Forecasting that looks beyond the item

Predict demand per customer, not just per item. So account managers can adjust early, and supply no longer has to guess.

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Customers at the centre of demand planning

Forecast per customer

Understand buying behaviour and budget shifts at account level, not just per item. See how demand develops per customer, instead of having to infer it from an item-level total.

Insight per customer Understand buying behaviour and budget shifts at account level, not just per item.

Early warning Recognise declining demand from key customers and act before it has impact. Account managers see changes as they happen, not just in the quarterly numbers.

Automatically pre-filled A machine learning model pre-fills the forecast based on your sales history and trends. Account managers don't have to enter everything themselves, only adjust where they know more than the model.

Who is this for?

Sales and supply, both in the driver's seat

Forecasting per customer isn't just a sales tool, and it isn't just a supply tool. It only works well when both sides share the same picture.

For account managers

Put your customer knowledge straight into the forecast. You know when a customer starts or stops a project, that belongs in the plan, not just in your head.

For supply & planning

The customer forecast is aggregated to item level and compared with your own supply forecast. So you manage by exception, instead of checking everything by hand.

Beyond customer behaviour

Recognise seasonal peaks before they happen

Customer signals aren't the only pattern a flat average misses. Demand also moves with the calendar: a collection launch, a trade fair, a supplier's own peak season. That pattern returns every year, at roughly the same time, whether a customer mentions it or not.

Pattern detection A machine learning model compares the same period across several years and calculates how strong and consistent a seasonal peak or dip really is.

Trend versus season Separates structural growth from a seasonal peak, so a temporary spike doesn't get mistaken for lasting demand.

Works from day one A new item can borrow the seasonal pattern from others in the same group, for example the same colour, instead of waiting for its own sales history.

Read how seasonal forecasting works

Recognise seasonal peaks before they happen

Forecast + pricing agreements: the full picture

A forecast per customer is powerful. Combine it with your pricing agreements per customer, and it becomes more than that: you see not just what you'll sell, but what you'll earn from it.

See pricing agreements