One of the most important problems in retail industry is to associate the right product to the right customer at the right time. Companies spend huge amount of marketing dollars each year in terms of coupon promotions, email campaigns, targeted advertising, social media campaigns, etc. At the heart of all this activity is the notion of knowing the target set of customers for a given product or product segment, and the efficiency of these marketing activity hugely depend on accurately predicting the strength of the customer-product association. In this presentation Arun will talk about how the small team at Nordstrom Data Lab addresses this problem by analyzing large amount of data from various channels including transactions happening at Nordstrom stores, Nordstrom Rack, Nordstrom.com, product reviews, product search, email campaign data, social media data. Learn how the team came up with software product for recommendation and targetted product campaigns, with revenue per email increasing up to 30% and ability to associate customer affinity to thousands of segments.
Senior Data Scientist
Arun Veettil works as Senior Data Scientist at Nordstrom Data Labs. He helps companies find deep insights from hundreds of tera bytes of data and helps to create predictive analytics software solutions by developing machine learning based models. He has been working at the intersection of Big Data and Data Science for the past 5 years, using NoSQL data bases, Hadoop stack, Java and Python. At the Nordstrom Data Lab he works on interesting problems in retail domain, specifically to associate right product to the right customer, from various channels of data such as purchases, online search, click stream, email campaigns, product reviews, customer returns, social media, etc. Deeply passionate about machine learning, In his spare time, he blogs about various practical aspects of big data and machine learning and answers machine learning questions on Quora. He lives in Issaquah, WA with his lovely wife and two beautiful daughters. When he is not thinking about how to make you buy the right product, you fill find him grinding on the tennis court for hours or playing with this daughters.