Industrial applications of recommendation systems aim at recommending top-N products that are the most appealing to their customers, often focusing on those products that customers are likely to purchase in the near future. In this experiments and analyses paper, we present an extensive experimental evaluation of various top-N collaborative filtering recommendation algorithms based on a real-world dataset of customer's purchase history provided by our business partners at TOTAL. Our study aims to compare representative collaborative filtering approaches in practice and study the ones yielding the highest recommendation accuracy, with respect to well-established evaluation measures. These experiments are part of the development of a promotional offers campaign for TOTAL customers owning a loyalty card. We show how different settings for training and applying the selected algorithms influence their absolute and relative performances. The results are valuable to our TOTAL partners as they constitute the first large-scale analysis of recommendation algorithms in the context of their datasets. In particular, the study of the impact of recency in the training set and the role of customer activity and of context in recommendation shed light on a finer design of promotional product campaigns.