How are AI attribute segments calculated?

See also:

• AI Attributes
• Contact details: Performance overview

Our metrics for predicted purchasing behaviours are powered by statistical models that learn from your order data. 

As well as looking at each individual customer's order behaviour, the models compare those behaviours against overall patterns across your entire customer base.

We use up to three years of transaction history to train our models. 

If you have less than three years' data, we can still train once there are at least 180 days of transactions in your account.

The models analyse recency, frequency, and monetary value (RFM) data across all customers who have placed orders. 

The models consider data points such as:

• Purchase history - number of orders, timing between purchases
• Spending behaviour - CLV, average order value
• Recency - how long since the last order

By combining individual behaviour with overall customer patterns, Ometria estimates:

• The likelihood a customer will order again in the next 12 months
• How much they’re likely to spend
• Their predicted average order value

One-time buyers

Customers who have only placed one order will still get predictive labels - but as these are based on your account average they might be less effective. 

Accuracy

Ometria's models are trained and continuously validated against real data. 

That means we regularly check predictions against what customers actually do, and update the models to keep them accurate. 

This approach is widely used in retail to forecast customer lifetime value (CLV) and future purchasing behaviour.