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You can read a P&L and see what's coming six months from now. But you have hundreds of locations and one of you. Domain Intelligence captures how you investigate your business and runs it across every location, every week, without you.
It's rarely a single cause. It's traffic softening while transaction value props up the number. It's inventory arriving late while assortment gaps open. It's a new store manager learning while shrink creeps. By the time someone asks why, the moment to act has already passed.
For retailers using Circana, Nielsen, or third-party traffic data, there is a second problem: the licensed data cannot leave the perimeter. Scoop deploys inside your AWS environment, so your existing data agreements stay intact and the AI works with the data you have already paid for.
When a store underperforms, there are usually 10 primary hypotheses: inventory, assortment, traffic, staffing, shrink, manager tenure, pricing, category mix, competitive activity, and seasonal shifts. It's rarely one. It's usually several at once, and the interactions between them are where the real answer hides. Nobody has time to untangle that for every location.
Someone asks why a region is underperforming. Your team pulls data, builds the story, gets it reviewed. By the time the answer lands, actions take weeks to flow through the organization. The window to intervene has closed.
Power BI, Tableau, your data warehouse — they all work. But they show single-source views. The real answers come from connecting sales data with traffic data, licensed market data, staffing patterns, and competitive activity. That integration is where everyone gets stuck.
There's one person in our organization who can look at these reports and see what's going to happen in six months. We have over a thousand locations. He can't get to all of them. We're trying to scale that person.
Our team sits with you and your best operators. We capture what you check first, what thresholds matter, what you'd escalate vs. ignore. That judgment gets encoded into investigation logic. Then the system runs it weekly, with no one in the loop.
Multiple lenses evaluate every location in parallel. Revenue imbalances, leading indicator decline, margin erosion, traffic gaps. Flags from angles your weekly review wouldn't cover.
Dozens of diagnostic probes per flagged location. The investigation adapts based on what it finds, the same way your best people actually diagnose problems.
Every location that passed screening gets a second look. In production, this caught developing issues in 22 of 24 that initially appeared healthy.
Findings become written analysis at every level of your org. Executive-ready documents land in your inbox. 48 reports from a single cycle in the current deployment.
Patterns from a live retail deployment. The kind of findings that surface when you systematically investigate every location instead of waiting for someone to ask.
Revenue on plan. Customer frequency declining. Traffic shifting. The headline is held up by a few strong categories while the foundation erodes. DI checks every location against the leading indicators you'd check yourself, before anyone feels urgency.
Operational decisions drive customers away. Traffic drops. Inventory ages. Margins erode from markdowns. You can trace this when you sit with the data. You can't watch for it across hundreds of locations. DI follows the full causal chain and tells you where to intervene.
ML tests every combination of dimensions: category, customer segment, transaction type, geography. In production, it found customer loyalty tier was the strongest predictor of YoY change across multiple regions. Not on any dashboard. Changed the entire triage.
A category decline that looks alarming is actually division-wide. A month-over-month drop is normal seasonality. A flagged location has already inflected. DI applies the same contextual judgment you would: benchmarks, seasonality, peer comparisons, trend direction.
Monday morning. You open the report and know exactly where to focus the week.
"Systemic issue across both divisions. Here's the pattern and the triage framework."
"40% of locations flagged. The region will feel this in 3 to 6 months if it's not corrected now."
"4 of 12 locations need attention. This one needs same-week intervention."
"Here's what's happening, why, and exactly what to do this week."
Domain Intelligence is in production for multi-location retail. We'll show you what a weekly cycle looks like on your data.
