Successful AI Meets Business Needs—and Human Needs Posted on : Oct 16 - 2020

A look at how Zulily is using the latest tools in artificial intelligence, machine learning, and cloud computing to innovate and serve its customers with purpose.

Each day at Zulily we add 9,000 products to our online store and process more than 5 billion clicks from online shoppers. That is more virtual inventory than you'll find in the warehouses of many retailers, and it’s by design. We've built a supply chain where we hold only some goods: most of the time, we don’t purchase inventory until our customers have, so we are able to pass down savings from our unique supply chain down to our customers around the world. To the customer, that means a constantly changing and new shopping experience. Our system works only if we can ensure that both shoppers and suppliers move quickly. To do this, we're cloud-based, data-driven, customer-first, and obsessed with machine learning—pretty cutting edge.

The way we get to all that innovation, however, is by staying close to a few basic rules that we amplify with cutting-edge technology, particularly when it comes to the way we look at the utility and purpose of data. And though Zulily may specialize in ecommerce, every technology professional interested in AI and ML can benefit. Here is a short list:

1. How well you describe your data is key

Obviously, we have a lot of data, and we believe strongly in data analytics and ML to power our business. A critical part of this analysis is the tags we put on data, and how well we describe things with those tags.

For example, we sell a lot of fashion. Frequently, we've found that things that matter to consumers, like the length of sleeves on a shirt, or the height of a shoe's heel, are not inside standard product descriptions. Typically, this hasn't been an aspect of clothing descriptions online—suppliers may only provide that level of detail 20% of the time or less. We could manually input information by having someone examine images of each product before it goes live, but this would be time-consuming and inaccurate.

Using AutoML Vision, which we use to train our own custom ML models from our image data, we get much closer to expressing data about our products that our customers want to make a buying decision. We train custom ML models to classify product images according to labels we define, such as sleeve length. This enables us to include sleeve data in our product descriptions more than 80% of the time.

This isn't a problem that is unique to retailers. Take a company like real estate company Zillow, where I previously worked. Companies like them have a slower-moving inventory and work inside a different business model, but face similar issues around turning photographic information into floor plans. You also see leaders in construction-related fields use AI and ML to determine the types and amounts of stone in a quarry, based on drone and mobile device video footage. View More