Topline for any business is important for growth, hence the performance of sales operations process needs focus. The case study takes a deep dive into the horizontal process of sales operations in home insurance sector with help of statistical techniques and data science. The study involves identifying different performance metrics of the process and applying various hypothesis tests (Wilcoxon, Kruskal–Wallis and Chi-Square) and supervised machine learning (logistic regression) for prescriptive and predictive (Random Forest) purpose to get key thresholds for measuring and setting effective business targets for improving sales outcomes. The case indicates that effective recommendations can be implemented from statistical analysis of process parameters like response time for leads, number of interactions, aging of the leads in pipeline, sales agent performance in terms of sales conversion and revenue. The accuracy of the models (more than 71%), the values of probability ratios (odds ratio) and significance of tests showing impact of critical factors not only enable strong recommendations but also impacted the revenue by more than fifty percent. This study demonstrates the effectiveness of using data-driven scientific approach and our chosen analysis methods to evaluate sales operations processes in services sector, particularly B2C and D2C businesses. By adapting the input parameters while maintaining similar process parameters, these methods can be leveraged to drive efficiency and improve business outcomes, topline across various services businesses.