Customer Case Studies

Commerce intelligence. Measured in outcomes.

See how Path Analytics helped two retailers close performance gaps with week-by-week corrective actions, clear guidance on what to do and how, and incremental progress tracking toward better sell-through and sales.

$7.8MCross-channel revenue drift identifiedRead the revenue recovery story →
16%Sales lift across 64 storesRead the inventory placement story →

From real engagements. Customer names withheld under NDA.

01 / Revenue Leakage Prevention

The channel looked steady. The category told a different story.

California-based omnichannel retailer
Amazon · eBay · Direct-to-consumer

$7.8M
Revenue drift over three months
6 weeks
To return sell-through above target

The situation

A retailer selling across Amazon, eBay, and its own direct-to-consumer channel saw one channel hold steady while the same category quietly lost sell-through in another. The drift continued for three months, costing $7.8M in revenue before it became visible in reporting.

Looking at each channel on its own left the team without a clear view of where category performance was falling short.

What Path Analytics found

Path Analytics compared category performance across channels against expected performance. That comparison exposed the gap that the historical trend had not surfaced: stable results in one channel were sitting alongside missed demand in another.

What changed

Path Analytics mapped week-by-week corrective actions for the channel and category gap, with clear guidance on what needed to be done and how to do it. Teams tracked incremental progress week over week to see whether those actions were improving sell-through.

The outcome

Sell-through returned above target within six weeks of corrective action.

The $7.8M figure describes the revenue drift identified over the preceding three months. The six-week result measures sell-through recovery.

02 / Inventory Placement

Move inventory to where demand is converting.

64-store retailer with a strong D2C business
Central distribution center in Texas

64 stores
Demand evaluated store by store
16%
Sales lift over the following quarter

The situation

The retailer held inventory centrally at its Texas distribution center and released it on a planning cycle. Store allocations followed what the plan expected, while actual demand varied across its 64-store network.

The operating question was where available stock could convert into sales now.

What Path Analytics found

Path Analytics read demand store by store in real time, identifying where stock was actually converting. That gave the retailer a current view of demand for inventory placement across the store network.

What changed

The retailer used Path Analytics to route distribution-center stock toward stores where demand was converting. Inventory decisions reflected observed store performance rather than relying solely on the scheduled allocation plan.

The outcome

Sales across the 64 stores lifted 16% over the following quarter after stock was routed to demand.

Daily Use in Customer Operations

Part of the operating day.

Adoption reaches from the teams closest to the work to the executives reviewing business performance.

Usage reflects customer operations; it is not a separate measurement for each case study.

10–15×a day — store and floor teams open Path Analytics.
2–3×daily — executives check in on business performance.
LiveDemand-driven inventory for the warehouse team.
How Path Analytics Works

Connected data. Clear intent. Tracked actions.

Path Analytics brings multi-channel data into shared business context, so operators can understand a gap, decide what to change, and track the result.

01
Unify commerce data
02
Understand the question
03
Explain the gap
04
Act and measure
Explore Connected Intelligence →
See Path Analytics in Your Business

Start with the performance gap
you need to close.

Explore revenue leakage, sell-through, or inventory placement across your stores and channels.

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