Welcome back to Exit & Equity, the email for serious self storage owners.

Today we're talking about the most frustrating thing in self-storage software: the data you're paying for but can't actually see.

Let’s go!

The timestamp. The unit size they asked about. Where they came from (Google, Facebook, referral). Whether they converted to a rental or disappeared.

All of this data exists. Your PMS captures it.

But when you log in and try to answer basic questions like:

...your PMS gives you nothing.

Or worse, it gives you a generic "Lead Report" that shows a list of names with no analysis, no patterns, no insights.

The data is there. Your software just won't show it to you.

This isn't an accident. Most property management systems are built to handle transactions, thinks like charging rent, tracking leases, processing payments. They're very good at that.

But they're terrible at giving you operational intelligence. They collect thousands of data points per month that could tell you exactly where you're leaving money on the table, which managers are underperforming, which marketing spend is wasted, and which operational changes would have the highest ROI.

And then they bury that data six menus deep with no way to analyze it.

I got tired of this. So I started treating my PMS like a data engineer treats any system: as a source of raw data that needs to be extracted, cleaned, and analyzed outside the vendor's limitations.

Here's what's hiding in your PMS—and how to dig it out.

Your PMS tracks all of this data in the background. The question is: can you get to it?

For each goldmine, I'll show you what insight is hiding, where the data lives (even if your PMS won't report on it), and what decision you can make with it.

What's hidden:

Most PMS systems show you "where leads came from" (Google Ads, Facebook, walk-in, referral). But they don't easily show you conversion rate by source or quality of tenant by source (length of stay, payment reliability).

⛏️ The insight you're missing:

Let's say Google Ads sends 40 leads/month, Facebook sends 30 leads/month.

Standard report says: "Google is winning, spend more there."

But when you extract the raw data:

Worse: When you track length of stay:

You should be spending MORE on Facebook, less on Google, but your PMS just shows "40 vs. 30 leads" and you're doing the opposite.

Where this data lives:

The problem: These are three different reports/tables that your PMS doesn't connect for you.

How to extract it:

What decision this enables:

Reallocate your marketing budget to channels that actually convert AND retain, not just the ones that generate the most noise.

Real example:

When I ran this analysis, I discovered my "Referral" leads had a 50% conversion rate and 22-month average stay vs. "Google Ads" at 12% conversion and 11-month stay.

I cut Google Ads spend by 60% and launched a referral incentive program ($50 rent credit for successful referral). My CAC dropped by 40% and tenant quality improved dramatically.

What's hidden:

You know your occupancy by unit size. But you don't know days-to-rent by unit size, how fast each size moves when vacant.

⛏️ The insight you're missing:

Let's say you're 90% occupied. Looks healthy, right?

But when you extract unit velocity:

You have too many large units sitting empty while small units fly off the shelf.

This reveals "pricing friction":

Where this data lives:

The problem: PMS shows occupancy % but not velocity (time vacant).

How to extract it:

The formula:

Unit Velocity = (Total Days Vacant) ÷ (Number of Rentals)

What decision this enables:

Real example:

One of my facilities had twelve 10x20 units averaging 61 days vacant. I converted six of them into twelve 10x10 units (cost: $8K in construction).

Result: Those 10x10s now rent in 18 days average. Annual revenue increased $23K because I eliminated the slow-movers.

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What's hidden:

Every inquiry in your PMS has a timestamp. But your PMS doesn't aggregate this to show you peak inquiry hours or day-of-week patterns.

⛏️ The insight you're missing:

When you extract the raw timestamps:

Your office is closed when most of your leads come in.

Or your manager is at the facility but too busy with other tasks to respond quickly.

Where this data lives:

The problem: PMS gives you a list of inquiries but no time-based analysis.

How to extract it:

What decision this enables:

Real example:

I discovered 47% of my leads came in after 5 PM. I hired a part-time manager for evening shifts (4-8 PM, four days/week).

Response time dropped from 8 hours to 45 minutes. Conversion improved 23% because we were catching leads while they were hot instead of the next morning after they'd called three competitors.

What's hidden:

You know storage is "seasonal" (summer busy, winter slow). But you don't know your specific facility's pattern or how pronounced it is.

⛏️ The insight you're missing:

When you extract move-in/move-out history by month:

This tells you:

Where this data lives:

The problem: PMS doesn't chart year-over-year patterns or net movement velocity.

How to extract it:

What decision this enables:

What's hidden:

Do tenants who move in during peak season stay longer or shorter than off-season renters? Your PMS doesn't tell you.

⛏️ The insight you're missing:

When you analyze by move-in month cohort:

Off-season tenants are "stickier" (better lifetime value).

This might mean:

Where this data lives:

The problem: PMS shows individual records but no cohort comparison.

How to extract it:

What decision this enables:

Real example:

I assumed summer tenants stayed longer because "that's moving season and people downsizing stay long-term."

Data showed the opposite: Summer cohort averaged 9.8 months, January cohort averaged 18.4 months.

I shifted 40% of my annual marketing budget to November-January (previously considered "dead season"). Acquired fewer tenants but with nearly 2x the LTV. Overall revenue increased because tenant quality improved.

The question most operators ask after realizing this data exists is: "Why doesn't my software just show me this?"

The answer lies in how property management systems are built.

Your PMS was designed as an "Online Transactional Processing" (OLTP) system. Its primary mission: maintain transaction integrity.

When a tenant pays $100, the system must ensure:

To do this efficiently for thousands of facilities simultaneously, the software is optimized for "current state" data—the right here, right now.

Analytical queries—like "Show me average stay length of tenants who moved in on Tuesdays"—require scanning millions of historical records. This is computationally expensive.

If a PMS vendor allowed every user to run complex analytical queries in real-time, the entire system would slow to a crawl, and the transactional integrity (taking payments) would be compromised.

There's also a vendor incentive problem.

PMS companies are evaluated on their ability to handle day-to-day operations. As long as the "Management Summary" provides enough information for a bank audit or tax return, the vendor has met the standard.

Providing deep, data-driven insights requires a separate architecture—often called "OLAP" (Online Analytical Processing)—which is expensive to build and maintain.

Rather than building these sophisticated tools, vendors often:

This leaves the independent operator in a "data hostage" situation: The data is yours, generated by your business, but the vendor has made it nearly impossible to access without paying more or having a computer science degree.

There's also a psychological element.

Software vendors want their products to feel "easy." A dashboard with 50 complex analytical metrics is intimidating to a new manager.

So the "default" reports are simplified to the point of being operationally shallow.

The gold is hidden not necessarily out of malice, but because the vendor has decided most operators don't want to see the complexity of their own data.

They're wrong. You do want to see it. You just need to extract it yourself.

As an operator with a data engineering background, the most important lesson I've learned is this:

You don't need to wait for a vendor to "build" a report. You can "source" the data yourself.

In the tech world, we call this "ETL"—Extract, Transform, and Load.

For the self-storage operator, this is the process of getting the "shovel" and digging into your PMS.

Here's the 4-step process:

The primary tool: CSV Export

Almost every report in SiteLink, storEDGE, and Tenant Inc has an "Export to Excel" or "CSV" button.

The secret: Ignore the "Summarized" reports. Look for the "Detailed" or "History" versions.

Examples:

In storEDGE:
The "Move-Ins/Move-Outs/Transfers" report in CSV format contains columns that don't exist in the PDF version: Business Name, Amenities, Transfer Unit Rate.

In SiteLink:
The "Marketing Roll" is a goldmine of longitudinal data that can only be properly analyzed once exported.

In Tenant Inc:
The "Audit Log" has manager override data that never appears in summary reports.

What to export this week:

These four exports will unlock 4 of the 5 goldmines.

Once you've exported raw data, you need a "Staging Area."

For most independent operators, this should be a Master Google Sheet or Excel workbook.

You're not just looking at the data—you're "mirroring" it outside the PMS so you can manipulate it without breaking the system of record.

The transformation process involves "cleaning" the data:

Date formatting:
Ensure all dates are in consistent format (MM/DD/YYYY or YYYY-MM-DD)

Source standardization:
Your PMS might have "Google," "Google.com," and "Google Search" as three different sources. Merge these into one "Google Organic" category.

Remove blanks:
Filter out any rows missing critical data (no phone, no date, no unit size)

Create calculated columns:

This is tedious but crucial. Garbage in = garbage out. Clean data = reliable insights.

This is where the magic happens.

The Pivot Table is the single most powerful tool for an operator.

It allows you to "re-slice" your data in ways the PMS vendor never imagined.

To find Unit Velocity:
Pivot by "Unit Type" and "Average of Days Vacant"

To find the January Effect:
Pivot by "Move-In Month" and "Average Length of Stay"

To find Manager Overrides:
Pivot by "Manager Name" and "Sum of Discounts Given" or "Average Discount %"

To find Lead Source Performance:
Pivot by "Lead Source" and calculate "Conversion Rate" (Count of Converted ÷ Count of Total)

By using a pivot table, you're essentially building your own "Analytical Database" on top of the transactional mess provided by your vendor.

You're no longer asking the software for a report. You're telling the data to give you an answer.

For operators with more than 5 facilities, manual CSV exports become a burden.

This is where APIs (Application Programming Interfaces) come into play.

An API is a "digital handshake" that allows your spreadsheet to talk directly to your PMS.

Systems with good API access:

What you can automate:

If you find yourself exporting the same CSV every Monday morning, it's time to look at API automation.

Tools like Make.com or Zapier can connect your PMS API to Google Sheets, Monday.com, or Slack with no coding required.

Your property management software is a gold mine. The vendor just buried the gold six menus deep and didn't give you a shovel.

The six goldmines are real:

The process to extract them:

You don't need to be a data engineer. But you can borrow the process:

Stop waiting for your PMS vendor to build better reports. They've had a decade. They won't.

Treat your PMS as a source of raw material. Extract the data. Analyze it in tools designed for analysis. Make decisions based on what the data actually shows, not what your PMS chose to report.

Pick one goldmine. Extract the data this week. Spend 2-3 hours with a spreadsheet.

You'll find something that changes how you operate. Guaranteed.

The data is there. You've already paid for it. It's time to use it.

P.S. - Which goldmine are you going to dig into first? Reply with a number (1-6) and I'll send you the exact step-by-step extraction process for that specific analysis.

No person has the power to have everything they want, but it is in their power not to want what they don't have, and to cheerfully put to good use what they do have.

— Seneca