Why Wyvern
Why do we build Wyvern?
Revenue Growth and Cost Saving
Every marketplace encounters common challenges that can greatly benefit from the application of Machine Learning (ML) solutions. These challenges bring huge upsides into revenue growth and cost saving. They encompass following areas
Search & Discovery
In marketplaces, especially those that sell products, their core product is to help the buyer find the right products that they’re looking for, whether it’s through a recommendation email, the products or carousels on the home page or through search results. The creator of Wyvern, Suchintan, built the ML Platform at Faire and Gopuff to improve their Search and Discovery experience. At both places, the platform became an engine that empowered the data team to independently deliver new models to production, generating over $100M of impact.
Other Common Use Cases
Fraud Detection: The total cost of ecommerce fraud was expected to be $48 billion globally this year according to ekata. Being able to detect and prevent fraudulent actions would prevent them from losing millions of dallars every year. This is exactly what the other creator of Wyvern, Shu, had experienced when he worked at Lyft and Patreon.
Shipping Cost Estimation: Shipping is a key part of the success of a marketplace’s or for any brand’s success in your marketplace. 48% of the cart abandonments are due to extra costs like shipping and taxes. Marketplaces have a strong will to optimize their shipping cost strategy as a way to improve their revenue.
Credit Scoring: Lots of marketplaces provide NET 30/60/90 terms as well as granting credit limit to their users. For example, Faire has net 60 terms, meaning that purchases mad with net 60 terms won’t be charged until 60 days later after the order. In order to provide such service, the marketplace usually has to do credit approval process, just like what banks does to approve or deny your credit card. Moreover, they also have to decide how much credit limit the user could get based on the credit information. Usually this process starts with manual review but it won’t scale. Machine learning models derisk and automate the process, saving a lot of cost here on operations.
Simplify ML Platform for Marketplaces
From 2019 to 2022, we built two ML platforms from scratch both at Faire and Gopuff. We’ve also observed the wide usages of such a platform at a few others (Lyft, Patreon). We intimately understand the pain, substaintial costs and extensive efforts required to build a top-tier ML platform:
- Feature store to store features
- Model service to host ML models
- Feature + Model observability
- Orchestration for ML pipeline
While being mindful of the following constraints:
- Pipeline evaluation in < 200ms (Reference: https://iarapakis.github.io/papers/TOIS17.pdf)
- Minimizing train / test skew
We explored multiple vendor solutions in the market for product ranking back then but couldn’t find any solution that would empower the team to rapidly iterate on ML models tailored for our own business. We had so much unique business insights so having the control of the models was a no brainer. Therefore, building a ML platform in-house became the only solution.
We talked to a bunch of other marketplaces, including Etsy, Amazon and Ebay, and learnt that they all face and share the same challenges above.
When you start building an ML platform in-house, it’s like exploring a maze. We want to build Wyvern to help marketplaces get through it.
Was this page helpful?