Ometria's profile optimisation agent uses your transaction data to learn about your customers' behaviour and predict whether or not they are likely to place another order, when they are likely to order, and how much they are likely to spend.
It does this by comparing the contact's behaviour with normal behaviour across your whole account.
You can use this data to optimise your revenue by targeting and personalising content for strategically valuable customer cohorts, e.g. one time buyers likely to buy again.
Find out more: How are AI attribute segments calculated?
Ometria's predictive metrics are available in the single customer view, or as segment conditions in the customer filter.
You can use them to improve your marketing spend efficiency and results by targeting customers who are most likely to provide value in the coming year.
These conditions all look at your data to make a prediction for 12 months in the future.
Each condition has additional 'which' filters for more granular results.
| Condition | Description |
| Predicted 12 month customer spend |
Filter based on the contact's predicted spending over the next 12 months:
See also: Customer spend bands |
| Predicted 12 month order likelihood |
Filter based on the contact's likelihood to place an order in the next 12 months:
|
| Predicted average order value (AOV) |
Filter based on the contact's predicted AOV in the next 12 months:
|
Segment one time buyers who are likely to become repeat buyers
Set up the following:
- Customer frequency band - which is - 1 order
AND
- Predicted 12 month order likelihood - which is - Likely to order
OR
- Predicted 12 month order likelihood - which is - Likely to order multiple times
Create a VIP segment
Set up the following:
- Customer spend band - which is - Above average/Well above average
AND
- Predicted 12 month customer spend - which is - Above average/Well above average
Find your future VIPs
Set up the following:
- Customer spend band - which is not - Above average/Well above average
AND
- Predicted 12 month customer spend - which is - Above average/Well above average
Find loyal customers who will stay loyal
Set up the following:
- Customer frequency band - which is - 3-4 orders/5+ orders
AND
- Predicted 12 month order likelihood - which is - Likely to order/Likely to order multiple times
Find customers who will become loyal in the next 12 months
Set up the following:
- Customer frequency band - [which is not] - 3-4 orders/5+ orders
AND
- Predicted 12 month order likelihood - which is - Likely to order/Likely to order multiple times
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