Drew’s Database Had Contacts. What It Lacked Was Inventory.

A Real Estate Database Audit Case Study: How Revaluate Repaired 2,320 Missing Addresses and Increased Actionable Records by 246%

In America’s heartland, relationships still drive a large percentage of residential real estate business. Past clients, referrals, and reputation matter. But over time, the databases behind those relationships often become fragmented, especially for growing teams that have accumulated years of internet leads, brokerage contacts, referrals, past clients, and multiple CRM imports.

That was the situation Drew Deck found himself in.

Drew is part of a second-generation real estate team in Lawrence, Kansas with ReeceNichols Preferred Realty, part of the HomeServices of America and Berkshire Hathaway family of companies. Over the years, the business had accumulated contacts and relationships from Follow Up Boss, brokerage leads, YLOPO buyer leads, internet inquiries, family-team records, referrals, and historical exports. The CRM had grown to more than 8,000 contacts and relationships. On paper, the database looked substantial. Operationally, Drew knew it was fragmented.

Large Database ≠ Usable Database

Like many long-running real estate teams, the database was not built intentionally. It accumulated over time. Some records had emails but no addresses. Others had phone numbers but no homeowner visibility. Duplicate records existed across multiple lead sources, and some contacts had moved out of market years ago. Email addresses quietly continued to decay, eroding deliverability and marketing performance in the background.

Drew already knew the database needed work, but the scale of the problem was difficult to quantify manually.

“I just needed some way to analyze this huge chunk of people that I had no idea where to start.”

That challenge is increasingly common for teams using Follow Up Boss and similar real estate CRMs like Lofty. Teams continue adding contacts and relationships every year, but very few stop to audit homeowner visibility, geographic relevance, duplicates, invalid records, or operational usability. The result is often a database that grows larger while becoming less actionable.

The Missing Inventory Problem

Location, location location. In real estate, inventory is actually simple, its addresses. Without addresses, many contacts cannot be tied back to actual homeowner opportunities. A CRM may contain people and relationships, but much of the inventory remains invisible and inaccessible. That was one of the largest issues uncovered during Drew’s audit.

The initial Revaluate Report Card identified 5,676 incomplete records, 5,005 contacts without valid addresses, 1,693 contacts without valid phone numbers, 1,651 contacts without emails, and more than 400 suspicious contacts. The database received an F grade.

The biggest surprise for Drew was not the total number of contacts. It was how few were actually complete and actionable.

“I was shocked how low that was.”

Before repair, Drew’s CRM contained only 1,234 complete mailing addresses and just 837 fully actionable records containing a first name, last name, email, phone number, and physical address. That operational gap matters because a contact is not necessarily a relationship, and a relationship is not necessarily tied to usable inventory.

Many of the records in Drew’s database originated as buyer leads from portals and internet inquiries. By appending missing homeowner information and physical addresses, many of those contacts became substantially more useful and visible inside the CRM, effectively moving further down the value funnel from raw contact toward actionable inventory.

Revaluate ultimately repaired and restored 2,320 mailing addresses, increasing Drew’s complete homeowner address inventory by 188%.

The Repair Process

Revaluate merged multiple historical datasets and standardized the database into a more usable structure. The process included deduplicating records, formatting fields to industry standards, appending missing addresses, repairing missing emails and phone numbers, identifying suspicious contacts, flagging invalid emails, and syncing cleaned records back into Follow Up Boss.

The turnaround time for cleanup and repair was approximately three business days. Implementation inside Follow Up Boss took Drew roughly one afternoon.

“I thought it’d be a little bit heavier lift.”

The operational impact was immediate.

“Doing it all in one fell swoop felt really great.”

Visibility Changed The Database

One of the most revealing outputs from the repair process was the geographic heat map generated after address repair and enrichment. Before the cleanup process, much of the database lacked usable geographic visibility. After repair, clear concentrations emerged around Lawrence, Kansas City, Wichita, St. Louis, and migration corridors extending toward Denver, Dallas, and Chicago.

The Data Audit heat map indicated a wide spread of addresses in Drew’s database.

That visibility immediately created operational value. Drew could now identify homeowner concentrations, separate local opportunities from out-of-market contacts, prioritize likely listing opportunities, identify referral potential outside Kansas, and reduce wasted marketing spend. The database became easier to classify operationally because not every contact needed the same workflow anymore. Some became listing opportunities, others became nurture relationships, referral opportunities, or exclusion candidates. Prior to the repair process, that level of visibility simply did not exist.

The Time Cost Of Manual Cleanup

One of the strongest themes throughout the project was not technology. It was time.

Prior to working with Revaluate, Drew had already spent months manually trying to improve the CRM himself by searching for missing information, reviewing duplicates, appending homeowner details, organizing records, and identifying opportunities. But manual cleanup at scale becomes operationally unrealistic for most producing agents.

Metric | Before | After | Improvement
Complete Mailing Addresses | 1,234 | 3,554 | +2,320 / +188%
Actionable Records | 837 | 2,900 | +2,063 / +246%
Valid Emails | 6,411 | 6,800 | +389 / +6%
Valid Phones | 6,383 | 6,778 | +395 / +6%

“That would’ve taken me a year to do manually at nearly a full-time job.”

That is one of the hidden costs of neglected CRM data. Not simply poor records, but operational drag, lost focus, wasted outreach, fragmented follow-up, reduced visibility, and opportunity cost. Most agents are already paying for database decay. They simply do not see the invoice directly.

The Results

The measurable improvements were significant.

Beyond the raw numbers, the database became more geographically visible, easier to segment, easier to prioritize, and easier to market to operationally. More importantly, it allowed Drew to focus more time on homeowner opportunities and less time trying to manually repair years of accumulated CRM decay.

From “Spray And Pray” To Structured Workflow

Prior to the cleanup process, much of the outreach process lacked prioritization.

“We were just spraying and praying and sending stuff everywhere.”

After repair and segmentation, Drew rebuilt the workflow around smart lists, homeowner prioritization, local market filtering, likely mover scoring, segmented outreach, and listing-focused follow-up. The database became operationally usable.

“It structures my day.”

Today, Drew begins each morning reviewing prioritized homeowner opportunities instead of broadly working disconnected lead pools. Past clients, likely movers, homeowners, renters, agents, and out-of-market contacts can now be segmented differently based on actual business value. The CRM stopped functioning as a giant contact repository and became a more structured inventory and relationship system.

The Bigger Insight

Most agents do not have a lead problem. They have a visibility problem.

Over time, CRMs quietly accumulate fragmented records, duplicate contacts, incomplete homeowner data, invalid emails, disconnected relationships, and hidden inventory. Because the decay happens gradually, many teams overestimate how usable their database actually is.

Drew’s database did not look broken at first glance. But once the repair process was complete, it became clear how much homeowner visibility, segmentation, and operational structure had been missing. The opportunity was already inside the CRM. The database simply lacked the organization and visibility needed to act on it efficiently.

About The Audit

Revaluate analyzed and repaired exported CRM records provided by Drew Deck’s team, including historical lead sources, internet leads, and Follow Up Boss exports. The database audit evaluated database completeness, homeowner visibility, address quality, duplicate records, suspicious contacts, invalid emails, and overall operational usability.

Learn more about Revaluate’s real estate database audit and CRM data repair process.
revaluate.com/audit

Written by Chris Drayer, CEO of Revaluate
Drew Deck database audit performed April 2026
Quotes in this case study were taken from an interview with Drew Deck conducted in May 2026.