We just raised $12.5 million in our A round led by Canaan with support from existing investors NEA, Fernbrook Management and Unshackled Ventures. Here’s the story and why we are excited.
Lily AI helps brands and retailers see their customers as they see themselves. The Lily AI Intelligence Platform helps predict the emotional context of each customer using deep product data and anonymized customer behavior data to deliver highly relevant and individualized experiences on e-commerce sites.
It's been quite a journey for Sowmiya Chocka Narayanan, my superstar co-founder and me. Lily AI was born from the idea that fashion should not be limited to the view that designers have of their models, but rather that all women should be able to wear the latest styles that appeal to them and make them look and feel their best. After interviewing 1000+ women about why they bought their last item of clothing, two years of testing with our own consumer app, and a major, successful pivot into an enterprise platform, it's hard to believe that we’ve really only just begun.
Emotional Context Is Unique for Each Individual
The old adage of “clothes make the man” is obviously out of touch today. But clothes, or really any purchase that a customer makes, reflects who they are. And the better the decision, the more confident the person feels whether it is the couch they sit on, the car they drive, or the food they order when dialing up a delivery for dinner. While that may sound obvious, it is difficult for retailers to know what makes each individual tick.According to Forrester Research, 93% of brands agree that consumers are more likely to spend money with a brand that they feel connected to, Yet barely 60%of brands understand how their customers think and feel, and a mere 38%strongly agree they know why one customer chooses to buy from their brand while another doesn’t. Additionally, Forrester states that how customers “feel” has a1.5x higher business outcome than how they “think.” For that, we created LilyAI.
One of the biggest unsolved challenges in fashion e-commerce is how to communicate the fit and feel of each product and understand each individual customer's emotional context as they shop. Most personalization technologies are segment-based because neither the insight about the product nor the customer is rich enough. For example, two people with the same psychographics - income, location, body shape and brand preference -could have very different views when it comes to their personal taste. Understanding emotional context on every visit and for every customer will transform the relationship retailers have with their customers and how they feel when they shop, first online and eventually in the store.
Understanding the Relationship Between People and Products
What does Lily AI actually do? At our core, we do three related things:
First, we have built the deepest understanding of products. Our computer vision and deep learning technology can understand and assign ten times more attributes to every product in a product catalog than a typical retailer can tag today. Those tags include objective attributes like cut, sleeve fit or embellishments. With our team of stylists and image consultants, we can also teach the AI to assign emotional and contextual attributes like “feminine style” or fit attributes like “Hits at Hip.” LilyAI’s uniqueness comes from understanding every detail that could be a reason for the user to like or dislike a product.
Next, we have built the deepest understanding of customers shopping behavior. Our machine learning technology helps us create an in-depth prediction of the customer’s preferences and self-perception using anonymized customer behavior data. Here Lily AI’s unique value lies in the strength and accuracy of the prediction for each individual customer’s ‘why’ behind what they do at an e-commerce store. Inferring the “why” behind each shopping visit through this process allows those experiences to vary based on tangible product features as also the emotional context of that visit
Finally, we help retailers create individualized customer experiences. Using our deep understanding of product data and the unique patterns of each customer’s visit, we’re able to create effective personalization by improving site search, enhancing filters or providing product recommendations. Occasions such as work or going out, dressing style preferences like romantic or fun, and fit preferences like a tighter or looser fit all factor into how retailers can help customers find what they are looking for.
Next Steps in our Journey
We are already working with many household names in retail and fashion. Our customers range from big box stores to specialty brands, and full-line department stores to resale marketplaces. This has confirmed our belief that the next phase of retail will need to emphasize an individualized experience and an understanding of emotional context wherever and whenever a customer shops. With so much fashion shopping moving online, the ability to replicate the knowledgeable and personal in-store associate in a 2-D online environment will be fundamental to deeper customer relationships.
This new capital will give us the ability to grow our engineering, data science, product and sales teams. We are very excited to use this investment to help retail transform into a new and exciting phase and help everyone find the products that make them look and feel their best.