Salesforce Einstein is the artificial intelligence layer that delivers predictions and recommendations based on the customer’s unique business processes and data. Einstein Journey Insight is one of the key product offered by Salesforce DMP to help marketers and publishers leverage AI to analyze billions of touchpoints across consumer journeys and discover the optimal paths to conversion, including insights about which channels, messages, and events perform best.
To understand how consumers engage with website articles, advertising campaigns, social events, products and how that essentially leads to a conversion, analysts need to identify key events among thousands of events per user. Frequent pattern mining is the key technique for solving such problems. We have all heard about the beer and diaper story for mining consumer buying habits, however, at Salesforce DMP, we see over 3.5 billion unique users globally a month, across sites, media, mobile app, transactional, and offline, traffic sources. That is more than Facebook, Wikipedia and Twitter combined. The sheer volume, the heterogeneous nature of events and their metadata offer unique opportunities to analyze the complete consumer journey.
However, it also makes it extra challenging to interpret the results or even run the frequent pattern algorithm cost effectively. In this talk, we are going to share our experience of running large scale frequent pattern mining operations using Apache Spark in our Einstein Journey Insight product. We will examine the practicality of the Frequent Pattern technique, and show how Spark helps us address the scaling problem, deal with diverse metadata, and generate interpretable and actionable insights.