
Cleanse historical sales to strip out outliers, promotions, and stockout distortion. Judge each style on real demand. Know which SKUs to keep, cut, or grow.
Model the ideal width and depth for every assortment and see the productivity and margin impact of each cut in real time. Trim the tail without losing sales.
Compare rationalization scenarios side by side and seed next season's assortment straight from the winning one. Every choice is backed by data.
Deseasonalize sales and filter outliers and low performers automatically, so rationalization runs on clean, comparable data.
Model marginal return curves with interactive sliders to find the point where adding SKUs stops adding sales.
Build rationalization scenarios by cluster or product type and seed the new assortment directly from the results. No re-keying needed.
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Teams
Merchant teams
Plan with greater precision, adapt to market shifts instantly, and maximize profitability through advanced retail planning capabilities.

Use Cases
Core & Seasonal Line Planning
Plan core and seasonal products together in one workflow, aligning every assortment with financial and sales goals.

Use Cases
Localization & Cluster Planning
Tailor product assortments by region, size, or demographics with automated clustering that scales personalization.
Not when the cuts are the right ones. Toolio identifies the styles that add cost and clutter without adding sales — the tail that duplicates demand others already capture. AKA Brands used Toolio to run 50–75% fewer SKUs at the same sales, freeing cash and space for the products that actually perform.
A once-a-year spreadsheet review can't cleanse the data or model the trade-offs, so cuts get made on gut and gross sales. Toolio deseasonalizes history, strips out stockout and promo distortion, and shows the margin and productivity impact of every change — so the review is faster, defensible, and repeatable each season.
That's exactly what the cleansing step is for. Toolio deseasonalizes sales and removes outliers, promo spikes, and stockout gaps before anything is rationalized — so you're judging each style on true demand, not distorted history.
No. Toolio surfaces the data and models the impact, but planners decide — you can protect keystone or halo styles and override any recommendation. It gives merchants evidence to defend the calls they already believe in, and catches the quiet underperformers they'd otherwise miss.
A bottom-sellers list ranks by raw sales and misses why an item underperformed — a stockout, a late delivery, a clashing duplicate. Toolio models marginal return, showing where adding styles stops adding sales and which cuts genuinely won't cost you — a rationalization, not just a list.
Absolutely. Toolio supports large data volumes, complex hierarchies, and multi-level planning, making it ideal for enterprise retail planning and analysis.
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