How to Use Recommendations in Online Marketplace Marketing

How to Use Recommendations in Online Marketplace Marketing

Online marketplaces that deliver stellar recommendations are a staple of customers’ browsing habits worldwide. Each country may have its own favorites, but they all have the same basic structure: get goods from many sellers into the view of customers, in one central location. And while we traditionally think of ecommerce and used goods as being the primary uses for marketplaces, almost every industry and vertical have their own version of a marketplace, whether it’s mortgages and credit cards in financial services or baseball cards and beanie babies on auction sites. These sites are essentially huge databases filled with content and products, which can be difficult to sift through — that’s where recommendations come into play.

Marketplaces have incredible earning potential — the major marketplace players sold $2.03 trillion worth of goods in 2019, which made up 57% of global web sales for that year as well. These astounding numbers were possible in part to the (almost scarily) relevant recommendations the top marketplaces, like Amazon, serve up to customers. Our 2020 Benchmark Report found that campaigns using Predictive Recommendations are 116% more effective than those without. There’s no doubt that while marketplaces are a staple of online shopping today, a huge part of their success comes from their ability to deliver hyper-accurate recommendations throughout the customer journey. Here are just a few ways Blueshift customers in the online marketplace space are using recommendations successfully today.

Tradera: Online Marketplace for Used Goods

Tradera is one of Sweden’s most popular online marketplaces to buy and sell used electronics, household goods, clothing, and more. As Tradera has evolved and grown, their marketing team struggled to balance all of the moving pieces that came with an ever-changing inventory of millions of items up for auction (to make matters more complicated, the team had limited ability to collect dynamic customer data). To deliver personalized auction recommendations at scale, Tradera uses Blueshift’s fully integrated system to handle the demands of their dynamic catalog, varying product auction length, and ability to collect large amounts of unstructured data.

Blueshift’s unique ability to understand complex listing data against customer data was key to the team. This capability allows Tradera’s marketers to serve up timely personalized recommendations to each customer during the buying process. Additionally, Blueshift’s Single Customer View enabled the team to collect, unify, and understand their first-party data better than ever before. Blueshift’s AI-powered segmentation and Predictive Recommendations made it simple for marketers to build on-brand, templated emails with custom recommendation blocks and deliver those messages to their most valuable customers — all autonomously. Blueshift made it easy for the Tradera team to surface 1:1 auction recommendations onto their site, allowing each user’s experience to be completely unique and tailored to their preferences.


  • On-site and in-app recommendations as users browse through products up for auction that increased sales by +131%
  • Specific product recommendations within email campaigns, that drove click-through rates back to their site and app by over 40%
  • 1:1 recommendations across paid media campaigns, especially using Facebook to target ideal users

Zumper: Online Housing Marketplace

Zumper is an end-to-end rental platform and online listing marketplace which aims to make renting a place to live as easy as booking a hotel. But, no two users have the same journey and as their user base grew, their marketing team struggled to deliver timely consumer-centric marketing based on shifting user preferences and an ever-growing catalog of listings. Blueshift’s fast, flexible system could keep up with millions of listings at any given time, and the team loved that it was AI-powered, rather than rule-based because our recommendation engine covered all their use cases.

The Zumper team set out to use our AI-powered platform to revolutionize their user experience and put the power of customer data in the hands of their marketers. Blueshift’s ability to process Zumper’s vast amount of customer data in real-time has surfaced some fantastic results for the fast-growing real estate brand.


  • Curated listings based on explicitly given criteria and browsing patterns that scaled leads by +384%
  • Listing recommendations across email, Push, SMS, and more that resulted in +128% click-through rates
  • Surface hyper-accurate recommendations based on what’s new, trending, or relevant to the customers to promote win-back and re-engagement

These are just two of the many recommendation examples that online marketplace companies like LendingTree, Groupon, Vouchercloud, and more use Blueshift to accomplish. If you’re interested in learning more about what makes Blueshift’s AI-powered recommendations different, chat with one of our experts and see Blueshift first-hand, today. And, dive deeper into what makes our recommendations successful in our 2020 Benchmark Report, available here.