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Network Performance2026-06-26

A 12-store network, managed in real time

A head of retail ran 12 stores on monthly reports with a 3-week lag. Solya turned that retrospective cadence into one live dashboard she opens every morning.

Outcome

Monthly → live

Network

12 stores · lifestyle apparel

Measured outcomes

Monthly → live

Network performance cadence

+11 pts

Reaction speed on drifting stores

Days

From signal to corrective action (was weeks)

What's wired up

Systems connected

MERCH

Merchandising system

POS · 12 stores · live

ECOM

E-commerce platform

Online sales & conversion

STORE

Store dashboards

Manager feedback loop

Before · After

Before

Running on yesterday's news

The head of retail inherited a monthly review cadence with a 3-week analysis lag. When a store's traffic dropped or a key category underperformed, she learned three to four weeks late — by which point the lever to act had already closed.

After

One live dashboard, every morning

Solya consolidates POS, e-commerce and stock data in real time. Each store is scored against its own tier benchmark, and a drifting store raises a color-coded signal with context and a recommended action. She acts in days, not weeks.

The challenge

It was 2024. The head of retail had inherited a reporting cadence from the company's smaller days: monthly store reviews with a three-week analysis lag. Sell-through, margin, stockouts, conversion — all aggregated, all retrospective.

When one store's traffic dropped or another underperformed on a key category, she learned three to four weeks late. By then the season had moved on and the lever to act had closed.

The monthly report was built by a finance analyst over three weeks. It covered every store on every dimension, and it arrived too late to be operational.

Drifting stores stayed drifting until the next monthly cycle. Key-category stockouts were handled reactively. She ran a 12-store network on yesterday's news, managing on intuition and store visits rather than data.

What we changed

Solya consolidates POS, e-commerce and stock data in real time through its data layer. Every morning the head of retail opens one live dashboard.

It shows each store's sell-through, margin, conversion and stockout status — but evaluated against that store's own tier benchmark, not the network average. A small regional store is no longer judged against the flagship.

How decisions get made

When a store drifts — sell-through 5 points below its tier benchmark, margin compressing, conversion dropping — the intelligence layer raises a color-coded drift signal. It comes with a confidence level, historical context, and a recommended action.

She acts in days, not weeks. Solya tracks each corrective action and its impact, learning what works per store rather than per network.

Where it lands

The signals and feedback land in the application layer: one dashboard for the head of retail, and precise, contextual feedback for each store manager instead of a generic monthly recap.

The monthly report still gets produced — it just stops being where decisions happen. After six months, the network started behaving like a network.

What changed

  • Network performance moved from a monthly cadence to live
  • +11 points of reaction speed on stores that start to drift
  • Signal-to-action measured in days, not weeks, every store on its own tier benchmark

Related: see how network-aware allocation sets each store up before the season, or how continuous replenishment acts on the same live signal.

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