A machine learning approach to customer profiling by identifying purchase lifecycle stages
Who is this presentation for?Data scientists or analysts
Customers in an ecommerce website go through a buying cycle. In an ecommerce website, the customer buying cycle could be broadly classified into four different states: awareness, interest, desire, and action. At each phase, a customer exhibits different browsing behavior on the website. Identifying customer stages enables you to perform personalized targeting depending on the customer’s stage. In addition, understanding what events triggered the customers to move from one stage to another is also crucial. For instance, a consumer in the awareness stage could move to the interest stage after viewing the stellar customer reviews of a product while another consumer might have triggered due to the product price. Identifying this heterogeneity between the consumers would further fine-tune the targeting capabilities of a marketer.
Shankar Venkitachalam, Megahanath Macha Yadagiri, and Deepak Pai demonstrate machine learning techniques to analyze the online journey of a customer’s clickstream behavior to find the different stages of the customer’s buying cycle. They identify the critical click events that help transition a user from one stage to another.
- A basic understanding of machine learning (classification, clustering techniques), deep learning (long short-term memories (LSTMs)), and statistics
What you'll learn
- Understand why customer profiling and targeting is crucial for any ecommerce platform and identifying the customer’s stage in the purchase lifecycle is an important way for companies to do so
- Discover the stages of the purchase lifecycle of an ecommerce customer and how you can help in better targeting
- Learn the machine learning techniques used to identify the stages and critical events that help transition a customer between the stages
Shankar Venkitachalam is a data scientist on the experience cloud research and sensei team at Adobe. He holds a master’s degree in computer science from the University of Massachusetts Amherst. He’s passionate about machine learning, probabilistic graphical models, and natural language processing.
Megahanath Macha Yadagiri
Carnegie Mellon University
Megahanath Macha Yadagiri is a graduate research assistant at Carnegie Mellon University.
Deepak Pai is a manager of AI machine learning core services at Adobe, where he manages a team of data scientists and engineers developing core ML services. The services are used by various Adobe Sensei Services that are part of experience cloud. He holds a master’s and bachelor’s degree in computer science from a leading university in India. He’s published papers in top peer-reviewed conferences and have been granted patents.
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