In the following tutorial, we answer both questions using the R package arulesSequences , which implements the SPADE algorithm . Concretely, given data in an Excel spreadsheet containing historical customer service purchase data, we produce two separate Excel sheet deliverables: a list of service bundles, and a set of temporal rules showing how service bundles evolve over time. We will focus on interpreting the latter result by showing how to use temporal rules in making predictive sales recommendations.
Our running example below is inspired by the need for Microsoft’s Azure Services salespeople to suggest which additional products to recommend to customers, given the customers’ current cloud product consumption services mix. We’d like to know, for instance, if customers who have implemented web services also purchase web analytics within the next month. Actual Azure Service names have been removed for confidentiality reasons.
Market basket analysis is an interesting topic, though in my limited experience, it really falls apart when you have a large number of products to compare, so it tends to work better with toy examples or limited product selections because when you have a 50,000+ SKU inventory, the lift of any individual combination of products rarely gets above the level of noise.