Tradesy is a popular ecommerce platform for buying and selling luxury clothing and accessories. The peer-to-peer marketplace is on a mission to bring trust and fairness to fashion, making great style affordable and accessible to everyone.
According to Tradesy, closets shouldn't be littered with unwanted wear: a once-worn wedding dress, beautiful shoes that pinch your feet, or pants that no longer fit. Instead they should be sold to people who will love them. Millions of people who use the platform agree. Tradesy is quickly becoming the go-to exchange for luxury goods.
To improve buyer conversion rates and better understand its customers, Tradesy needed consolidated data, but building an inhouse solution required 3 dedicated employees for 6 months. With Twilio Segment’s Amazon S3 integration, Tradesy now has a scalable data platform for exploratory analysis on user flows and machine learning for product recommendations. Tradesy saved 6 months of engineering time and effort, and is now leveraging its data for product improvements.
Turning browsers into buyers with more customer insights
Tradesy's data engineering team had implemented Google Analytics to understand shopper behavior, hoping to turn browsers into buyers with great app design. Google Analytics helped give insight into customer behavior, but it only illuminated simple aggregated conversion metrics, for instance, if a customer landed anywhere on the site or app and eventually purchased something. In addition, the team couldn't tie funnel data to actual users since Google Analytics anonymizes every interaction.
In order to improve buyer conversion rates and learn more about its customers, the team needed more information. Why were customers bouncing? What made them stay? Where exactly in the checkout process did they abandon the chase?
Tradesy's CTO considered building a solution in house to consolidate data across their apps, websites, and transactions. However, building a data collection library, ETL pipeline, and analytics integrations that were reliable, scalable, and worked across devices would have been a massive undertaking — requiring three people for six months to be exact.
Capturing valuable shopper data across web and mobile
Tradesy chose Twilio Segment as their Customer Data Platform. They use Twilio Segment’s collection libraries for the web and SDKs for Android and iOS to capture valuable user data in a granular fashion. Twilio Segment reliably delivers the data to their end tools, helping them understand how people come to their platform, what exactly they do, and whether or not they buy or come back.
Once they had instrumented Twilio Segment, the team had the option to send their data to any of the 200+ tools on the Twilio Segment platform with a few clicks. Tradesy chose to load their data into Amazon S3 via Twilio Segment’s integration.
Amazon S3 offers a scalable storage platform for raw data that the team uses to power exploratory analysis on the checkout funnel and other user flows. They also use the data to feed machine learning algorithms that provide product recommendations to each customer.
Deeper understanding of the customer journey with Twilio Segment
Since implementing Twilio Segment, Tradesy has been able to gain a deeper understanding of the customer journey. They now know exactly when, why, and how people get stuck before buying or listing a fashion item.
With Twilio Segment, Tradesy has been able to ramp up the number of data points the team is collecting from 5 to 100. This has helped to significantly reduce time to insight. Before Twilio Segment it would take weeks, or months even, to implement any new event. Now it’s a few minutes.
With the data pipeline project and maintenance off their plate, Tradesy’s data engineers have been able to work on more interesting challenges, like filtering out bot traffic from their analytics and making their recommendation engine operate in real-time.
"Twilio Segment has been a pleasure to work with on both the product and the support side," said Michael Viamari, data engineer at Tradesy. "Implementing it was easy and straightforward, and it has saved us a significant amount of engineering time and effort that we can now devote to leveraging our data to improve our product."