This report presents an exploratory and inferential analysis of a CRM sales pipeline dataset from Dochase ADX. It applies statistical techniques including ANOVA, correlation analysis, and regression modelling in R, supported by Python-based data validation and visualization. The study identifies key drivers of deal value, evaluates pipeline performance across stages, and provides actionable insights for improving sales efficiency and forecasting accuracy.

This report presents a full exploratory and inferential analysis of 135 CRM deal records from Dochase ADX for the 2025 financial year. Using R (Quarto) with integrated Python analytics via reticulate, the study applies five core analytical techniques: Exploratory Data Analysis (EDA), Data Visualisation, Hypothesis Testing (ANOVA and Kruskal-Wallis), Correlation Analysis, and Linear Regression. The objective is to identify the key drivers of deal value within the sales pipeline and translate statistical findings into actionable commercial insights. The analysis reveals that deal value is highly skewed, significantly varies across pipeline stages, and is primarily driven by stage progression rather than time in pipeline. The findings support a stage-based sales management framework, including prioritisation of mid-pipeline deals, improved CRM monitoring, and stage-linked performance tracking to enhance revenue predictability. This work demonstrates applied business analytics combining statistical inference, predictive modelling, and executive-level interpretation for data-driven decision-making in a commercial SaaS environment.