For retail suppliers
Most suppliers spend hours every week running reports, dumping data into Excel, and explaining what already happened.
TenX is the other thing: an operating system for your biggest retail account, not another report. It reads your retailer's data feeds on your cadence, ranks what is costing you money, names the setting to change and who owns it, where there is one, then tracks whether the number moved.
See a demoThirty minutes on a call, on a live cockpit for a fictional brand.
690 new stores granted. Roughly 680 have nothing flowing.
At the expansion
~680 granted, nothing flowing
July 4th week
After the push
Set up: traited and valid Selling
Typical week before
Before the expansion
Typical week after
Up almost 30%
Weekly store sales, one item
What broke: store flow. Replenishment was never activated, so the DC stock sat.
Push the warehouse cases out to the empty stores now. Switch replenishment on so it keeps ordering.
Illustrative alert with mock-up numbers. Weekly store sales; typical weeks leave out the July 4th week.
How it counts a dollar
Anyone can point at a chart and call it savings. Our attribution is gated, and it argues against itself. A comeback that predates tracking is excluded. A comeback the retailer drove on its own is credited to them. If the ask was flagged but never sent, no credit. What survives all three gates is the only number we put our name on.
Comebacks watched
380 store-item reactivationsExcluded, pre-tracking
History is never claimed.Credited to the retailer
215 combos. Their move, their credit.Left after the gates
Postdates tracking, not the retailer's doing. Credited only where the ask was actually sent.Illustrative mock-up numbers. $209K of $240K goes to history and to the retailer before we count a dollar, which is the whole point.
The refresh cycle
No dashboard to remember to open, no report to build. The work runs as soon as your data posts, and the answer is waiting for you. It is a browser tab: nothing to install, no software for IT to deploy.
WHEN DATA POSTS
Your retail files pull in and rebuild across every level we track, down to item and store.
THEN
Each one looks for a specific failure worth money: phantom inventory, stranded distribution, a DC that quit shipping.
YOUR MORNING
Ranked by dollars at stake, naming the owner and, where there is one, the setting to change, with the evidence attached so you can forward it without rebuilding the case.
AFTER
A comeback watcher checks whether the store actually started selling again, and banks only the dollars that followed an ask.
Alerts in action
The cockpit sorts every alert by the move you make: ship product that is stuck, fix the setting that is throttling it, grow back the distribution you lost. Each one names the owner, what is at stake, and the ask, already written. Three illustrative examples, one per lane.
Not flowing to store · a new-store grant sitting idle
690 new stores granted on one item; roughly 680 with nothing flowing. DC stock is in place. Store replenishment never activated, so every quiet week reads as a $0 on the item's scorecard.
The ask: push the warehouse cases out to the empty stores now, sized to what each store sells. Durable fix: switch replenishment on so the system orders by itself.
Conversion forecast gap · the successor keyed low
A pack change issued a new item number and the demand history did not follow: proven 24 units per store-week, keyed at 8, selling 9. The low sales will confirm the low forecast at the next review.
The ask: raise the store forecast to the predecessor's demonstrated rate before the review locks it in.
Lost distribution · stores that quietly stopped buying
A de-trait, a reset, a forgotten price cut. Stores that used to buy the item stopped, and the loss hides inside a healthy-looking total. Every store is listed and valued at its own trailing rate.
The ask: take the store list to the merchant and win back the trait or the reset. The comeback watcher then tracks which stores start selling again.
The push on the first alert landed: 760 stores selling by the July 4th week, up from 220, and a typical week went from $48K to $62K. After the ask on the second one, the forecast came up and the new pack went from 9 to 22 units per store a week, back near the old pack's 24.
Illustrative examples with mock-up numbers, one alert per lane. Lanes and timings are the cockpit's own.
In your pocket
Check the list between meetings or from the parking lot. It is the same cockpit in any phone browser, with the same one-time-code sign-in. Nothing to install, no software for IT to deploy.
What it finds
Illustrative examples with mock-up numbers. None of them would look broken on a scorecard.
Pack conversion
The retailer issues new item numbers on a size change, and the old item's demand history does not follow. The successor was keyed at a third of its predecessor's proven rate, so stores were stocked for a third, and the low sales were about to confirm the low forecast. After the ask, the rate came back.
Proven
Keyed
Selling
After
Units per store per week, selling stores
Price
Units responded to the price cut, so the review page called it a win. But units rose less than the price fell, so sales dollars went down, and margin dollars per store fell by 36 percent. Eight weeks earlier the same brand raised a price on another size and units held while margin rose. Same shelf, both directions, measured the same way.
Price down ▼
Units
Margin/store
Price up ▲
Units
Margin/store
Expected: price up, units down. What happened: units held and margin rose.
Replenishment
A de-trait, a reset, a forgotten price cut. Nobody calls. The item's sales go to zero in that store and stay there, and the loss hides inside a healthy-looking total until someone counts the stores that used to buy it.
Watched
Left
380 reactivations watched; history and the retailer's own moves come out first
Illustrative examples with mock-up numbers. Price changes compare every settled week at each price, the July 4th week left out. Units are per selling store per week. Margin is net IMU dollars per selling store per week, the retailer's own economics on the item.
What changes
Built on your retail data
Your store and DC feeds, daily and weekly, kept as history and reconciled so store movement and warehouse position line up in one place.
DailyStore sales and inventory, item by itemWeeklyStore sales and inventoryDailyWarehouse inventory and what is on orderWeeklyWarehouse inventoryWho this fits
The bottom line
What they actually need is a system that tells them what to do, and makes it easy to do it. Supply chain first. Everything else is a report.
Start here
Thirty minutes on a call. We walk a live cockpit set up for a fictional brand and show you where that problem would surface and what the ask would be. If it is not a fit, I will tell you that.
info@tenxanalytics.comEric Fritts · Founder · 479-713-0714 · Rogers, Arkansas