Customizing Odoo CRM for Pipeline, Opportunity, Project Delivery, and After-Sales Management in a Project-Based Service Company: A Case Study of PT XYZ

Ferri Fatra; Fadhil Muhammad Akbar; Rohimin Imani Arti; M. Galih Fikran Syah

doi:10.62123/aqila.v3i1.194

Authors

Ferri Fatra Telkom University
Fadhil Muhammad Akbar Telkom University
Rohimin Imani Arti Telkom University
M. Galih Fikran Syah Telkom University

DOI:

https://doi.org/10.62123/aqila.v3i1.194

Keywords:

CRM, Design principles, Design Science Research, Odoo CRM, Pipeline management

Abstract

Project-based service companies manage long sales cycles, multi-channel prospects, quotation revisions, project handover, delivery monitoring, and after-sales opportunities. This study designs and demonstrates a customized Customer Relationship Management (CRM) artifact using Odoo CRM for PT XYZ, an anonymized project-based service company, following Design Science Research. The artifact instantiates an end-to-end pipeline (New Lead, Qualified, Needs Analysis, Proposal/Quotation Sent, Negotiation, Won/Contract Signed, Handover to Delivery, Project Delivery, After Sales/Retention, and Lost) together with opportunity fields, stage-gated documents, activity-based follow-up, quotation conversion, lost-reason capture, and reporting dashboards. Rather than treating the configuration as the result, the study abstracts it into six transferable design principles: stage completeness, stage-gated evidence, mandatory next action, commercial-to-delivery continuity, loss-as-learning, and decision-support reporting. The associated benefits, such as follow-up discipline, quotation traceability, handover quality, delivery visibility, and forecasting support, are presented as design propositions with proposed measurement indicators rather than empirically demonstrated outcomes, and the artifact is evaluated ex ante against defined criteria. The contribution is twofold: a replicable Odoo configuration and a nascent set of design principles for project-based service CRM. Because the evaluation is ex ante, future work should validate the artifact through user acceptance testing and longitudinal performance data.

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