The analysis of customer journeys is a subject undergoing an intense study recently . The increase in understanding of customer behaviour serves as an important source of success to many organizations. Current research is however mostly focussed on visualizing these customer journeys to allow them to be more interpretable by humans. A deeper use of customer journey information in prediction and recommendation processes has not been achieved. This paper aims to take a step forward into that direction by introducing the Order-Aware Recommendation Approach (OARA). The main scientific contributions showcased by this approach are (i) increasing performance on prediction and recommendation tasks by taking into account the explicit order of actions in the customer journey, (ii) showing how a visualization of a customer journey can play an important role during predictions and recommendations, and (iii) introducing a way of maximizing recommendations for any tailor-made Key Performance Indicator (KPI) instead of the accuracy-based metrics traditionally used for this task. An
extensive experimental evaluation study highlights the potential of OARA against state-of-the-art approaches using a real dataset representing a customer journey of upgrading with multiple products.