Segment AI is a powerful marketing and sales tool. It uses data science algorithms to sort lead data into segments based on various criteria. As a result, it is possible to target each segment with special offers, increasing the efficiency of marketing and sales campaigns.
Our client had to deal with improper lead segmentation daily. He came with an idea of a product that could solve the problem for himself and thousands sales and marketing professionals worldwide. His decision was to use the power of data science to sort leads fast and exactly based on various available data.
The product owner hired a team to build a Python-based module which was responsible for sorting lead data.
Having certain expertise in web design, our client has created the user interface for the MVP version of his application
The data science module was not enough to build a competitive product. It lacked an attractive frontend and a powerful and reliable backend to run properly. Our task was to build a strong system around the existing data science module. The process involved:
It was important to build a responsive application that would work on a variety of devices with different screen sizes. This was achieved with Bootstrap. We also had to consider how the sorting results would be displayed on the screen. React Bootstrap Datatables, Masonry and D3 were used to achieve different display modes.
To build a reliable infrastructure around the core module, we used Laravel, a powerful PHP framework. Amazon Web Services, as one of the best cloud solutions currently available, was chosen to host the application. In addition, we implemented synchronization with Salesforce, and other major CRM systems follow.
We extensively tested the whole system to ensure that the software had no major bugs that could affect user experience. In addition, automated testing was implemented to audit the functionality of the product.
The process of creating a software product always meets challenges, especially when working with new technologies. Our development team willingly accepted these challenges and managed to release a competitive web application.
As the product development was the initiative of a sales and marketing enthusiast, the budget for the project was limited. It also wasn’t clear whether it was worth investing an impressive sum of money to build a full-feature application. So, the team agreed on MVP development, which allowed us to start testing the product with real users and release it in a short amount of time.
The project required complex initial settings, such as the installation of a specific version of Python, data science libraries, queues management tools Redis and Supervisor, backend framework Laravel/PHP and the database MySQL. Installing, setting up and testing in the new environment was a time-consuming process. There were also conflicting dependencies in the staging environment. To solve this problem, we used Docker containers.
The data science module was performing complicated computations. As a result, the application had a slow response time. Our solution was to implement queues which allowed to separate computations into parallel processes. It significantly increased segmentation speed and as a result, offered better performance of the app.
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