Run every store with the local context it deserves

Factori helps retail brands understand the real world around every store—who lives there, how people move, what they spend, and what’s changing—so you can make better decisions on forecasts, inventory, labor, sites, and local marketing.

Trusted by

Trusted by

Modern retailers operate with real world intel

QSR is won and lost on small margins and short windows: the lunch rush, the game night, the storm, the new competitor across the street.

Most retail decisions are still built on POS history and simple store clusters. That breaks when:

Neighborhoods change faster than your models

Events, weather, and local economics hit some stores but not others

Competition and new formats show up around specific locations

Factori adds the outside‑in view your systems don’t have, so you can:

Improve store‑ level demand and labor forecasts

Cut stockouts and overstocks in the same network

Open fewer weak stores and more strong ones

Plan promos and media around where demand is actually building

Give your retail P&L a boost

Store‑level demand & sales plans

Make daily and weekly plans that reflect real footfall, events, and local conditions.

Inventory & replenishment

Set stock and safety levels based on each store’s true demand pattern and catchment, not just chain‑wide rules.

Labor & scheduling

Align staffing to when people actually shop—by daypart, day of week, season, and event.

Site selection & formats

Choose new locations and formats with a full view of audience, economics, and competition.

Local marketing & retail media

Aim offers, OOH, and CTV at the neighborhoods that match your best customers and categories.

Real world data made for retail teams

You bring POS, e‑com, loyalty, and supply chain data. Factori brings the real‑world layer.

All datasets are aggregated, documented, and built to be simple to understand and join to your store list.

Mobility

Mobility

How many people are around each store, when they come, and how patterns are changing.

Places

Places

Clean details on nearby stores, shopping centers, venues, and services.

Business

Business

Competitive density and complementary brands around each location.

People

People

Aggregated view of local households and workers: age mix, income bands, lifestyle indicators.

Events

Events

Sports, concerts, festivals, school breaks, holidays—what drives local spikes and dips.

Retail

Retail

Category and brand spend by market—where retail spending is growing or softening.

Market

Market

Search and commerce interest for your categories and brands across regions.

Property

Property

How neighborhoods are built (urban, suburban, mixed‑use) and how they’re changing.

Driving change across retail functions

Better forecasts and inventory

Stronger site and format decisions

Higher‑impact marketing and retail media

Clearer performance reviews

Key questions you can now answer

Where are we consistently over‑ or under‑staffed versus real traffic?

Where should we open the next wave of stores or new formats?

Why did these two similar stores diverge so much over the past year?

Which stores should get deeper inventory for this category next season?

In which trade areas are we losing share to competition, not just “the market”?

Best retail data teams 
use real world data

Heads of Retail & Store Operations

who need error down and trust up.

Looking for clean, reliable visit data to plug into models and dashboards.

Looking for clean, reliable visit data to plug into models and dashboards.

Looking for clean, reliable visit data to plug into models and dashboards.

Looking for clean, reliable visit data to plug into models and dashboards.

Looking for clean, reliable visit data to plug into models and dashboards.

Pick a focus area

For example: forecast & inventory accuracy, underperforming regions, or upcoming store openings.

Choose a starter bundle

Common starting point for retail: Mobility + People + Events + Business + Retail Sales, then add Market or Economic as needed.

Run a side‑by‑side review

Compare how you plan and evaluate stores today vs. how decisions look with real‑world data, and decide where to scale first.