where a loyalty card was scanned, which means cash and unlinked trips go missing, and that gap varies by store and category. Pricing is enterprise-tier, gated behind a vendor agreement, and the cost model folds in both platform fees and, for Kroger Precision Marketing (KPM) campaigns, media spend minimums. It is not a self-serve tool for a small brand without a broker or retail relationship manager to open the door.

Kroger data sources comparison: question → source

A worked example: post-promo analysis at Kroger

A protein bar brand runs a four-week temporary price reduction (TPR) at Kroger in Q3. Once it ends, the category director asks the two questions that always follow a promo: did it work, and did it hold?

SPINS and Circana answer "did it work," from the syndicated read:

The syndicated read says the promo lifted volume 85 to 90% during the four promo weeks, and post-promo, sales settled back within 5% of pre-promo levels. No long-term pantry loading paid for with a slow recovery. By that measure it was a clean, efficient promo.

84.51° Stratum answers "did it hold," and the loyalty data adds the part the syndicated read can't see. 38% of promo-period buyers were new to the brand at Kroger in the prior 26 weeks, so genuine trial buyers. Of those, 29% came back for a second purchase within 8 weeks of their first promo buy, which is a solid repeat rate given the category averages 22% for new buyers. The trial buyers who repeated skewed heavily toward King Soopers and Ralphs, the banners where household income and organic-buying behavior line up with the brand's core buyer. And 12% of promo-period buyers were lapsed brand buyers, people who'd bought 12-plus months ago but not recently, so the promo also clawed back some lapsed customers.

That's the story the syndicated read just can't tell: the promo worked in the right banners and converted 29% of trial buyers to repeat, above category average. That's the number you take to commercial leadership and to the Kroger buyer as proof the promo built durable value instead of a one-week volume spike.

What loyalty data sees that scan data cannot

The reason Stratum costs what it does is that loyalty data answers a class of question POS data structurally cannot. Scan data is anonymous by construction: it knows a unit sold in a store in a week. Loyalty data ties that unit to a household ID that persists across trips, and that one difference cascades.

The last two are the ones that change decisions. Scan data can tell you a promotion moved 12,000 incremental units. Only loyalty data can tell you whether those units went to lapsed buyers you wanted back, to new households, or to your own heaviest buyers who were going to purchase at full price. Same lift, three very different verdicts on whether to run it again.

Growth decomposition is the other durable use. A brand up 8% at Kroger is up because more households bought it, or because the same households bought more often, or because they bought more per trip. Penetration, frequency, and basket size. The fix for each is different, and a POS report cannot tell them apart because it never sees a household at all.

Three limits worth stating before anyone over-reads it. Loyalty coverage is not total: unidentified transactions, roughly the shopper who does not scan a card or enter a phone number, are excluded, so the loyalty universe is a large subset rather than the whole store. It is single-retailer by definition, so a household that buys you at Kroger and Sprouts looks like two unrelated people. And a household ID is not a person; a family shares one card, which is why demographic reads on shared-consumption categories are softer than they look.

84.51° Stratum: access and pricing realities

84.51° runs several data and media products under the Kroger Precision Marketing (KPM) umbrella. Four of them matter to a CPG brand analyst.

The Stratum analytics platform is the self-serve dashboard layer, covering household behavior, basket, demographics, and brand-level KPIs at Kroger. Access takes a vendor agreement with 84.51°, and the pricing is either a subscription fee or per-query, depending on your tier. Brands without a direct relationship often reach Stratum through a shopper-marketing agency or a broker's data services.

Data Orders are ad-hoc pulls for custom analyses the standard dashboard doesn't cover. They're the right move when a specific question, say "what demographic over-indexes on our brand in Q4 versus Q3?" needs a cut nobody pre-built. 84.51° does not publish pricing for them.

Data Direct is the raw-feed integration, for brands or agencies that want to run Kroger loyalty data through their own analytical stack. It needs technical setup and a higher-tier vendor agreement.

Agent Monday is 84.51°'s AI-powered weekly digest, launched in November 2025. It summarizes brand performance at Kroger automatically and is delivered to your inbox each Monday, powered by Stratum data rather than living inside the platform; it is available to Platinum Stratum subscribers. It hands you a pre-synthesized view of the key weekly metrics so nobody has to build the report by hand.

On pricing: 84.51° doesn't publish its numbers, and everything is negotiated through the vendor agreement. Our own rule of thumb, not 84.51°'s: the platform is hard to justify until Kroger is a material line in the business, and obvious once it is one of your largest accounts. Shopper-marketing spend through KPM, Kroger's media network, can offset some of the platform access cost inside the KPM program structure, and brands running meaningful KPM campaigns often get Stratum access bundled in.

A practical pattern

Most brands that take Kroger seriously end up running two of these three sources. They keep a syndicator, SPINS or Circana, or both if they span natural and conventional, as the always-on cross-retailer read: what's happening at Kroger against the backdrop of every other retailer. That's the weekly category and sales reporting surface. And they add 84.51° Stratum for the buyer-side strategic work, quarterly or campaign-driven, focused on who's buying and what else they buy. That's what feeds innovation, segmentation, and shopper marketing.

Run only a syndicator and you have no buyer attribution and no real-time read. Run only Stratum and you're Kroger-myopic, with no benchmark against the rest of the category.

For a brand growing into Kroger, the phasing usually goes like this. Early on, under $5M in Kroger, SPINS carries Kroger in the same report as every other retailer, Stratum isn't cost-justified yet, and the Kroger total read is your primary Kroger metric. In the growth stage, $5M to $15M, you scope banner-level Kroger with SPINS to pull King Soopers and Ralphs apart from the mainstream banners, and you start looking at Stratum access through a broker or agency for key campaigns. Once established, $15M-plus in Kroger, a direct Stratum vendor agreement pays for itself, SPINS or Circana stays on as the cross-retailer benchmark, and you may carry both syndicators if the brand spans natural and conventional channels.

Where the gaps are

Two gaps none of these three sources closes.

The first is cross-retailer buyer behavior. 84.51° Stratum tells you who's buying at Kroger, but it can't tell you whether that same household is also buying you at Whole Foods, Sprouts, or Costco. For that, you need a household panel like NielsenIQ Homescan or Numerator (see Syndicated vs. panel data). This matters whenever a brand is asking "is our Kroger buyer also buying us in the natural channel?" or "once we get authorized at Sprouts, do we cannibalize Kroger volume?" Panel data is the only clean answer to those.

The second is the gap between shelf execution and data-reported sales. None of these sources tells you whether your product was actually on the shelf when the shopper looked for it. Out-of-stocks at Kroger are real: a fast-moving SKU in a mid-size banner can sit OOS for days between replenishment cycles. Catching that needs shelf data, audits or image recognition, layered on top of the transaction data (see What is share of shelf?).

Doing this in Scout

Scout's main surface for Kroger is the SPINS extracts your team uploads weekly, the cross-retailer, cross-channel context for "how's Kroger doing relative to every other retailer we sell in." For 84.51° data, Scout takes customer-uploaded Stratum exports, including the Sherlock Matrix Export format, so brands paying for Stratum can layer the loyalty-attached insights onto the same dashboard as their syndicated reads. Direct API feeds with 84.51° aren't wired today, so the integration model is upload-driven. The point is one analytical surface for a Kroger read that holds both the syndicated category context and the loyalty-attached buyer behavior, instead of flipping between the Stratum portal and a SPINS spreadsheet.

Summary + further reading

• SPINS, Circana, and 84.51° Stratum each measure a different aspect of Kroger performance, and none substitutes for the others.
• SPINS fits cross-retailer comparison for a natural-plus-conventional brand. Circana fits conventional-only brands and any brand where Whole Foods matters. 84.51° Stratum fits buyer attribution, basket behavior, and post-promo analysis.
• Most Kroger-serious brands settle on a syndicator plus Stratum, scoped to different cadences and use cases. Around $10M-plus in Kroger revenue is the rough point where direct Stratum access pays for itself.
• Neither syndicated data nor Stratum covers cross-retailer buyer behavior or shelf execution. A household panel and shelf audit data fill those two gaps.

Related: Reading Kroger total-store performance in SPINS · SPINS vs. Circana vs. NielsenIQ