How RV Nirmaan Transformed CRM Operations with a Clean Data Foundation and Scalable Pipeline
We knew our CRM data was slowing us down, but we didn’t realize just how much. The team stepped in, streamlined everything behind the scenes, and gave us the confidence to scale our operations without second-guessing our numbers.
WHAT YOU’LL LEARN
- Why clean, structured CRM data is critical for high-performing sales and marketing teams.
- How to design a scalable data pipeline that feeds into analytics and automation.
- What it takes to resolve months of CRM issues with minimal disruption to daily operations.
THE PROBLEM
RV Nirmaan, one of South India's most trusted real estate development firms, was facing increasing friction in its CRM-driven workflows. With multiple teams inputting data over time and no centralized clean-up mechanism, the CRM system had become cluttered with duplicate entries, inconsistent formatting, and incomplete lead records. Sales reports often conflicted with marketing attribution, and campaign performance could not be reliably measured. The cost of inaction was growing; lost leads, inaccurate forecasting, and erosion of internal trust in data.
THE HYPOTHESIS
The RV Nirmaan leadership believed that the foundation for any effective CRM or marketing automation strategy is clean, trustworthy data. They hypothesized that a complete audit, clean-up, and re-architecture of their CRM pipeline would not only solve current inefficiencies but also unlock future capabilities like personalized nurturing, accurate attribution, and proactive follow-ups.
THE SOLUTION
Gitforce partnered with RV Nirmaan’s sales and marketing leadership to perform a full-stack data overhaul. The engagement included technical deep dives, cross-functional collaboration, and ongoing support to ensure sustainable improvements. The transformation was delivered in six focused phases:
1Audit CRM Data Quality
Gitforce began with a complete scan of the CRM database, identifying data health issues such as duplicate leads, missing contact fields, inconsistent formatting, and outdated segmentation tags. Using custom scripts, they quantified error rates across lead sources and funnel stages to prioritize cleanup areas.
2Resolve Duplicates and Conflicts
They implemented a de-duplication framework combining exact-match logic and fuzzy matching algorithms to consolidate redundant entries. Gitforce also developed rules for record survivorship ensuring that the most up-to-date and accurate information was retained. The result was a single view of each customer with full activity history.
3Rebuild Pipeline Logic
The original CRM funnel contained custom statuses and ambiguous field mappings that made automation brittle and reporting inconsistent. Gitforce standardized lead statuses, mapped historical activities to a new schema, and restructured the pipeline into clear, trackable stages. These changes brought instant clarity to lead lifecycle tracking.
4Engineer Clean Data Feeds
Gitforce wrote modular Python ETL scripts to automate data validation, enrichment, and loading from marketing forms, sales inputs, and third-party platforms. These pipelines were built to run daily and flag anomalies proactively ensuring data integrity from the moment of entry.
5Enable Cross-Team Dashboards
With the data clean and structured, Gitforce developed reporting layers to serve Marketing, Sales, and Leadership. They created unified dashboards in Google Data Studio, covering lead generation performance, sales velocity, and campaign attribution—ending weeks of manual reporting.
6Institutionalize Data Hygiene
To make the gains stick, Gitforce designed a playbook for CRM usage, covering input guidelines, field ownership, QA processes, and sync rules with other systems. They also held training sessions with Sales and Marketing staff to align teams on how to maintain the CRM moving forward.
THE IMPACT
- CRM load speeds improved dramatically, with queries executing 3X faster post-cleanup.
- All known duplicate and conflicting records were resolved and consolidated.
- Historical reporting errors across six months of campaign and sales data were corrected.
- Data pipelines now ensure clean records from day one, with automated validation checks.
- Sales and marketing teams adopted a shared reporting language for the first time.