The Customer Acquisition Cost (CAC) Payback Period is one of the key indicators in determining the effectiveness of marketing strategies and sales, especially in Software-as-a-Service (SaaS) and other subscription business models. The existing methods of CAC payback analysis are usually based on aggregate data that does not correct to temporal and behaviour heterogeneity of cohorts of customers. The study presents an automated and machine learning capable framework of cohort analysis which could be used to optimize the CAC Payback Period. The strategy to be proposed will utilize the concept of temporal segmentation, behavioural track, and predictive modelling to link the costs of an acquisition with the revenue inflow more closely in real time. In several SaaS datasets that have been tested empirically, cohort-based insights cause the payback period to shrink, increased customer lifetime value (CLTV), and increased marketing return on investment (ROI). The theoretical frames, the methodology of the implementation, international best practices and strategic challenges are provided in the paper, with explanations offering a complete handbook to practitioners and researchers who want to enhance the efficiency of their marketing by utilizing the information on customer groups.