Alex Weinstein, Director Marketing Technology and CRM bei eBay, gibt in einem Interview mit eMarketer spannende Einblicke in das E-Mail Marketing der Handelsplattform. Weinstein erläutert, wie sich eBay von einer „batch-and-blast“ Strategie verabschiedet und einen klaren Fokus auf das Themenfeld Personalisierung gelegt hat.
eBay hat die dafür notwendige technische Infrastruktur weitgehend Inhouse aufgebaut:
We evaluated a bunch of third-party offerings, but there were two reasons for doing this in-house. The first reason was eBay’s sheer scale. […]
The second reason was our internal decision to prioritize this work and be one of the best in the world at it. That’s why we decided we have to develop in-house expertise that will enable us to deeply understand every element of the stack, develop machine learning models and create real-time data processing pipelines to differentiate ourselves.
Weinstein erläutert, wie eBay Machine Learning im E-Mail Marketing implementiert hat:
Now, we have a real-time data pipeline that powers our downstream marketing campaigns. Whenever an action takes place on the site—a customer buying an item, browsing or just seeing an ad—the activity is tracked by our real-time engine, which updates the profile of the customer and sets off triggers that we have embedded in the system. The triggers apply to both customers and items: For example, the moment the price changes on an item a customer has viewed, we can automatically send that customer an email..
Our journey with CRM started with building this real-time data pipeline for email. Our next step was adding machine learning to that pipeline. And the third step was expanding this system to all outbound channels, including the customer’s experience on-site.
Weinsteins abschließende Empfehlung für E-Mail Marketer:
My invitation to my colleagues in the industry is to not try to boil the ocean, but rather dip their toes in a little bit. For example, use a light machine learning model to improve your newsletter. Try it, and you’ll see results.