Peak season just ended, and your data is the freshest it will ever be. QuikStor brought together Tron Jordheim, managing partner at Self Storage Strategies, and Scott Worden, VP of Growth at QuikStor, for a live conversation on what to do with that data before it goes stale. Here's the play-by-play.
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Duff Ferguson, QuikStor's Director of Marketing, opened by framing the timing problem. Peak season is over, occupancy is likely at its high point for the year, and the instinct after a busy summer is to take a breath. Tron pushed back on that instinct immediately.
"The challenge of waiting to look back is your impressions get a little stale," Tron said. Without structured time to reflect, operators get pulled into the next fire, the next project, and the specifics of what actually happened over the summer blur.
Scott added the dollars-and-cents angle. A decade ago, storage ran on a simple Memorial Day to Labor Day cycle and gut instinct. That's gone. "There's so much data at your fingertips that is in real time that you can make decisions and also plan ahead," Scott said. The tools exist now. The only question is whether operators use them before the trail goes cold.
Tron walked through the metrics that matter and the ones that get overlooked.
Occupancy is the easy number, how many units are filled, how much square footage is rented. Every FMS can spit it out. But Tron was clear that it's not the be-all and end-all.
Rate elasticity, also called rental increase resilience, is the one Tron flagged as underused. It measures how much of a rate increase your customers can absorb before you get a reaction that hurts the business. He argued this metric gets far less attention than it deserves.
Economic occupancy measures how close a facility is to its gross rent potential, though Tron noted this number can get skewed by special deals or long-tenured customers who've been through a dozen rent increases.
ECRI, the effective customer rate increase, came up repeatedly through the session as the number that tells you where the tipping point actually sits for a rate increase.
Scott pushed operators to widen the lens beyond their own FMS: Bureau of Labor Statistics data, local competitive intelligence, and area economic conditions all factor into a smart read of the numbers.
So what does a healthy post-peak occupancy curve look like? Tron's answer: growth, and growth alongside revenue, not instead of it. "Growing your occupancy while maintaining flat revenue or creating shrinking revenue is not going to help you," he said. And rather than obsessing over the competitive set, Tron pointed operators inward first: "Concentrate on competing with yourself. Look at your historic numbers, your historic performance, and look at yourself as your competitor."
On telling a real rate ceiling from ordinary seasonal softening, Scott pointed to QuikStor's approach of pulling in 46 external data sets to analyze a facility's specific market rather than applying broad-brush rules like "95% full means raise rates."
The one number both panelists said gets overlooked: how you got to your current occupancy in the first place. Tron warned that occupancy built on aggressive move-in specials can set a facility up for a move-out wave the moment those specials expire. Occupancy built on referrals and longer-term intent is a much sturdier foundation. Scott's addition: know your risk tolerance before you decide how hard to push.
This is where the conversation got tactical. Tron's framework starts with a simple question: are you on pace to hit this year's goals? If yes, there's nothing wrong with holding steady on rates to protect occupancy. If not, it's time to model the increase.
His rule of thumb: increases in the 7 to 9% range tend not to trigger much backlash. Below that, you're absorbing risk for not much gain. "A lot of people would tell you that a 3% increase gets you as much backlash as an 8% increase," Tron said, so the move is to run the spreadsheet first. Figure out how many move-outs the property can absorb at a given increase and still land ahead on total revenue, then decide if that risk is worth taking.
A live poll asked attendees whether their facility typically raises rates around Labor Day. Roughly 30% said yes, about a quarter said no, and the plurality, over 40%, said they hadn't decided yet.
Duff pressed Tron on a specific question: is it better to do two smaller increases a year or one larger one? Tron's take, based on experience rather than hard data: frequency annoys customers more than size. "It's the frequency of increases, not necessarily the size of increase that annoys people," he said, comparing it to how tenants expect one rent increase a year from a landlord and tolerate it, but two starts to feel like a pattern worth complaining about.
Scott layered in a guardrail mindset here. If a property can theoretically absorb 10 to 20 move-outs to hit a target increase, operators need monitoring in place to catch it early if reality diverges from the model, the same way a trader sets stop-loss parameters. Anniversary-date rate increases, tied to a customer's move-in date rather than a single calendar event, also give operators a steady stream of feedback on which cohorts tolerate increases and which don't, something a single annual blanket increase can't offer.
Scott opened this section with a blunt line: "If you're not tracking the phone calls where those main sources are coming from, you're guessing." Unique tracking numbers, trackable links, granular source attribution, these are table stakes now, and they matter more as summer volume tapers off and every dollar of spend needs to justify itself.
Tron's caution was about what "conversion" actually means. A campaign that generated 17 rentals tells you nothing on its own. What matters is what happened to those 17 tenants afterward. Did they stay a month or seven years? Did an entire cohort churn out the moment their first rent increase hit? "Look at the long-term result, not just what was my first month income from this ad," Tron said. QuikStor's cohort tracking is built for exactly this kind of analysis, sorting tenants by acquisition source so operators can see which channels bring in tenants worth keeping and which just bring in short-term churn.
Scott flagged the shift in how renters search, moving from generic "self storage near me" queries to more specific, conversational questions, and the growing expectation that they can complete a rental directly from the search result. Both panelists pointed to attribution complexity as an underrated trap: first-click versus last-click attribution can tell two completely different stories about which channel actually earned the rental, and a channel that looks weak under one model might be the one starting every customer's journey.
The shared warning on new channels and AI tools: don't chase them just because a competitor is. "Look at your numbers and see what's working for you and keep doing that," Tron said. Test new things deliberately, but don't burn time chasing shiny objects without the data to back the move.
As lead volume slows into fall, both panelists framed the season as an opportunity rather than a downshift. Tron's take: use the quieter months to retrain. During peak, call handle times tend to compress as volume rises, and the quality of the conversation, the qualifying questions, the relationship-building, slips. Slower season is the time to rebuild that muscle. "I want to have some emotional connection with them," Tron said of self-service renters, arguing that a rental made without any human touch may be more vulnerable to leaving at the first rate increase.
Scott described how call center models have shifted from centralized buildings to fully distributed teams, with technology routing and dispositioning calls to the right person regardless of where they sit. He also flagged first-month billing cycles as a factor in staffing: the first ten days of a billing period have traditionally driven a spike in payment-related calls, though automation is absorbing more of that volume now.
Both agreed the underlying shift is generational and behavioral. A majority of rentals now originate on mobile, and a growing share of renters want zero human contact at all. The fix isn't picking one channel, it's making every path available so customers can transact however they prefer.
On automation specifically, Scott was direct: "If you don't have some sort of automation built into your systems right now, it just creates more manual task for you to try to keep up with." Autopay and self-service payment links reduce inbound calls. Abandoned-rental capture, following up automatically when a prospect starts a rental online but doesn't finish, closes a gap that costs real revenue. The panel cited the well-known rule that follow-up within five minutes converts at a dramatically higher rate than follow-up at 15, 30, or 60 minutes. Tron's analogy: a prospect abandoned on your website with no follow-up is exactly like a customer standing at an empty reception desk. Tron also shared a real example from a property walkthrough: a prospect at the counter, mid-move-in, took calls from two competitors he'd also contacted, both too slow to win the business.
Duff closed the main discussion with the direct question: what does an operator actually lose by waiting until November to look at this?
"It's old," Scott said. "Timing is essence." Tron went further: by November, operators are shifting into holiday mode, and trying to reconstruct a clear picture of the summer while mentally checked out for the season means the analysis gets shortchanged, or skipped entirely, and next year it's even less likely to happen. Waiting also compounds any mistakes already made. The longer a course correction is delayed, the harder and slower the recovery.
Their homework for attendees:
Tron added a third angle: ask yourself simple questions about the season and see how easily you can answer them. Which channel was the best? Which was the worst? Where did spend run out of line with results? If you can't answer quickly, that's a signal about what to track better next year.
"My occupancy dropped in August. Is that normal seasonal softening or a real demand shift?" Tron's suggestion: call the people who moved out. Even a 10% response rate gives useful signal on whether it was a genuine dissatisfaction issue or simply life circumstances resolving (a move, a decluttering project finished). Scott added that a competitor's aggressive rate or promotional activity in the same window can also explain a dip that has nothing to do with your own performance, so check the local market too.
"I'm just above 85% occupancy. How aggressive should I be on rate increases without triggering a wave of move-outs before Q4?" Both panelists steered back to the underlying goal, not the occupancy number itself. If a facility is on pace for its annual revenue and NOI targets, there's no need to force an increase. "A lot of times occupancy is just sort of a proxy," Tron said. "The real number is what's revenue and NOI doing." A follow-up question raised unit mix: a facility can be full on large units and empty on small ones, and Tron agreed that decisions should be made by unit type and cohort, not just at the whole-facility level, since a 20% increase might make sense on one size and none at all on another.
"I run a single facility and don't have a large data set. Can I still make reliable decisions?" Tron encouraged upgrading to a modern FMS where possible, since better data access changes what's possible. Short of that, the fallback is competing against your own history: what did you do last year, what do you want to do differently this year. Scott added that even a single-facility operator has access to competitive data in their radius, and that market intelligence functions as a second data set even without scale.
"I run local search ads and Google ads side by side. How do I know if a channel is really converting versus just riding summer search volume?" Tron separated the two concepts cleanly: volume and conversion are not the same thing. A flood of leads during peak season may just be seasonal search volume, not a sign the channel is performing. The real test is the actual conversion rate, and how granular that conversion definition can get, depends entirely on the depth of tracking behind it. Scott sharpened the definition further: for storage, a conversion is a completed rental, not a landing page visit or a reservation start, and platforms like QuikStor can show the true percentage of traffic that turns into signed leases.