The Mail Marketing Platform was an automatic system created for niche digital-to-direct sphere operations.
Our client was a pioneer in a niche business segment related to digital-to-direct mail. They created a unique marketing platform with over 100 brand partners and a listing in ARF’s First Innovators A-List. The client was promoted by a number of widely known business media, such as Forbes Magazine, Business Insider, Business Wire, etc. The company comprises more than 50 employees and is headquartered in New York.
The client needed to rebuild their existing platform entirely as the business grew rapidly and the company needed a more scalable solution. The current platform was developed as a proof of concept and wasn’t designed for huge loads. In addition, the client wanted to utilize Big Data technologies to the project to ensure high performance and scalability.
The client’s original solution was based on Python which didn’t provide enough scalability for the rising demand. The client wanted to rebuild the platform completely and implement new web features. At first, Geomotiv ran deep technological analysis and provided a new architecture plan. Our team decided on Big Data technologies such as Scala, Spark, and GraphX for the solution. The client didn’t stop the original project, so we were supporting it and creating another one at the same time. Later on we performed full data migration from the primary solution to the new one and then the client stopped the original project. Our web technologies stack comprised Java and Spring for back-end and React for front-end.
Our client had an agile (scrum) development approach and we were essentially an extension of the core team. The management process included three-week sprints, scrum meetings, backlog refinement, sprint planning, etc.
- New architecture design
- Back-/front-end development
- Data migration
- Quality Assurance
Having partnered with Geomotiv, the client was able to rebuild their entire system to the new business requirements. A specialized Big Data stack helped ensure scalability and fault tolerance of the delivered solution, which original Python-based platform couldn’t provide. With that system, the client managed to reduce the costs of additional support and provide their users with a greater scope of services.
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