10X Analytics

Case studies

Proof, not promises.

Every dollar below was measured by the platform's own gated attribution. Nothing is projected, annualized, or assumed. Client identities are withheld; the metrics are shared with permission. The numbers re-generate with the data.

Case study 1 · Replenishment

The dollars that came back.

Items fall off replenishment quietly. A store stops ordering: a de-trait, a reset, a forgotten rollback. Nobody calls. The item's sales go to zero in that store and stay there, and the loss hides inside a healthy-looking total.

The platform runs the loop. Every alert names the owner, the single Walmart setting to fix, and the ask to send. Then the comeback watcher tracks whether the store actually starts selling again.

Comebacks watched

$196K314 store-item reactivations

Credited to Walmart

$62K187 combos. Their move, their credit.

Excluded, pre-tracking

$111KHistory is never claimed.

Banked and claimed

$13K204 store-items, 187 stores, 30-day window. Every dollar postdates its ask.

Why a buyer believes the number. The attribution is gated and argues against itself. A comeback that predates tracking is excluded. A comeback Walmart drove on its own is credited to Walmart. If the ask was flagged but never sent, no credit. What survives all three gates is the only number we put our name on. The other $183K stays on the board as watched, not claimed, which is exactly why the claimed number survives scrutiny.

Reactivation watch and captured tracker, live cockpit, read 2026-08-05.

Case study 2 · Distribution

Activated on paper.

In May, Walmart expanded a link pack from 232 stores to nearly 1,000, overnight. On paper every new store was fully set up: traited, valid, ready. But only 238 stores were selling. Roughly 750 brand-new points of distribution had no product flowing. No angry call comes for that. The item simply posts zero-dollar weeks in stores Walmart just granted, and those weeks sit on the item's scorecard at the next review.

The platform named the lowest broken rung. Not the trait, not the valid flag: store flow. Replenishment had never activated, so GRS built no auto-order and DC stock sat. The bandaid was a velocity-weighted DC push, capped at order-up-to. The durable fix was activation.

The expansion

232 to 997stores traited and valid, in one week

Selling at expansion

238roughly 750 new stores, nothing flowing

Selling by July 4th week

835after the push and the activation work

Weekly POS

$57K to $114Kpeak week, same item

And it shows up in Case Study 1: $12K of this DC-push recovery is already banked in the captured tracker, same loop, same gates.

Weekly store inventory silver mart, single item, fiscal weeks 202601 to 202626, read 2026-08-05.

Case study 3 · Price

Two price experiments, one verdict.

A client texted a question at 9:27am: why does the review page say the rollback is working when sales are down versus last year? By mid-morning the platform had reconciled every number on the page.

The rollback bought volume, not money. The 36oz dropped from $10.12 to $8.98 and units responded, up 18 percent per store. But margin dollars per store fell 34 percent, roughly $27 to $17 a week. The control: six weeks earlier the same brand raised the 12oz from $3.54 to $3.87. Units held, up 1 percent per store, and margin dollars per store rose 39 percent. Same shelf, both directions, measured the same way.

Rollback: units

+18%per store, every settled week

Rollback: margin $ / store

-34%$27 to $17 per week

Price up: units

+1%held at the higher price

Price up: margin $ / store

+39%$12 to $16 per week

Why a merchant says yes. The margin in these numbers is the retailer's own economics on the item, the same lens their merchant applies at line review. Restoring price is not a supplier favor.

Price-change ledger, live cockpit read 2026-08-06. Units are per selling store per week; margin is net IMU dollars per selling store per week.

Case study 4 · Second client · Pack conversion

The forecast that did not make the move.

A pack conversion re-keys everything. When an item moves to a new size, Walmart issues new item numbers, and the old item's demand history does not automatically follow. GRS starts the successor nearly blind.

The outgoing pack sold 27 units per store per week, every week, across 223 stores: months of proof. Its successor was keyed at 12. GRS orders to the forecast, so stores were stocked for 12, sold about 10, and the low sales then confirmed the low forecast at the next review. Nothing looked broken. The new item was set up, stocked, and selling. Only the side by side with the predecessor showed the launch running at less than half its proven demand.

Proven, old pack

27units per store per week, 223 stores

Keyed, new pack forecast

12less than half carried over

New pack selling

10tracking the forecast, not the demand

The ask

Key itone number, one owner, one screen

Conversion watch, live cockpit read 2026-08-06, second live tenant.

The pattern

The same operating system, four times.

It finds what hides in healthy totals

A zero-dollar week in a granted store, a comeback nobody noticed, a launch at half its proven rate. Losses invisible at the totals level.

It speaks Walmart's language

The ask names the trait, the flag, the order-up-to: the one setting the owner can actually pull.

It grades its own homework

Gated attribution means the claimed number is the defensible number. Watched is not claimed.

Start here

Tell me one item you think is bleeding.

Thirty minutes on a call, your data on the screen. If there is nothing there worth fixing, I will tell you that.

ericfritts@tenxanalytics.com

Eric Fritts · Founder · 479-713-0714 · Rogers, Arkansas