No-Code vs Traditional Machine Learning for Lead Generation: A Comparative Case Study

This research paper compares no-code machine learning (ML) platforms, specifically AWS SageMaker Canvas, with traditional Python-based ML methods in the context of lead generation for a direct selling company. The study examines each approach's performance, cost-effectiveness, ease of use, effort and compatibility for various business scenarios using a real case study of a direct selling company with 40000 consultants and 10 million customers. It reveals that while traditional ML delivers improved performance (22% higher conversion improvement), it also demands specialized skills, significant development time and pipeline management. On the other hand, no-code solution offers faster implementation (12 vs. 18 weeks) and higher ROI (2566.67% vs 388.14%), while also enabling business users with minimal technical background to build and deploy predictive models efficiently. This research also helps companies to make informed decisions about their ML strategy and implementation for lead generation. Based on the findings, the study recommends using No-code ML platforms when speed, ease of use, and lower costs are prioritized; opting for traditional ML methods when business needs demand high customization, advanced analytics, and detailed model transparency and considering a hybrid approach to leverage the strengths of  both solutions by prototyping quickly with no-code tools and deploying robust, scalable solutions using traditional ML techniques.[1][2]