CRM Data Cleanup & Automation for a Commercial Real Estate Trust

Company Overview

This engagement involved a publicly traded commercial real estate investment trust that manages a portfolio of postal and logistics properties across the United States. With operations spanning multiple states and a growing roster of tenants, brokers, and service partners, the organization relies heavily on its CRM to coordinate sales outreach, track deal flow, and manage stakeholder relationships.

As the company scaled its portfolio, the volume of contact records in HubSpot grew rapidly — but without a structured data governance process in place, the quality of that data deteriorated just as quickly. Leadership recognized that unreliable CRM data was undermining the accuracy of revenue operations reporting and creating friction across the sales pipeline.

The Challenge

The core issue was straightforward but deeply entrenched: the CRM contained 6,900+ duplicate contact records, and there was no existing deduplication process to prevent new duplicates from forming. Over time, contacts had been entered by multiple team members, imported from trade show lists, and pulled in through integrations — all without consistent formatting or validation rules.

The consequences of this data sprawl extended well beyond a messy contact list. Sales representatives were unknowingly reaching out to the same prospects multiple times, creating a disjointed experience for potential tenants and partners. Revenue operations reports — which leadership depended on for forecasting and strategic planning — were skewed by inflated contact counts and fragmented deal histories.

Compounding the problem, the team maintained critical prospecting data in Microsoft Excel spreadsheets that needed to be integrated back into HubSpot. These spreadsheets had their own inconsistencies: non-standard field names, inconsistent formatting for phone numbers and addresses, and no clear mapping to HubSpot's property structure. The gap between the Excel-based workflow and the CRM meant that field prospecting insights were effectively siloed from the rest of the organization's data.

Coordination was also a factor. The cleanup effort required alignment across multiple stakeholders — including the VP of Sales, the Sales Director, and the Operations team — each of whom had different priorities and visibility into the data.

Our Approach

We began with a comprehensive audit of the HubSpot CRM to assess the full scope of the duplication problem. This involved analyzing contact records by creation source, identifying the most common duplication patterns, and mapping the relationships between duplicate records and their associated deals, companies, and activity histories. The goal was not just to merge duplicates, but to ensure that no meaningful data — notes, emails, logged calls — was lost in the process.

From there, we established a milestone-based tracking system to manage the cleanup in structured phases. Rather than attempting to process all 6,917+ duplicates at once, we broke the work into manageable batches, prioritizing records tied to active deals and high-value accounts. Each batch went through a review cycle before merges were finalized, giving the sales team confidence that their pipeline data remained intact.

In parallel, we tackled the Excel restructuring effort. This meant standardizing column headers, normalizing data formats (state abbreviations, phone number formatting, postal codes), and building an import-ready structure that aligned with HubSpot's property schema. We documented every transformation so the team could replicate the process for future imports without external support.

Cross-functional coordination was managed through regular check-ins with the VP of Sales, Sales Director, and Operations. We provided progress dashboards and flagged any records that required human judgment — for example, cases where two duplicate contacts were associated with different companies and the correct association was ambiguous. This collaborative approach ensured buy-in at every stage and prevented post-cleanup surprises.

Finally, we developed data quality standards documentation — a set of guidelines covering naming conventions, required fields, and deduplication protocols — to help the team maintain clean data going forward.

Tech Stack

• HubSpot CRM — Primary contact and deal management platform; the target environment for all cleanup and standardization work
• Microsoft Excel — Used for field prospecting data management, restructured and standardized for CRM integration

Key Deliverables

• 6,917+ duplicate contacts processed, representing approximately 46% of the entire database — each record reviewed, merged, or resolved with full activity history preservation
• Excel restructuring — Field prospecting spreadsheets reformatted, standardized, and mapped to HubSpot properties for seamless import
• Milestone tracking system — A phased project management framework that provided visibility into progress and allowed stakeholders to review work at each stage
• Data quality standards documentation — A reference guide for the internal team covering field formatting rules, deduplication protocols, and import procedures

Business Outcomes

The most immediate outcome was the resolution of 6,917+ duplicate contacts, which gave the organization a clean, trustworthy contact database for the first time in years. Sales representatives could now search for a prospect and find a single, complete record rather than multiple fragmented entries.

With duplicates eliminated and data standardized, RevOps reporting achieved 100% accuracy. Leadership could trust the numbers in their pipeline reports, forecasting models, and territory analyses without needing to manually verify or cross-reference data. This had a direct impact on the speed and confidence of strategic decision-making.

The project also delivered a significant efficiency gain: an estimated 2 months of internal man-hours saved that would have otherwise been spent on manual data reconciliation, duplicate hunting, and report troubleshooting. Those hours were redirected toward revenue-generating activities and strategic initiatives.

Perhaps most importantly, the data quality standards and documentation left in place ensured that the cleanup was not a one-time fix but the beginning of an ongoing governance practice.

What the Client Said

"Very happy with the work product, understanding, and technical abilities. Would recommend!"

— Operations Team