Sales Funnel Diagnostic and Lead Scoring Model for B2B SaaS Firms

Objective: To develop a data-driven sales funnel diagnostic and lead-scoring model to improve conversion rates for an Indian B2B SaaS company.

Real problem & context: Many Indian B2B SaaS companies face challenges in identifying high-potential leads and optimizing their sales funnels, leading to low conversion rates and inefficient use of sales resources. The project addresses the need for systematic diagnostics and prioritization of sales opportunities using data-driven methods.

Approach/methodology: The student team will analyze CRM and sales data to diagnose funnel leakages, map customer journeys, and build a lead scoring algorithm using statistical and machine learning techniques. The methodology includes stakeholder interviews, benchmarking, and application of frameworks such as AIDA and BANT.

Deliverables & analysis: Key outputs include a funnel diagnostic report, a lead scoring model (Excel/Python), actionable recommendations for process improvements, and a dashboard prototype (Tableau/Power BI) for ongoing monitoring. The analysis will be reviewed with industry mentors and validated through historical sales data.

Business impact & decision it informs: The project enables data-driven prioritization of leads, streamlines sales processes, and informs management decisions for resource allocation and improved sales performance, ultimately boosting revenue and operational efficiency in a competitive SaaS market.