Understand demand

    See what's selling, what's stalling.

    A single month-to-date number tells you almost nothing: it cannot separate a SKU that is genuinely accelerating from one that had a good week. Unilytic compares any window against the one before it, per SKU and per warehouse, and shows you what is already in flight — so you can tell real demand from noise before you commit money to it.

    Seven windowsPeriod-over-period growthPer-warehouse historyIn-flight stock counted
    Compare, don't just count

    Growth needs two numbers.

    Pick a window — 7, 10, 15, 30, 45, 60 or 90 days — and Unilytic reports what sold in it alongside what sold in the equal window immediately before, plus the growth between them. That comparison is the whole point: 596 units this month is meaningless until you know last month was 412. Switch windows and the story often changes, which is exactly how you catch a SKU that is trending up on a 90-day view while quietly rolling over on a 15-day one.

    • Seven windows from a one-week read to a full-quarter trend.
    • Period-over-period growth against the equal preceding window, not a running total.
    • Grouped by sub-category, so a whole line's direction is visible at once.
    Read it at a glance

    Every SKU, every warehouse, over time.

    Sales tell you what customers wanted; stock history tells you whether you could actually supply it. Unilytic keeps a per-warehouse stock history for each SKU, so a run of empty days at one fulfilment centre stands out immediately — and you can stop mistaking a supply failure for a demand problem. Drill into any SKU for its own breakdown, or search a SKU or order number from anywhere in the app to pull up everything known about it.

    • Per-warehouse stock history beside the sales that depended on it.
    • Drill into any SKU for its full per-location detail.
    • Search by SKU or order number and get the position, the value at risk and the history.
    In the product

    This is the actual screen.

    Captured from a live Unilytic workspace — not a mockup, and not a roadmap promise.

    The Unilytic analytics page showing per-SKU sell-through, growth and stock by warehouse
    Plan ahead

    Numbers you can plan against.

    Analytics that only describes the past leaves you doing the forecasting by hand. These are the pieces that feed the next decision.

    Rewind to any past date

    Set an as-of date and the numbers recompute as they stood that morning. When you are reconstructing why a SKU ran dry, this is the difference between a theory and an answer.

    Spot the slow movers

    Declining growth against a healthy stock position is the signature of capital going stale. Sorting by growth surfaces the SKUs to stop reordering before they become a storage-fee problem.

    Why this matters

    Most restock mistakes are reading mistakes.

    The data to make a good replenishment decision is usually already in Seller Central. What is missing is the comparison: this window against the last one, sales against the stock that was available to make them, on-hand against what is already inbound. Without those, a seller reads a single number and fills in the rest from memory — and memory reliably over-weights whatever went wrong most recently. Every one of the expensive errors below is a comparison that was never put on screen.

    Demand and supply are separated
    Sales history sits next to the stock history that constrained it, so the two stop being confused.
    In-flight stock stops being invisible
    Future Stock folds pending shipments into the position you plan against.
    The past is reconstructable
    Any date up to yesterday can be replayed exactly, instead of argued about.

    Common seller mistakes

    • Reading one window and calling it a trendA strong seven days can be a promotion, a competitor stockout, or noise. Without the previous window beside it there is no way to tell which.
    • Mistaking a stockout for weak demandA SKU that was unavailable for eleven days sold badly — of course it did. Judge it on those sales and you will under-order it into a second stockout.
    • Ignoring stock already in transitOrdering against on-hand alone, when a shipment is days from landing, is how sellers end up paying long-term storage on their own duplicate order.
    • Never looking at the slow end of the listAttention flows to what is running out. Meanwhile the SKUs quietly declining with full cover are the ones eating your storage fees and your working capital.
    Questions

    Analytics, answered.

    How do I see sell-through for my Amazon FBA products?01

    Pick a window — 7, 10, 15, 30, 45, 60 or 90 days — and Unilytic shows units sold in it, units sold in the equal window immediately before, and the growth between the two, for every SKU. You can group by sub-category to read a whole product line at once, or drill into a single SKU for its per-warehouse detail.

    How do I know which SKUs are stalling?02

    Sort by growth. A SKU with declining period-over-period sales and healthy stock cover is capital going stale — it is accruing storage fees while it waits. Those are the lines to stop reordering, and they are easy to miss because nothing about them looks urgent.

    Does it account for stock already on its way to Amazon?03

    Yes. Future Stock adds your pending shipments to current on-hand, so a SKU with 60 units on hand and 240 inbound reads as covered rather than short. This is what stops you ordering the same units twice.

    Can I see what my inventory looked like on a past date?04

    Yes. Set an as-of date, any day up to yesterday, and the analytics recompute against the ledger as it stood that morning. It is the practical way to reconstruct what actually happened before a stockout instead of relying on recollection.

    Can I look up a single SKU or order quickly?05

    Yes. Global search takes a SKU or an order number from anywhere in the app and returns the current position, the value at risk and the relevant history, with exports available.

    Get started

    Find the trend before it costs you.

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