DIY With AI: The Home Depot's Huiming Qu

**Sam Ransbotham** (0:01)
When you think about tackling complex home improvement projects, the Home Depot likely springs to mind. But what complex AI and ML problems does the Home Depot face while helping you with your projects? Find out today when we talk with Huiming Qu, Senior Director of Data Science at the Home Depot.
Welcome to Me, Myself, and AI, a podcast on artificial intelligence in business. Each episode, we introduce you to someone innovating with AI. I'm Sam Ransbotham, Professor of Information Systems at Boston College. I'm also the guest editor for the AI and Business Strategy Big Idea Program at MIT Sloan Management Review.

**Shervin Khodabandeh** (0:40)
And I'm Shervin Khodabandeh, Senior Partner with BCG, and I co-lead BCG's AI practice in North America. And together, MIT Smr and BCG have been researching AI for five years, interviewing hundreds of practitioners and surveying thousands of companies on what it takes to build and to deploy and scale AI capabilities across the organization and really transform the way organizations operate.

**Sam Ransbotham** (1:09)
Today, we're talking with Huiming Qu. She's the Senior Director of Data Science and Machine Learning, Products, Marketing, and Online at the Home Depot.
Huiming, thanks for joining us from my hometown, actually, welcome.

**Huiming Qu** (1:21)
Glad to be here. Thanks, Sam.

**Sam Ransbotham** (1:22)
Can you tell us a bit about your current role at Home Depot?

**Huiming Qu** (1:26)
Yeah, so I support this awesome team that has data science and products for overall online and marketing.
And there's many challenging problems that we are solving, right, for improving the digital experience for our customers. So I'm super excited. I'm not a DIY person, so every day is a learning experience for me as well.

**Sam Ransbotham** (1:49)
Yeah, customers need help with so many different types of projects.
It seems overwhelming to think about narrowing that down to find specific products that would help them. So how is the Home Depot using AI to help with those projects?

**Huiming Qu** (2:02)
That's a question our teams continuously reflect on. These are very specialized categories, right? And sometimes we need to have other business partners involved as well. There is a particular domain knowledge about appliance or flooring or even plumbing, electrician.
When we have machine learning algorithms, if we have enough data, we can typically solve a lot of these problems. But a lot of times we don't have enough data for the niche problems that we're solving. When we're going down to the detail of a specific department in that specific category in that plumbing, we talk about PVC pipe. How do we do recommendations the best way? I think a lot of our merchant expertise knows what should go into the recommendation for that particular product. But we have over 2 million products online. How do we train the machine correctly to serve that in a scalable way is really the key. First of all, we need to make sure we are solving the actual true customer pain point and really aligning around data scientists, user experience, product engineering, this really cross-functional team aligning the goal together.
One of the examples is we have this project guide recommendation. We also serve a snippet of the difficulty level of that project. And we also serve algorithms in real-time identifying potentially what is the project you're working on. So if you're searching for Mira, you're searching for some of the tools and hooks, then we think you probably need some guide about installing a Mira.
These are things that our customers are facing every day, especially when we're at home, now a lot more than before. Literally, every day you can think about things could be improved.
We definitely feel the responsibility to help our customer, to get the help they need, even just when they search, we need to provide that specific product they're looking for.

**Sam Ransbotham** (3:59)
Shervin and I were just literally talking about how we're sitting around at home and seeing more things that need to be done now that we're home more often. Lots of what you've described I might call sort of episodic, like someone's at a search and they're trying to find something and they're doing something specific, but you have a larger relationship with customers. The search process might be improving an existing way of searching.
But can you tell us a little bit about what you're trying to do with multiple searches and longer customer lifetime experiences?

**Huiming Qu** (4:29)
Absolutely.
We wanted to really improve the search relevancy, recommendation relevancy, and every time when people landed on our sites to provide a better experience. These projects, sometimes it takes multiple sessions, multiple days, multiple weeks. So it certainly is a customer journey. So we do want to remember as much as possible where the customer stopped, what is in the cart, what are some of the prior searches, what are some of the prior visits, and did this customer actually click on some of the email that we sent or outside of Home Depot engagement on other websites about some of our marketing messages. It's a holistic experience. The more we understand about holistically where the customer is in their journey, the better we can serve them.