At ProfitPeak, we think the problem is structural — and we've rebuilt forecasting from the ground up to fix it.

The problem with historical data alone

Here's what historical analysis can't tell you: what's happening right now.

Your sales data from 12 months ago reflects a different customer base, a different competitive landscape, a different macro environment, and in many cases, a different product mix.

Using that data to forecast demand for an order landing in 3–6 months isn't just imprecise — it's forecasting the wrong market entirely.

The gap between "what sold last year" and "what will sell next season" is where most inventory mistakes live.

How ProfitPeak forecasts differently

We combine historical data and seasonality models with something most tools don't have access to: your own first-party pixel data.

Because ProfitPeak is tracking behaviour on your storefront in real time, our models understand current demand signals — not just what converted, but what customers are engaging with right now. Which styles are attracting attention. Which categories are trending up or falling away. What your customer base actually looks like today.

That last point matters more than most brands realise. Your customer cohort shifts constantly. New acquisition channels, changing demographics, different price sensitivity.

A forecast built on who bought from you 12 months ago isn't a forecast built on your current customer — and your current customer is the one you're buying inventory for.

Forecasting new styles before they have history

One of the hardest problems in inventory planning is the new product problem. You've designed a new style, you need to place an order, and you have no sales data to draw on.

We've built a lookalike model to solve this.

When you upload a new product, ProfitPeak analyses your catalogue and customer data to find the closest analogues — styles with similar attributes, price points, and customer affinity. The model uses those signals to generate a demand forecast before a single unit has sold.

It's not perfect, but it's substantially better than a gut feel or a flat opening order.

Planning at the level your business actually operates

Aggregate forecasts are a starting point. Real inventory decisions happen at variant level — by size, colour, and region.

ProfitPeak forecasts demand down to the SKU and location level, so you're not averaging out the signal. You can see that your size 8 in black is going to be the constraint in your Sydney and Melbourne fulfilment nodes, while your size 10 in white has headroom. That specificity is what separates an accurate forecast from an actionable one.

You can also add strategic context directly to the model. If your growth objective is new customer acquisition, you can tell ProfitPeak to weight the forecast accordingly — prioritising the products that are driving first-time purchase over those that perform better with existing customers. The model adjusts.

Your plan reflects your strategy, not just your history.

From forecast to purchase order

A forecast that lives in a spreadsheet isn't doing anything for your business. ProfitPeak's agents take the demand plan and build your purchase orders automatically.

You can set your available open-to-buy budget, and the system will rank reorder recommendations by lost revenue opportunity — so when you're making trade-offs under a cash constraint, you're prioritising the inventory gaps that cost you the most if left unfilled.

It's not just a smart forecast. It's a complete demand planning workflow, from signal to PO, with cash flow prioritisation built in.

The bottom line

Inventory planning has been stuck in a loop — look back, project forward, hope for the best. The brands that win on inventory aren't the ones with the most data. They're the ones acting on the most current, relevant signal.

ProfitPeak was built to close that gap: real-time demand intelligence, first-party data, lookalike modelling for new styles, variant-level precision, and agents that turn the plan into action.

If your current tool is telling you what happened, we'll tell you what's about to.