Crisp vs. SPINS vs. Both: How to Think About Your Analytics Stack

If you're running a natural or better-for-you CPG brand doing $5M–$50M, you've probably had the SPINS conversation and the Crisp conversation within the same quarter. Maybe the same week.

Both platforms promise better visibility into your retail performance. Both have credible customers and reasonable pricing at the mid-market level. And the Venn diagram of what they do has just enough overlap to be confusing.

Having evaluated both platforms multiple times — at Navitas, at Mezzetta, and now with NWTN AI clients — here's how I think about the comparison.

What SPINS actually does

SPINS is syndicated data. They aggregate point-of-sale data from retailers in the natural/specialty and conventional channels and sell it back as market intelligence.

What you get:

• Category-level and brand-level sales data across participating retailers
• Market share, velocity, ACV (All Commodity Volume) metrics
• Competitive benchmarking — how your brand performs against the category and specific competitors
• Trend data over time — typically 52-week rolling windows

What it's best for:

• Understanding your competitive position in a category
• Preparing for buyer meetings with data the buyer trusts (retailers use SPINS too)
• Tracking distribution gains and losses at the category level
• Annual business reviews and strategic planning

What it doesn't do:

• It doesn't show you your own operational data in real time
• Reporting cadence is typically monthly or bi-weekly, not daily
• It doesn't connect to your trade spend, your distributor data, or your internal metrics
• Granularity is limited by what retailers share and how SPINS categorizes it

What Crisp actually does

Crisp is a data platform that connects directly to distributor and retailer systems to give brands real-time (or near-real-time) visibility into their own sales and inventory data.

What you get:

• Direct sell-through data from distributors (KeHE, UNFI, DSD networks)
• Retailer-level data where available (depends on retailer participation)
• Inventory and fill rate visibility
• Alerts for stockouts, low inventory, and velocity changes

What it's best for:

• Day-to-day demand planning and supply chain decisions
• Catching stockouts before they become missed sales
• Understanding your own sell-through patterns at the SKU level
• Operational dashboards that the ops team actually uses daily

What it doesn't do:

• It doesn't provide competitive or category-level market data
• You can't see how competitors are performing at the same retailers
• It doesn't replace the syndicated data your buyers expect to see in line reviews
• Coverage depends on which distributors and retailers are in Crisp's network

Where they overlap — and where they don't

The overlap is smaller than most people assume. Both platforms show you sales data for your products at retail. But the data sources, update cadences, and use cases are fundamentally different.

SPINS answers: "How is my brand performing relative to the category and competitors?"

Crisp answers: "What's happening with my inventory and sell-through right now?"

One is strategic. The other is operational. Most brands at the $10M+ level need both. The question is whether they need both at the same time, or whether one can wait.

How to decide at each stage

$5M–$10M: You probably need SPINS for buyer meetings and line reviews. You may not need Crisp yet if your distributor relationships are manageable with portal exports. Invest in SPINS first.

$10M–$25M: This is where Crisp starts paying for itself. Your distributor data is getting complex enough that portal exports are eating hours. Your supply chain team needs real-time visibility. Add Crisp alongside SPINS.

$25M–$50M: You need both, and ideally they're connected. SPINS data flowing into the same analytics layer as Crisp data, alongside your trade promo tracker, gives you the full picture: market position + operational performance + trade spend ROI in one view.

$50M+: At this point, you're likely evaluating enterprise-tier options across both categories, and the integration question becomes even more critical. Bedrock Analytics enters the conversation for deeper analytics, and the build-vs-buy decision gets more nuanced.

The integration layer

The real unlock isn't choosing one platform over the other. It's connecting them.

When SPINS category data feeds into the same system as Crisp sell-through data, you can answer questions that neither platform handles alone:

• "Category is growing 8% but my brand is growing 3% — is that a distribution gap or a velocity problem?" (Requires SPINS category data + Crisp SKU-level sell-through)
• "We gained 15 new doors last quarter — what's the incremental revenue impact?" (Requires SPINS ACV data + Crisp fill-rate and sell-through)
• "Our trade promo delivered 2.3x lift — how does that compare to category promo lift?" (Requires your promo data + SPINS category promo analysis + Crisp actual sell-through)

These cross-platform analyses are where the strategic value lives. And they're exactly the kind of integration that falls through the cracks when each platform lives in its own silo.

Evaluating your analytics stack? NWTN AI helps CPG brands choose, implement, and connect the right platforms for their stage and budget. Book a strategy call and we'll walk through what fits.