ML at The Home Depot with Pat Woowong: The Falloff Model and Lead Scoring

07/20/22 • 65 min

When people think about The Home Depot, they probably think more about lumber
and tile than they do ML models. Sure, there is plenty of lumber. But machine learning also plays a key role in the business, in places that customers can see as well as the behind-the-scenes operations.Senior Content Advisor Q McCallum met up with Pat Woowong, Director of Data Science at The Home Depot, to explore how the company mixes their very rich dataset with domain knowledge to employ machine learning deep inside the business. To frame this, he walked me through the Falloff model and Lead scoring, two projects that his team deployed to address the unique challenges of a company that handles both retail and services.During our conversation, we discussed: understanding where models fit into the bigger business picture; using expert domain knowledge to drive feature selection and feature engineering; the value of process; and, to top it off, what it's like to work at The Home Depot.Other places to find Pat:

LinkedIn: https://www.linkedin.com/in/patwoowong/
"How THD keeps shelves stocked using ML" (the talk he mentioned during our interview): https://twimlai.com/podcast/twimlai/how-ml-keeps-shelves-stocked-home-depot-pat-woowong/
"The Value Proposition for Using ML in Brick-and-Mortar Retail Stores: Home Depot" https://www.youtube.com/watch?v=rF8jtdX-hGo

Be part of the conversation and connect with the data science community at DSS Miami Hybrid on September 21, 2022.

Book your ticket now.

LinkedIn: https://www.linkedin.com/in/patwoowong/
"How THD keeps shelves stocked using ML" (the talk he mentioned during our interview): https://twimlai.com/podcast/twimlai/how-ml-keeps-shelves-stocked-home-depot-pat-woowong/
"The Value Proposition for Using ML in Brick-and-Mortar Retail Stores: Home Depot" https://www.youtube.com/watch?v=rF8jtdX-hGo

Be part of the conversation and connect with the data science community at DSS Miami Hybrid on September 21, 2022.

Book your ticket now.

Previous Episode

Coffee Chat: Inspiring ML Use Cases in Retail Delivering Measurable Impact

This episode is a coffee chat recording from DSS Virtual in May 2022. Charles Irizarry (Phygital) and Ankita Mangal (P&G) share in war stories of ML use cases they use in retail and eCommerce scenarios, brokering data, and protecting the important principles of data ethics and privacy. Ankita shares the digital transformation journey that P&G undertook, her growth together with P&G, and some of the incredible technologies P&G has developed to better serve their customers world wide.

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The roles of economists in data science, with Dr. Amar Natt

We've all heard the term "economist," sure. But exactly what does and economist do? And as economics is a very data-driven field, where does their work intersect with data science, machine learning, and AI?

To answer that question, Senior Content Advisor Q McCallum spoke with Amar Natt, PhD. She's an economist at Econ One Research, and her work focuses on advanced analytics and predictive modeling. Does that sound like ML to you? Well, Amar explains that it's similar in some ways, different in others. From there, she tells us about techniques economists can learn from data scientists, and what data scientists can pick up from econ. (Hint: "causal inference." You heard it here first.) You can find Amar online: