What is SPINS data?

SPINS syndicated data — the definition

SPINS is a syndicated retail data provider focused on the natural, specialty, and wellness CPG channel. Founded in 1995 and headquartered in Chicago, SPINS aggregates point-of-sale and distributor-flow data from thousands of retailers and sells the resulting reports, dashboards, and data feeds to CPG brands, retailers, and investors.

If you're a brand-side analyst at a natural-products or specialty CPG brand, SPINS is almost certainly the data your sales and category teams are looking at every Monday morning. It's the dominant data source in the natural channel, the way Circana (the August-2022 merger of IRI and NPD) and NielsenIQ are dominant in conventional MULO grocery.

What SPINS natural channel data tracks

Three primary data streams:

1. Direct retailer scan data. Point-of-sale transactions from natural and specialty chains that license their POS to SPINS — Sprouts, Natural Grocers, and a long list of regional naturals and specialty chains. One notable absence: Whole Foods Market doesn't report scanner data to SPINS. Whole Foods coverage in brand-side analysis comes from Circana (which carries Whole Foods as part of conventional grocery) or from NielsenIQ panel-based projections.

2. Distributor-flow data. What KeHE and UNFI shipped to retailers in their networks. This covers the long tail of independent natural retailers that don't license their POS individually — single-store naturals, regional co-ops, specialty grocers. Distributor-flow data measures what's-shipped, not what's-sold, so there's a lag and a reconciliation step before it becomes a comparable sales number.

3. Product attribution. SPINS maintains a deep proprietary attribute layer over UPCs — natural/organic certifications, ingredient classifications, claims (gluten-free, plant-based, keto, non-GMO), and functional benefits. This is what lets analysts cut sales by "plant-based snacks" or "non-GMO supplements" rather than by broad category codes. The attribution layer is a moat — no other syndicator has comparable depth on natural/wellness/specialty attributes.

Two secondary capabilities worth knowing about:

• Conventional MULO coverage layered alongside SPINS' natural data via a partnership with Circana, branded as MULO+ when natural channel coverage and Conventional Multi-Outlet are stitched together into one read.
• E-commerce and digital data through optional add-ons.

What SPINS is good at

The natural and specialty channel — full stop. If you're trying to understand:

• How a wellness brand performs across Sprouts, Natural Grocers, and the independent natural co-ops
• Whether a new SKU is gaining distribution in the Natural channel before it expands to Conventional
• Category trends in segments where natural/specialty leads conventional (functional beverages, plant-based, supplements, clean-label personal care)

…SPINS is the right tool. The product attribution layer in particular is hard to replicate — when a brand needs to track "share of the plant-based protein bar segment with non-GMO certification," SPINS is the data that lets that segment exist as a discrete cut.

A worked example: reading a weekly SPINS report

A natural snack brand with roughly $4.2M in annual SPINS-measured revenue gets a weekly data extract every Monday morning. Here's what a typical week's analysis looks like and the questions each number raises.

The analyst's read:

Total Natural Channel is up ~6% week-over-week. ACV held flat at 47%, so no new doors opened — existing stores sold more. This is a velocity lift, not a distribution gain. The question is why.

Sprouts outpaced the field. Sprouts is up 10% week-over-week while Natural Grocers is slightly down. That asymmetry warrants a follow-up: is there an in-store execution change at Sprouts, a secondary display, a promo that hit this week? If the lift persists two more weeks without an obvious cause, it's worth a conversation with the Sprouts sales lead.

Independent natural is up 13%. KeHE/UNFI distributor-flow numbers move more than direct-scan channels — a 13% week-over-week bump here could be a genuine velocity lift or a restocking order from a co-op that ran lean the prior week. The four-week trend matters more than any single week.

ACV at 47% is climbing slowly. The 52-week average is 45%, so the brand is adding doors over time. That's a distribution story, not a velocity story — worth tracking separately from the week-over-week performance reads.

This is the weekly cadence: velocity versus ACV, retailer-level asymmetries, and anything outside the 52-week band without a clear explanation.

The SPINS portal vs. the data extract

Most brand teams interact with SPINS through two surfaces:

• The SPINS portal (browser-based) for ad-hoc reporting, custom category definitions, and on-demand pulls. Good for exploration and for answering specific questions mid-analysis — "what's the plant-based protein bar segment doing at Natural Grocers this quarter" is a portal question.
• The weekly data extract (CSV or Excel), a scheduled delivery of the standard report template. This is the Monday-morning cadence feed that powers the category team's dashboard. It's faster to work with once templated but breaks if the category definition changes — historical comparison requires a stable reporting structure.

Most mature analytics teams run the extract for trend reporting and the portal for investigation. The extract is the production asset; the portal is the diagnostic tool.

Where SPINS is the wrong tool

A few common mismatches:

• Pure conventional analysis without natural exposure. If a brand only sells in MULO grocery and never touches natural, Circana or NielsenIQ are the primary sources. SPINS' MULO+ coverage is real but isn't the strongest fit when natural isn't part of the story.
• Whole Foods–dominant brands. Because Whole Foods doesn't report to SPINS directly, brands whose business runs heavily through Whole Foods need to layer in Circana coverage or panel-based projections to see Whole Foods performance accurately.
• Real-time data. SPINS reports on a weekly cadence with a multi-week lag. For yesterday's velocity, the retailer-direct portals (Walmart Luminate, 84.51° Stratum for Kroger, Target's POL) are faster.
• Non-CPG categories. SPINS is CPG-specific. Adjacent categories (HBA at the conventional drug-channel level, GM, soft goods) are not the focus.

Where SPINS gets misused

Reading it as direct measurement. SPINS is a projected estimate built on a sample — the projection rules and suppression thresholds are real, and reading around them is a skill. A zero in the independent-natural column often means data suppression, not zero sales. See Reading SPINS panel coverage for the mechanics.

Treating attribute codes as stable across years. Attribute definitions get refined; SKUs get reclassified. A "plant-based" cut in 2023 may not be the same set of SKUs as a "plant-based" cut in 2025 because SPINS updated the certification criteria for one or more attributes. Always confirm the attribute version when comparing segment definitions more than a year apart. For a brand that defines its competitive set by attribute (e.g., "non-GMO functional beverage"), this can shift the apparent market size by 5–15% without any actual change in sales.

Using category-level ACV as a distribution benchmark without checking channel. A brand that reports "we're at 62% ACV" without naming the channel denominator is reporting a number nobody can reproduce. MULO ACV and Natural Channel ACV are computed against different denominators — the number is meaningless without the channel label. See What is ACV? for the full picture.

What to ask your SPINS rep in the first 90 days

Most brands underuse SPINS in the first contract year because they don't know what to ask. Three questions worth raising early:

"What are the suppression thresholds for my category at each of my key retailers?" Suppression rules are retailer-specific. SPINS reps can map exactly which cells will show blanks or zeros before you start reading them as real data. Getting this mapping up front saves you from presenting a false zero to a category director.

"Which of our SKUs carry more than one product-type attribution?" SPINS attributes can overlap — a product might live in both "granola" and "snack bar" depending on the pull. Knowing your SKUs' attribute assignments is a prerequisite to defining a defensible competitive set.

"How does the KeHE/UNFI distributor-flow data lag compared to direct scan at our key retailers?" The answer varies by region and reporting contract. A 2–4 week lag is common on distributor-flow; knowing the typical lag lets you interpret velocity spikes correctly rather than celebrating what turns out to be a backfill.

Doing this in Scout

Scout takes the SPINS extracts your team already pulls and turns them into shared, queryable dashboards — the natural-channel cuts, the MULO+ totals, and the product-attribute layers all sit in one analytical surface. The flow is customer-uploaded extracts (CSV) on a weekly cadence rather than a direct API feed, which means the data freshness matches what your team already pulls from the SPINS portal. For the weekly-read pattern in the worked example above, Scout's shared dashboard means the Monday-morning analysis is visible to the sales lead and the CEO, not just on the analyst's laptop in a pivot table nobody else can open.

Summary + further reading

• SPINS is the natural/specialty/wellness CPG data leader, with three primary streams: direct retailer scan, distributor flow, and product attribution.
• Whole Foods doesn't report scanner data to SPINS — for Whole Foods coverage, brands rely on Circana or NielsenIQ panel projections.
• It's the right tool for natural-channel analysis and for any brand whose category cuts across natural and conventional via MULO+.
• Confirm the attribute version when comparing segment definitions across years — attribute codes change and can silently shift your competitive set.

Related: What is ACV? · Syndicated vs. panel data