Binary classification algorithm
Possible scenarios:


You can predict "Likely to Buy Probabilities": Create features that covers before purchase (e.g: product click, product view, purchases from same category etc). After, train a classifier and use prediction probabilities  (predict_proba_) to obtain scores. Then sort them pick x% and report them to business unit. They can offer discounts, campaigns to converge customers fast.
Try to find segment transitions. For example coke buyers move to lets say pie segments faster than vegetable segment. Identify those transitions and apply advertisement on cross categories.
If you have transaction data you should try to use apriori based algorithms and collaborative filtering methods for recommendations.
Additionally you can do RFM analysis and compare RFM segments and already created segments to find best customers of yours.


Hope this is helpful!