  Predicting the behaviour of shoppers provides valuable information for
retailers, such as the expected spend of a shopper or the total turnover of a
supermarket. The ability to make predictions on an individual level is useful,
as it allows supermarkets to accurately perform targeted marketing. However,
given the expected number of shoppers and their diverse behaviours, making
accurate predictions on an individual level is difficult. This problem does not
only arise in shopper behaviour, but also in various business processes, such
as predicting when an invoice will be paid. In this paper we present CAPiES, a
framework that focuses on this trade-off in an online setting. By making
predictions on a larger number of entities at a time, we improve the predictive
accuracy but at the potential cost of usefulness since we can say less about
the individual entities. CAPiES is developed in an online setting, where we
continuously update the prediction model and make new predictions over time. We
show the existence of the trade-off in an experimental evaluation in two
real-world scenarios: a supermarket with over 160 000 shoppers and a paint
factory with over 171 000 invoices.
