INVENTORY + MARKETING FOR SHOPIFY
Bring forecasting science, inventory economics and marketing evidence into one plan. Make your next move with your margins in mind.
Order before stock gets tight.Balance expected demand, margin and the value of leftover stock.
TWO PLANS. ONE DIRECTION.
Buying and advertising affect each other. Plan both around what you can sell, what you can supply, and what you keep.
See a range of possible demand, spot stockout risks, and size purchases around your costs, margins and supplier timing.
Find products to push, hold or clear. Review budget recommendations alongside stock, margins and the evidence behind them.
THE SCIENCE BEHIND YOUR NEXT MOVE
Modern forecasting research, established inventory economics and Bayesian methods help turn your data into decisions you can understand.
Demand is uncertain. Forecast scenarios show how sales could vary, so your inventory plan can account for both opportunity and risk.
Purchase quantities weigh expected profit, stockout costs and the value of unsold stock. Your margins and supplier timing shape the decision.
Eligible lift experiments inform how demand may respond to spend. Budget scenarios are compared with profit, stock and downside limits in view.
AVAILABLE TODAY
When a plan is generated, it uses the available sales history, stock and costs. Add eligible marketing experiments and the model can reassess its response estimates.
OUR DEVELOPMENT DIRECTION
We’re building toward ongoing model comparisons: test newer methods on historical periods they haven’t seen, check uncertainty ranges, and evaluate the trade-offs for inventory and profit.
More data creates opportunities to improve. Accuracy and profit gains still need to be measured. Automatic model selection and continuous accuracy monitoring are planned.
The default statistical forecast combines a damped trend with demand scenarios drawn from historical forecasting errors. The engine also supports Chronos-2, a time-series foundation model, as an optional provider that requires separate setup and evaluation.
Chronos-2 research (2025) ↗The established newsvendor model balances ordering too much against too little. Our implementation evaluates whole-unit purchases across demand scenarios and delivery timing. Residual value defaults to 30% of acquisition cost and can be changed in your company settings.
Newsvendor economics (research PDF) ↗The experiment-based model uses Bayesian response curves and compares complete budget allocations under uncertainty. Estimates from observed sales and spend are labeled separately. Research on advertising experiments informs this approach; the app does not reproduce every method in the linked paper.
Research on experiments and ad-budget decisions ↗These sources explain relevant methods. They do not establish performance for an individual store or imply endorsement of this app.
FROM DATA TO YOUR NEXT MOVE
Bring in Shopify sales and stock. Add product costs, lead times and your Meta or Google spend exports.
See demand ranges, reorder suggestions and marketing priorities, with an explanation behind each recommendation.
Approve your plan, export purchase orders, and apply your chosen budget changes in your ad platform.
SIMPLE, SEPARATE OR TOGETHER
Start with inventory, marketing, or bring both into focus.
Give your ad spend a clearer direction.
30 days free for eligible stores
Explore sample planKnow what to order, and when.
30 days free for eligible stores
Explore sample planBring your buying and growth plans together.
30 days free for eligible stores
Explore sample planSave $19/month compared with separate plans.
Prices in USD. Subscriptions are billed through Shopify every 30 days after the trial. Cancel before the trial ends to avoid a charge.
GOOD QUESTIONS
The details that help you decide.
Plans are recalculated from the store data available when they are generated. New eligible lift studies can update the marketing response model. We’re developing a process to compare newer methods against unseen historical data, review forecast uncertainty and assess inventory decisions before release. Automatic model selection and continuous accuracy monitoring are planned. More data does not guarantee better accuracy or profit.
The default forecast uses statistical trend models and simulated demand scenarios. The engine also supports Chronos-2 as an optional forecasting provider that requires separate setup and evaluation. Model updates need to be assessed for relevant store data and commercial use; a newer release alone is not evidence of better results.
Yes. Choose Inventory on its own, Marketing on its own, or both. The combined plan includes both modules, with a separate review and approval step for each.
Inventory planning uses your Shopify sales and stock, product costs, supplier lead times and expected deliveries. Marketing also needs your Meta or Google Ads spend, which you import as CSV files. The app flags missing inputs and products with too little history.
You stay in control. Review the recommendations and record your approval in the app, then apply budget changes in your ad platform. Inventory approval prepares purchase orders that you can download and send to suppliers.
The forecast considers a range of possible demand outcomes. Inventory recommendations weigh potential profit against the cost of excess stock. Leftover inventory is valued at 30% of its acquisition cost by default; your company can set a different assumption in Settings.
No. Recommendations depend on the available data and assumptions. Estimates from sales and spend history are labeled separately from recommendations supported by eligible lift experiments. You can review the evidence and the limits before making a change.
After installing the app, choose a plan and approve the subscription in Shopify. Eligible stores get 30 days free, then billing starts automatically every 30 days. Cancel before the trial ends to avoid a charge. The trial is available once per store; switching plans or reinstalling does not restart it.
MAKE ROOM FOR BETTER DECISIONS
Explore an illustrative plan. No sign-up needed.