Discovering unusual customer journeys with process mining

Alex is a data scientist who analyzes websites to improve conversion rates. When you optimize a website, you want it to become better at its primary goal. A typical goal for an e-commerce website is the completion of a purchase. However, for larger companies there are also other goals, such as the customer using an online calculator, visiting a store, or making an appointment with a sales rep. Alex applies classical tools such as BigQuery and Python to analyze website usage. Traditionally, customer journey analysts assume a sequential conversion funnel for the customer behavior toward the goal. For example, after the homepage, the customer visits the product pages, adds a product to the cart, and completes the purchase. However, in reality, customer behavior is much more complex. People take very diverse paths throughout the website. The traditional conversion funnel cannot capture that. Alex showed how he now uses process mining to understand actual customer behavior. The process mining tool shows the full complexity of how people interact with the website online. Alex then translates these complex behavior maps into actionable results relevant to his clients. For example, based on the current usage patterns, he develops multiple improvement ideas for the website that the company can validate in so-called AB tests.