Your front-desk team keeps repeating it: “We’re happy to book you, but there’s a two-night minimum.” Is that line powering higher revenue—or quietly turning prime sites into empty gravel? With average glamping stays stretching from 2.2 to 2.7 nights in just one year, and cancellations crashing 16 percent, the stakes have never been clearer: get the rule right and you’ll enjoy fatter ADRs and calmer turnover; get it wrong and weekend warriors vanish to the park down the road.
So how do you prove which side you’re on? Enter Interrupted Time Series analysis—the simplest, most owner-friendly way to turn your own reservation history into a verdict on minimum-stay policies. One clean data pull, one marked “interruption” date, and you’ll see in black-and-white whether the policy really lengthens stays, crushes no-shows, or merely clogs mid-week inventory. Spoiler: you already own every data point you need. Keep reading to learn the five blind spots that can sink an ITS study—and the quick fixes that turn your campground’s numbers into a crystal-ball for 2026.
Quick Takeaways
The big picture is straightforward: a minimum-stay rule can widen your margins, but only if guests accept it. Before locking the policy in stone, you need proof that it boosts length of stay without torching occupancy or spiking cancellations. The bullets below highlight the leanest path to that proof and set the guardrails for a stress-free ITS test.
Commit these points to memory—or better yet, pin them to the office corkboard—because each becomes a checkpoint in the sections that follow. They protect you from data gaps, segment blind spots, and knee-jerk pricing tweaks that undo hard-won gains.
– A two-night rule can make more money, but only if guests accept it.
– Test the rule with Interrupted Time Series (ITS); compare data from before and after the start date.
– Gather clean, daily numbers on stays, cancellations, occupancy, and price for at least one year.
– Split the data into groups (RV, tents, OTAs, direct bookings) to see who likes or dislikes the rule.
– In ITS, watch for a fast jump in average stay and a steady climb over time.
– Use findings to set different minimums for weekends, holidays, or special units.
– Longer stays cut housekeeping work and open time for upsells like tours or rentals.
– Tell guests why the rule exists and offer waitlists or mid-week deals to stay flexible..
The Market Signals Behind Longer Stays
Industry clues point in one direction: guests are sticking around. At the 2025 Glamping Show Americas, analysts reported that average length of stay jumped 23 percent, from 2.2 to 2.7 nights, while ADR cracked the $251 ceiling (Glamping Show data). More telling, over half of surveyed operators now enforce a two-night minimum on every reservation, not just holiday weekends.
The public sector is falling in line. Wisconsin’s state-park system now requires two nights for reservations placed between May 15 and October 31—although single-night walk-ins get a lifeline (Wisconsin policy thread). Meanwhile, guest reliability is surging: arrivals hit 70.7 percent in 2024 while no-shows shrank to 4 percent, according to The Dyrt brief. A market this stable gives owners permission to tighten rules—provided the data prove the juice is worth the squeeze.
Why Interrupted Time Series Beats Gut Feel
Interrupted Time Series (ITS) sounds academic, yet the premise is campground-simple: chart your KPIs before and after the day you flipped on the minimum-stay switch. If the graph shows an immediate step-up in length of stay or a steeper upward slope over time, the policy earns its keep. If not, you pivot before another booking season rolls through.
The method outshines year-over-year comparisons because it controls for seasonality, holidays, and freak weather. Instead of asking, “How did July do versus last July?” you ask, “Did July behave differently once the rule started, all else equal?” That nuance means you can roll out a two-night mandate in June and still isolate its impact from Labor Day’s built-in surge.
Build a Dataset That Won’t Betray You
ITS rises or falls on data hygiene. Make sure your PMS stores arrivals, departures, ADR, channel, and unit type in consistent fields. A single typo in the “no-show” column can poison an entire regression, so lock monthly read-only exports and create a plain-English data dictionary before staff turnover rewrites your history.
Daily granularity is non-negotiable. Tag each night with season, holiday, and even weather extremes so the model knows a thunderstorm, not the new rule, tanked occupancy. When you finally hit “run,” the software spots the policy’s fingerprint—free from background noise that could steer you into a costly false positive.
Zoom In Before You Zoom Out
Aggregated numbers can flatter or betray. Break the dataset into meaningful cohorts—RV sites, safari tents, dry-camping pads, direct versus OTA bookings, loyalty members versus first-timers. One seaside glamping operator found that a three-night minimum boosted tent revenue but strangled turnover in vintage Airstreams prized by road-trippers.
Run identical ITS models on each segment. You may discover that OTA guests tolerate a two-night rule only on peak weekends, while repeat direct bookers accept it year-round. Tailoring policy this way captures revenue without alienating the exact travelers who fill mid-week gaps and rave about you on social media.
Operations and Pricing: The Hidden Multipliers
Longer stays reshape the back of house. Housekeepers can switch from daily turnovers to third-day “refresh” cleans, cutting labor hours without dinging guest satisfaction. Bulk ordering linens, propane, and firewood reduces mid-week supply runs, while cross-trained staff can redirect idle check-in time toward revenue-makers like guided hikes or food-truck nights.
Price strategy needs equal finesse. Bundle kayak rentals, s’mores kits, or late checkout into three-night packages so guests perceive value, not restriction. Slide minimums to three nights on holidays, two on regular weekends, and one mid-week. An ITS overlay reveals whether length-of-stay discounts lift overall RevPAR or simply cannibalize premium nights—allowing you to tweak before the next demand spike.
Speaking the Guest’s Language
Clear communication is the cheapest insurance against cart abandonment. Post the minimum-stay rule on listing pages, booking calendars, and confirmation emails so no one feels ambushed at checkout. Explaining that fewer turnovers translate to quieter grounds and spotless bathhouses reframes a “restriction” as an amenity.
Flexibility still sells. Offer a real-time waitlist for one-night travelers and shoulder-season specials that drop the minimum when demand thins. Train reservation agents to fill added nights with local itineraries—sunset paddle tours, winery crawls, farm-market mornings—so the extra day feels like a curated perk instead of a fee.
Your Eight-Step ITS Roadmap
Mark the exact date your two-night rule went live; that becomes the interruption variable. Next, build daily metrics—length of stay, occupancy, cancellation rate, and ADR—for at least 12 months before and after the change. Confirm the pre-rule trend is stable; a wobbly baseline muddies any verdict.
Layer in seasonality dummies, holiday flags, and optional weather data, then run a segmented regression. Examine the immediate level change and the post-rule slope. Repeat the process for each cohort, run a placebo test using a fake interruption date, and interpret the coefficients: a positive jump with a stable upward slope signals “keep the rule,” while a flat line or negative tilt means recalibrate.
A Hypothetical Test Run
Picture a 40-tent glamping resort that flipped on a two-night minimum June 1, 2024. Twelve months later, ITS shows an immediate 0.6-night lift and a continuing 0.05-night monthly climb. Cancellations dropped 12 percent and ADR held steady.
Operational gains sweetened the pot: 18 percent fewer turnovers freed housekeepers for upsell activities like picnic-basket deliveries. Management kept the two-night rule year-round for tents but preserved single-night freedom in five Airstreams frequented by road-trippers—proof that segmentation plus data equals nuanced policy.
When the Numbers Say “Pivot”
Not every test returns a victory lap. If ITS reveals flat or declining length of stay, consider relaxing the rule Sunday through Thursday while holding firm on weekends. You can also fence rates: refundable two-night bookings alongside non-refundable singles sold via walk-ins.
Data-driven transparency builds trust. Announce that you ran an evidence-based trial and adjusted policies accordingly. Guests appreciate operators who experiment, measure, and adapt rather than cling to rigid rules that serve no one.
Key Takeaways for Peak 2026
First, secure at least a year of clean, daily reservation data before any policy change; the clearer the baseline, the sharper the verdict. Second, segment ruthlessly—average results disguise profitable pockets and problem children alike. Third, model both level and trend shifts to catch quick wins and slow burns.
Fourth, weave operations, pricing, and guest communication into your strategy so the rule enhances rather than hinders experience. Finally, let the ITS findings guide refinement, not just validation, of your minimum-stay playbook. A well-executed study converts uncertainty into actionable insight, positioning your property to ride the 2026 demand wave with confidence and precision.
Your reservation history is already writing the playbook—let’s make sure it’s the winning version. If you’re ready to turn those spreadsheets into AI-powered forecasts, automate policy tweaks in real time, and broadcast the right message to the right guest before they ever hit “book,” connect with the outdoor-hospitality strategists at Insider Perks. Our team translates ITS insights into smarter pricing, friction-free guest journeys, and marketing that fills every pad and tent, not just the weekends. Click here to see how quickly your data can start working overtime—and let tomorrow’s campers thank you for the two-night stay they never knew they needed.
Frequently Asked Questions
Q: What’s the quickest way to explain Interrupted Time Series (ITS) to my business partner?
A: Think of ITS as a before-and-after speed trap for your KPIs: you mark the exact date a rule changed, chart the metrics daily on both sides of that line, and let a simple regression tell you whether the “speed” of length of stay, cancellations, or ADR truly shifted because of the policy rather than normal seasonality.
Q: How much historical data do I really need for a credible ITS study?
A: Aim for at least 12 consecutive months before the policy change and 12 months after; anything shorter risks confusing ordinary seasonal swings with policy impact, while anything longer than three years can blur the analysis if you’ve upgraded sites or added new unit types in that time.
Q: Can a 40-site park or Mom-and-Pop campground run ITS without a data scientist on staff?
A: Yes—Excel or Google Sheets can manage the regression if you’re comfortable with formulas like LINEST, and free tools such as JASP or the freemium version of Tableau make visualizing the level and slope changes almost drag-and-drop.
Q: What are the absolute must-have data columns in my PMS export?
A: You need arrival date, departure date, ADR, site/unit ID, booking source, cancellation flag, and ideally a “no-show” flag; without these, you can’t calculate daily occupancy, length of stay, or filter out noise from walks and cancellations.
Q: How do I control for holidays, festivals, or weird weather that skew demand?
A: Add binary “dummy” columns for each holiday period, local event, or extreme-weather day and include them in the regression; that lets the model separate a rain-soaked Fourth of July dip from your new two-night rule’s true effect.
Q: My park added three new glamping tents midway through the test window—does that ruin the study?
A: Not necessarily; just tag those new units with a separate cohort ID and run a parallel ITS on legacy inventory only, then a combined model to see whether the capacity change, not the minimum stay, drove any revenue jumps.
Q: What sample size is “enough” to trust the results?
A: Statisticians like 50–100 observations on each side of the interruption, which a 40-site property easily hits in four months of peak season, but remember quality beats quantity—clean, gap-free data trump larger yet messy datasets.
Q: How do I interpret a result that shows a level increase but a flat post-rule slope?
A: A positive level jump means the policy delivered an immediate benefit (like longer stays), while a flat slope signals that advantage isn’t compounding over time; keep the rule but monitor every six months to ensure guest sentiment doesn’t erode the gain.
Q: ITS says my two-night minimum hurts weekday occupancy—what’s the first pivot to try?
A: Keep the rule Friday through Sunday but drop to one night Monday–Thursday, then rerun ITS after another season; most parks recapture mid-week volume without giving up the weekend ADR cushion.
Q: Does segmentation really matter for a small park with only two accommodation types?
A: Absolutely—tenters, RVers, and glampers behave differently, and one underperforming segment can mask success in another; a quick split by unit type or booking channel often reveals where to tighten or relax the rule.
Q: Will OTAs let me enforce different minimum stays than my direct site?
A: Yes, most OTA extranets allow channel-specific length-of-stay rules, but update them simultaneously with any direct-site change or you’ll confuse guests and risk double standards that muddy your ITS analysis.
Q: How do I explain a new minimum-stay rule to guests without sounding restrictive?
A: Frame it as a quality upgrade—fewer turnovers mean cleaner bathhouses, quieter grounds, and more time for staff to curate experiences; adding value-rich packages like late checkout or s’mores kits softens any perception of a penalty.
Q: What does an ITS study actually cost in dollars and hours?
A: If your data is clean, expect two to three hours for the export and formatting, another hour to run the regression in free software, and maybe $0–$200 for a consultant or upgraded BI dashboard; the real investment is staff discipline in maintaining consistent data fields going forward.
Q: Could an external shock like another pandemic void my ITS findings?
A: Black-swan events can distort any time-series model, so rerun ITS once normal patterns return and include a dummy variable for the shock period; that keeps extraordinary circumstances from obscuring the true policy effect.
Q: How often should I rerun ITS after the first successful test?
A: Treat it like an annual check-up—export the latest 12 months every off-season, rerun the model, and verify that guest behavior hasn’t shifted; continuous validation keeps a once-smart rule from turning into tomorrow’s silent revenue leak.