11 seller leads on autopilot while on vacation
Generated 11 motivated seller leads on autopilot while on vacation. CRM automation made it possible.
About
From the grind at a grocery warehouse to mastering New Jersey's real estate scene, AJ's story is one of true grit meets innovation. He ditched the chaos of spreadsheets for something that actually made sense.
Operating market
New Jersey
Favorite features
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The Challenge
AJ started in a grocery warehouse freezer, headphones in, listening to Tyler while he plotted a way into real estate. His first system was a Google Sheet. It was slow, it was tedious, and it could not tell him how many times he had called anyone. He took notes and hoped.
The business itself ran on chaos and luck. Dialing, smiling, and never knowing when the next deal would show up, then nursing every lead by hand. New Jersey foreclosures made it harder: sellers get blitzed with around 200 touches in the first two weeks and stop answering the phone. Being the fifteenth caller on a beat up list was not a business. He needed the records nobody else had reached yet, and a machine that kept working when he stepped away.
What They Built
A data pipeline that runs overnight
Fresh foreclosure records come in five days a week, whenever the state releases them. His full time data VA spends about four hours cleaning a typical 40 record day and loads everything into DataSift overnight. Skip tracing happens right inside the platform, so numbers are attached before anyone dials.
I'm blessed. I have a full-time data VA. She's excellent and she's very cheap.
First contact before he is even needed
A second VA opens each morning with the first to market presets and dials every new record. Each touch runs the full flow: call, voicemail, text, logged as one attempt. When she reaches someone, she warm transfers or tasks the lead over. AJ only enters the picture once there is a real conversation.
Exhausted records as a second mine
In his target counties, records that die after three attempts or bad numbers do not get archived. The data VA digs back in, pulls a second set of numbers from another skip provider, and hunts relatives. His Middlesex deal started exactly there: a failed third attempt, then she found the younger brother, and the rapport she built carried the deal.
They weren't getting called. That's what I'm trying to tell you.
Sequences that do not care where he is
Tags and statuses trigger sequences, sequences create tasks, and tasks land on the right person every day. Leads move across his board through 15 to 45 touches until there is a contract, a referral, or an endless drip. That is the machine that stacked up 11 leads while he was at a mastermind in Florida.
Before and After
Before DataSift
- Achieved Data Clarity
- Consistent Deal Flow
- Inconsistent Deal Flow
- Poor Marketing Flow
- Little Data Clarity
- Relied On Google Sheets
- Consistent Deal Flow
- Data Clarity / Organization
- Wholesales, Flips And Does Retail
- Strong Marketing Flow
- Didn't Understand Data Management
- Strong Real Estate Agent
- Wanted To Break Into Wholesaling But Didn't Know How
- Healthy Scale
- Data Driven Decisions
- Consistent Deals
- Strong Marketing Flow
- Inconsistent
- Little Data Insight
- Unorganized Bulk Marketing
- Strategic Focus on Niches
- Enhanced Lead Flow and Certainty
- Streamlined Marketing Process
- Efficient Use of Data
- Direct Mail & Deep Prospecting
- Broad, Unfocused Strategy
- Inconsistency in Results
- Dependency on Luck
- Marketing Clarity
- 6 Figure Months
- High Deal Volume
- Strong Marketing Flow
- Proper Data Management
- Weak Marketing Flow
- Poor Data Management
- Lack of Focus
- Manual Efforts
- Successful Scale
- Total Data Clarity
- Clear Marketing Approach
- Large Deal Spreads
- Improper Data Management
- Poor Marketing Method
- Inconsistent Deal Flow
- Strong Marketing Flow
- Clear Data Management
- Clear Data Management
- Successful Scale
- Many ways to close deals
- Large deal spreads
- Poor data management
- Unable to scale properly
- Poor marketing flow
- Smaller Deal Spreads
- Increased Deal Volume
- Platform Utilization
- Realtor Collaboration
- Funding Efforts
- Lots Of Deals
- Data Driven Decisions
- Strong Marketing Flow
- Sales And Marketing Clarity
- Poor Data Management
- Poor Marketing Flow
- Relied On Bulk Marketing
- Lack of Clarity
- Automated Acquisition
- Strong Marketing Flow
- Data Driven Decisions
- Structured Follow-ups
- Increased Lead Generation
- Achieved Sales And Marketing Clarity
- Manual Acquisition Processes
- Ineffective Marketing Flow
- Minimal Lead Insights
- Poor Follow Up Flow
- Inconsistent Lead Generation
- Lacked Sales And Marketing Clarity
After DataSift
- A VA Works Every No-Contact Lead in DataSift
- Daily Follow-Ups Run From the Sift Line Tasks
- Nine Deals Under Contract After the Stacked Niche Switch
- Cold Callers Sent KPIs by Hand Outside the System
- Expensive PPC Spend With No Return
- Follow-Up Leads Vanished Into an Abyss in the Old CRM
- 60,000 Records a Quarter Feeding His Bulk Callers
- Four Contracts Signed in 10 Days
- Three Deals a Week, Two Weeks Running
- First Direct to Seller Push in 30 Years of Business
- A $10,000 Mail Campaign That Produced Nothing
- 15 Years Buying Only From the MLS and Wholesalers
- New lender agreed 12 days after the first collapsed
- $105,696 net profit after closing costs
- Nine properties contracted at $360K, sold at $450K
- Hard money lenders would not lend on rural portfolios
- Local title companies refused assignments and passthroughs
- Seller would only sell one house, priced far over appraisal
- Still Runs on a $500 Monthly Budget
- An $85K Deal Off a 50-Name Stacked List
- 13 Contracts in Six Weeks From 250 Prospects
- About $40K Spread Across Six Months, Still Behind
- Five Contracts in a Row Fell Through
- Shotgun Lists Across Too Many Zip Codes
- One Contract Per 25-35 Leads, Half the Typical Ratio
- Each Caller Brings 16-18 Leads a Week
- 50,000 Free County Records Called on a Four-Month Cycle
- Two Partners Dialing Everything Themselves From 2017 to 2019
- VA Cleaned Every County Download by Hand in Excel
- Another Blanket ListSource Pull Whenever the Pipeline Ran Dry
- Same county-data system expanding into Texas and Miami
- $800K in revenue from six or seven deep-research deals
- About 20 people across data, calling, research, and sales
- PropStream tax delinquent lists arrived after owners had already paid
- Two years hunting a golden list that did not exist
- Bandit signs at intersections, cold calling all day from a coffee shop
- Four deals closed faster than his first three
- 8,200 dials and 25 leads in one November
- Four VAs running one calling and texting protocol
- Generic YouTube advice with no sequence to follow
- Back at a sales job four months after going full time
- Spent $35,000 on marketing with zero return
- About 35 Leads Per Deal Across 347 Total Leads
- $253K In Cash And Equity From One Package Deal
- 40 Properties Wholesaled In Under Four Months
- 12-Hour Days And A 45-Minute Commute
- No Deals Found Through Realtors, MLS, Or Craigslist
- Cold Calling From A Printed Spreadsheet In The Parking Lot
- Pulls Nearly All His Data Straight From the County
- Runs Jacksonville Virtually on $1,000 a Month
- Locked a Contract Four Days After His January Restart
- Scattered Across SMS, Facebook Ads, and a Website
- Partner's $2 an Hour Caller Hit a 1% Contact Rate
- Called Lists Three Times Then Trashed Them
- 50,000 sqft warehouses in negotiation, one to two leads daily
- $59,000 spread on his first big wholesale deal
- Every eviction filed in New Jersey, worked daily
- Old CRM buried callers in highlight and right-click drama
- Phenomenal months, then crickets for months
- Overnight pharmacy shifts, 84 hours in a single week
- Knows the playbook works and is replicating it
- First off-market contract four months after starting
- $250,000 assignment fee on deal one
- Weak cash position after funding ground-up projects
- Questioned whether real estate money was even real
- Two years spending on development deals, not making much back
- $40,000 month tracking with fixed costs
- Five first to market deals since April
- Probate filings pulled daily in multiple counties
- Two mail campaigns, response rates he did not like
- Costs fluctuated month to month with list spend
- Custom Podio build, broad absentee and vacant lists
- Owner buried beside her son after two years unclaimed
- Runs every heir search solo now
- Five heirs found, contacted, and signed across Texas
- Needed a specialist for her first deep heir search
- Paper records only, fathers listed by initials
- Probate leads dead-ended with no findable heirs
- Every No Recycled On A Two-Year Rehash Cycle
- Three VAs Clean Data Before A Single Dial
- Three First To Market Niche Filters Worked Daily
- Loose Onboarding And Under-Corrected Bad Hires
- Team Swung From Large To Small To Solo
- Broad Marketing Across Every Niche At Once
- Mornings and evenings back with his family
- 8 person crew handles whole projects in sets of two
- 40+ properties across three portfolios in about six months
- Afraid to take on payroll and a crew
- The bottleneck on his own projects
- Software job eight to five, real estate five to midnight
- 55% Answer Rate On One-By-One Click To Call
- Free County Data Replaced Every Paid List
- $45K Fee Closed One Week After The Callback
- Podio, Slack, And Multiple VAs, No Traction
- 30,000-Record Lists Dialed Two Or Three Times Through
- At Least $40K Wasted On Lists And VAs
- Four Marketing Attempts on a Couple of Lists
- A Three-Person Team at About $1,800 Cost Per Contract
- Almost Two Years Nomadic Across Asia, Europe, and the Middle East
- Behind a Jacksonville Desk 95 Percent of the Time
What changed
While working in a grocery warehouse, AJ's engagement with Tyler's content inspired him to join DataSift, marking the start of his transformative journey in real estate.
The Results
The headline number is small and it matters: 11 leads from two dial days, Thursday and Friday, waiting when he got home from Florida. Nothing paused because the owner left. The whole operation runs on $2,000 to $2,500 a month in overhead.
The Middlesex pre-foreclosure deal shows the depth. The record came in in September and went exhausted on the first set of numbers. It became a lead about two weeks later when his VA found the brother. Contract in September, closed January 31 after months of title work across three heirs. Before DataSift he tracked calls in a Google Sheet and ran on luck. Now the KPIs flag a problem before it becomes a missing contract.
It is very hard work, but the steps are simple.
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