Small businesses across diverse industries continue to operate critical customer relationships through a fragmented patchwork of spreadsheets, email clients, and ad hoc to-do lists, an approach that erodes data quality, increases the risk of missed follow-ups, and prevents systematic revenue analysis. Enterprise customer relationship management (CRM) suites such as Salesforce, Pipedrive, and Zoho CRM offer comprehensive functionality but impose recurring subscription costs and learning curves that many small enterprises find prohibitive. This paper presents the design, implementation, and evaluation of an open-source, web-based Intelligent CRM System purpose-built for small business environments. The system is developed using the Django 5.2 framework, the Python 3 programming language, and an embedded SQLite relational database, requiring no commercial licenses, no separate database server, and no proprietary infrastructure. A normalized four-entity relational schema centered on the Customer model, with Lead and Sale entities referencing Customer through cascading foreign keys and a Task entity supporting operational scheduling, captures the complete small-business customer lifecycle in a single coherent platform. Django's integrated administration interface is leveraged to deliver immediate CRUD functionality across all four modules, eliminating the development overhead of bespoke front-end code while providing search, filtering, and role-based access controls out of the box. An empirical evaluation conducted with three representative small-business users on 50 simulated transactions shows that the proposed system reduces average customer-lookup time by 76 percent, lead-status update time by 79 percent, and improves cross-record data consistency from 62 percent to 96 percent compared with a spreadsheet-based baseline. The system demonstrates that an open-source web framework combined with a disciplined relational schema can deliver an immediately deployable CRM platform whose modular architecture supports incremental enhancement toward dashboards, role-based access, and predictive analytics.
Sirisha et al. (Thu,) studied this question.