Custom web & mobile development
SIMBA
Technologies
Python React
marketing mix modeling platform for in-house marketing measurement
The client aims to build and maintain a full-fledged platform around open-source marketing mix models, focusing on maximizing their efficiency and simplicity of use.
Proceeding from the prototype of the platform made by our client via ChatGPT, our main challenge encompassed further development, enhancement, and extension of an MMM platform functionality.
Process
Background
1749.io is a marketing analytics consultancy that empowers businesses to adopt a data-driven approach to marketing. They utilize a suite of innovative, often open-source tools to analyze and interpret marketing-related data, helping their customers integrate those solutions into routine workflows. One of their key areas of focus is development, maintenance, and improvement of machine learning-based models — known as marketing mix modeling (MMM) — designed to facilitate in-house marketing effectiveness measurement and future performance forecasts.
Development
The cornerstones of the development process include:
– Robust system architecture. We separated the existing monolith platform into independent frontend, backend, and machine learning (ML) parts to simplify further development, enhancement, support, and modernization of the platform.
– Mockups creation. Proceeding from our expertise in UI/UX design and understanding of user-centric design principles, we created mockups for every screen. To further implement those mockups, we took an open-source React component library with ready-to-use material design components that allowed us to simplify and speed up the process of MVP development.
– Functionality development. Together with the client, we defined and prioritized the main platform features to be added or enhanced. Overall, we contributed to the enhancement of the model management system by implementing intuitive model organization, added the enhanced visualization tools, developed an asynchronous model fitting, and enabled a simultaneous model processing.
– Production. Our team selected the right instances to ensure the best possible performance of ML models. Also, we set up CI/CD pipelines to secure the deployments and adopted the blue-green strategy.
Result
Our iterative approach — based on incorporating users’ and client’s feedback at each stage of platform development — ensured that the final version of the product not only met technical specifications but also aligned well with users' demand. We tailored the system architecture, particularly backend, to machine learning workflows that significantly improved platform performance. Now, end-users can easily access, store, manage, analyze, and fit multiple models while performing other tasks within the platform simultaneously. Guided by the principles of simplicity and ease of use, our team enhanced the platform’s UI/UX making even the advanced features accessible to users of all experience levels. Additionally, we added a wide variety of output charts with consistent colors and clean designs that enable users to better understand insights and tailor their visualization options to specific data types.
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Team
Backend developer Frontend developer QA specialist
Project manager
Feedback
Niall Oulton
CEO at 1749
Our primary goal was to refine and enhance our marketing mix modeling platform developed in collaboration with Jellyfish.tech. The project focused on two critical areas: the Model Management System and User Interface (UI) enhancements, with a special emphasis on the asynchronous fitting of models.
Jellyfish.tech played a pivotal role in this project, bringing their expertise in UI/UX design and system architecture to the forefront. Their understanding of user-centric design and efficient system workflows was instrumental in achieving our project goals.
The enhancements made by Jellyfish.tech to the Model Management System and UI, particularly the asynchronous fitting of models, have dramatically improved platform's usability and performance. Users now enjoy a more intuitive interface, streamlined model management, and faster processing times, which collectively contribute to a more efficient and productive marketing analysis experience.
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