  Timeliness and contextual accuracy of recommendations are increasingly
important when delivering contemporary digital marketing experiences.
Conventional recommender systems (RS) suggest relevant but time-invariant items
to users by accounting for their past purchases. These recommendations only map
to customers' general preferences rather than a customer's specific needs
immediately preceding a purchase. In contrast, RSs that consider the order of
transactions, purchases, or experiences to measure evolving preferences can
offer more salient and effective recommendations to customers: Sequential RSs
not only benefit from a better behavioral understanding of a user's current
needs but also better predictive power. In this paper, we demonstrate and rank
the effectiveness of a sequential recommendation system by utilizing a
production dataset of over 2.7 million credit card transactions for 46K
cardholders. The method first employs an autoencoder on raw transaction data
and submits observed transaction encodings to a GRU-based sequential model. The
sequential model produces a MAP@1 metric of 47% on the out-of-sample test set,
in line with existing research. We also discuss implications for embedding
real-time predictions using the sequential RS into Nexus, a scalable,
low-latency, event-based digital experience architecture.
