Another sold-out Saturday, another half-empty Tuesday—yet you’re still guessing how many housekeepers to bring in and whether to slash mid-week rates. What if a few lines of code could read the rhythm of your park as clearly as you read a reservation chart?
Enter LSTM neural networks: AI models that “remember” every holiday surge, every rainy-day cancellation, every remote worker who extends through Wednesday. Feed them your past two years of occupancy, and they’ll hand back a day-by-day forecast up to five months out—pinpointing when to raise rates, when to add staff, and when to tempt campers with a weekday work-from-the-woods package.
Ready to swap gut-feel scheduling for data-driven profits? Keep reading; the next five minutes could rewrite your entire booking calendar.
Key Takeaways
- Weekend nights fill up, but mid-week spots stay empty, cutting profits.
- LSTM AI can read two years of past bookings and predict daily demand up to five months ahead.
- Start with clean data: remove canceled stays, fill blanks with zeros, and add flags for weekends, holidays, local events, weather, and booking windows.
- Forecasts give number ranges, helping you set prices, schedule staff, and stock supplies without guesswork.
- Raise rates when predicted occupancy is high; offer bundles (not big discounts) when it is low, and refresh rules every Monday.
- Use the same forecast to plan housekeeping shifts, maintenance jobs, store orders, and even temporary Wi-Fi boosts.
- Market quiet Sunday–Thursday “work-from-the-woods” deals to remote workers to fill weekday gaps.
- Choose to build your own model, buy a vendor solution, or mix both for flexibility.
- Quick start: export clean data, add features, build an LSTM prototype, test against last season, then plug results into pricing and staffing tools.
The Revenue Gap Hiding in Your Calendar
Industry data (RV-park study) shows most parks clear 80 percent occupancy on Fridays and Saturdays, yet drop under 50 percent by Tuesday—an imbalance that quietly chips away at RevPAR even when Saturday nights sell out. The 2025 U.S. RV-park outlook documents a 12 percent year-over-year lift in mid-week demand at warm-weather destinations, fueled by remote workers who turn long weekends into working vacations. Operators who still price by weekly averages leave money on the table twice: by undercharging high-demand nights and over-discounting soft ones.
The hidden cost goes beyond rate misfires. Overstaffed housekeeping crews wait for check-outs that never arrive, camp-store shelves overflow late in the week, and maintenance jobs pile up because no one knows the true slow periods. A precise demand curve turns these blind spots into levers—letting you throttle labor, inventory, and even amenity hours to match real-time need.
From Gut Feel to Machine Intelligence
Spreadsheets served well when booking patterns resembled neat bell curves, but today’s demand zigzags around hybrid work, pop-up festivals, and weather swings. That’s why campground software vendors are embedding AI directly into their dashboards; Campspot’s AI update, for instance, predicts occupancy and suggests prices 150 days ahead.
Why LSTMs Fit Campground Demand Like a Glove
Campground occupancy rises and falls in weekly heartbeats—Monday dips, Friday spikes, Sunday plateaus. LSTMs excel at capturing exactly that cadence because each training step “remembers” information from previous days instead of treating them as isolated points. Where rule-based systems crumble under quirky holidays that move dates each year, LSTMs learn their patterns organically, folding Independence Day weekends and Labor Day Mondays into memory.
Uncertainty is part of outdoor hospitality. LSTMs don’t just spit out a single number; they provide probabilistic ranges. That means you can staff to the 90th-percentile Saturday, stock propane for the 75th-percentile cold snap, and schedule road grading for the 20th-percentile Tuesday without fearing a last-minute rush.
Lay the Groundwork: Clean, Usable Data
A neural network can’t find rhythm in static. Pull at least two years of daily occupancy from your PMS, but strip out canceled and no-show reservations so the model trains on reality, not ghosts. Treat missing dates as zero occupancy—blank cells register as “unknown,” which twists predictions. Record site type, time zone, and whether the stay was a walk-in or reservation; these columns become crucial signals once price tiers diverge between cabins and back-in RV pads.
Before you feed data to the model, document every column in a quick data dictionary. That 30-minute chore prevents future confusion and protects model performance.
Turn Raw Numbers into Story-Rich Features
Weekend vs. weekday is step one, but campers don’t live by calendars alone. Add binary flags for each federal holiday and long-weekend Fridays and Mondays—days that behave more like Saturdays than weekdays. Overlay a local event calendar within a 90-minute drive: county fairs, bluegrass festivals, fishing tournaments. These micro-bursts of demand often outrank national holidays in lift but never appear in traditional seasonality charts.
Weather makes or breaks a tent booking, so bring in predicted high temperature and precipitation percentage for every day in the training set. Combine that with booking-window length—the days between reservation and arrival—and you allow the model to separate last-minute storm-fleeing RVers from meticulous planners who secure sites months ahead. Finally, feed the daily count of each site type still unsold; if you’re adding glamping domes this year, the model learns how mix shifts change sell-out thresholds.
Build and Tune Your LSTM Without Getting Lost in the Code
Start by normalizing inputs between 0 and 1; neural nets learn faster when every feature speaks the same numeric language. Split data into 70 percent training, 15 percent validation, and 15 percent test to monitor overfitting. A 30-day look-back captures monthly cycles, while two LSTM layers with 64 units each and dropout of 0.2 balance learning depth with generalization.
Use Mean Absolute Error as the loss metric—operators understand a “plus or minus five sites” miss more easily than abstract percentages. Back-test on last season’s Fourth of July; if the model catches the surge within acceptable error, promote it to production. If not, adjust sequence length, tweak learning rate, or add new features such as Google search volumes for “campground near me” to refine performance.
Translate Forecasts into Dynamic Rates
A prediction becomes profit only when it nudges price. First, lock in rate floors that cover operating costs and ceilings that respect competitor benchmarks. Next, create occupancy breakpoints: raise rates when the forecast tops 70 percent, hold steady between 40 and 70 percent, and sweeten with bundled firewood or kayak rentals when predictions fall below 40 percent. Guests expect weekend surcharges, so lean into them, but protect weekday ADR by adding value instead of slashing price.
Automate the rule engine to refresh every Monday morning. That cadence keeps the public rate grid consistent—no dizzying hour-to-hour swings—while still reacting faster than competitor parks that revise rates quarterly.
Let the Forecast Run Your Operations—Not the Other Way Around
Housekeeping schedules often ride on tradition, not data. Shift them to mirror forecasted departures: a 90th-percentile Saturday check-out list justifies full crews, while a 20th-percentile Tuesday frees time for deep-cleaning cabins. The camp store follows the same logic; order snack restocks and propane exchanges based on headcount instead of last year’s averages.
Maintenance finally gets breathing room. When the model flags a two-day valley next month, you can slot road grading, HVAC servicing, or hot-tub refills without scrambling. Even Wi-Fi bandwidth and streaming-TV licenses scale smarter: purchase temporary boosts for peak windows and drop back to base tiers overnight, saving hundreds in subscription overages.
Marketing That Fills the Weekday Trough
Remote workers prize reliable Wi-Fi and quiet corners. Package those strengths in a Sunday-through-Thursday “work-from-the-woods” offer, highlighting laminated picnic tables and surge-protected outlets. Sweeten the pot with weekly site rates that still beat city rent, and your occupancy gap narrows on its own.
Email past guests a mid-week loyalty bonus—stay three weekdays, earn a free future night. Combine that with geo-targeted social ads inside a 300-mile drive radius early in the week; these audiences have both the flexibility and the car trunks to act on impulse. Finally, partner with local wineries or kayak tours for bundled weekday experiences. A flexible cancellation policy during low demand reduces booking hesitation, while stricter weekend terms protect revenue when you already expect a full house.
Build, Buy, or Blend Your Forecasting Stack
Rolling your own LSTM in TensorFlow grants full control: custom features, custom horizons, and no vendor fees. The trade-off is time and talent—someone has to babysit data pipelines, retrain models, and troubleshoot anomalies. Buying turnkey functionality from platforms like Campspot delivers speed, support, and upgrades, but limits feature flex for niche inventory like safari tents or tiny homes.
Many operators settle in the middle. They deploy the vendor’s engine for core occupancy and pricing, then layer a homegrown LSTM on top for specialized segments. By exporting the vendor’s forecasts as features into the custom model, you stack intelligence rather than duplicating effort, arriving at a blended strategy that scales with ambition and bandwidth alike.
Your Five-Step Quick-Start
Turning theory into action starts with mindset. Think of this quick-start framework as a recipe for “campground occupancy forecast” success rather than a rigid checklist; each operator can season to taste. Follow the steps, measure outcomes, then iterate until your dynamic RV-park pricing engine hums as smoothly as your new espresso machine.
Implementation isn’t just a tech exercise—it’s a culture shift. Staff will trust the model when they see it nail Fourth of July traffic or predict that sleepy Tuesday in October. Share small wins in team meetings, celebrate early labor savings, and momentum will carry the project through inevitable data-cleaning hiccups.
- Export two years of clean, daily occupancy data—cancellations removed, zeros filled.
- Add features: weekend flags, holiday markers, event calendars, weather, booking window, and site mix.
- Prototype an LSTM in Google Colab using open-source Keras notebooks; aim for a mean absolute error under five sites.
- Compare predictions to last season’s actuals and fine-tune until weekend peaks and weekday troughs align.
- Connect the forecast to dynamic pricing rules and staffing schedules so insight turns directly into action.
The Road Ahead
Demand patterns won’t freeze in place. Remote work, new lodging types, and shifting school calendars will keep the curve moving, but sequence-based neural nets evolve with every nightly data refresh. Parks already experimenting with LSTM-powered decisions report smoother occupancy curves and double-digit RevPAS gains, proof that early adopters stake the highest ground. A 2025 lodging-sector peer-reviewed study confirms that LSTMs continue to outperform classic forecasting methods as data complexity grows.
Expect external shocks—fuel prices, campground-review viral posts, even unexpected eclipse tourism—to test your system. By combining automated retraining with human judgment, you’ll ride those waves instead of being swamped by them. Remember: every data point that surprises you today becomes fuel for a sharper model tomorrow.
Forecasting with LSTMs turns your reservation grid into a revenue engine—one that balances linen counts, propane orders, and mid-week rates before you finish your morning coffee. If you’d rather toast marshmallows than tune hyperparameters, let Insider Perks wire the tech for you. Our team blends AI forecasting with automated marketing and ad strategies so every day of the week performs like a holiday. Ready to see what a data-driven calendar can do for your park? Book a quick strategy call with Insider Perks and watch your Tuesdays start smiling as wide as your Saturdays.
Frequently Asked Questions
Q: What is an LSTM neural network in plain English?
A: Think of an LSTM as a spreadsheet that can “remember” yesterday, last month, and last Independence Day all at once; it looks at the sequence of past occupancy numbers and learns the rhythm of how demand rises and falls so it can predict what will happen on any future date.
Q: Do I have to be a data scientist to use LSTM forecasting?
A: No—many operators start with pre-built Google Colab notebooks or turnkey PMS add-ons, and the heaviest lift is usually exporting clean daily occupancy data; if you can run a mail-merge or a basic pivot table, you can follow step-by-step templates or outsource the setup to a freelancer for a few hundred dollars.
Q: How much historical data do I really need?
A: Two full years of daily data is ideal because it captures every holiday once and most twice, but usable models can start with as little as 12 months; the algorithm will simply express wider confidence ranges until more seasons are fed in.
Q: My park only has 60 sites—will an LSTM still be useful?
A: Yes, because the model learns percentages and patterns rather than absolute volume, a 10-site swing on a 60-site park matters just as much to the algorithm as a 100-site swing at a mega-resort, so the insights scale down perfectly.
Q: How often should I retrain the model?
A: Refreshing the model monthly keeps it current with new booking trends and weather anomalies without overburdening your system or staff, and most cloud notebooks can complete a retrain in under 15 minutes.
Q: What tools or software do I need to get started?
A: A cloud notebook like Google Colab (free), a CSV export from your PMS, and optional plug-ins for weather and local event feeds are enough; the heavy computing runs in the cloud so no specialized hardware is required.
Q: Is my data secure if I use a cloud notebook?
A: Your raw file sits in your private Google Drive or similar encrypted storage, and you control all sharing permissions; no data leaves your account unless you deliberately connect third-party APIs.
Q: How quickly will I see a return on investment?
A: Most parks notice measurable gains—fewer mid-week vacancies, tighter labor scheduling, and smarter store orders—within one high season, often covering any setup costs with a single weekend of optimized rates.
Q: What level of accuracy should I expect?
A: Well-tuned LSTM models commonly hit a mean absolute error of three to five sites per day on parks under 200 sites, which is precise enough to make confident decisions about staffing, pricing, and inventory.
Q: Will I need to type in weather forecasts and local events manually?
A: No—free APIs such as Open-Meteo and community-maintained event calendars can be pulled automatically into the notebook so the model ingests fresh data every time it retrains.
Q: How is this different from the forecasting module in my PMS?
A: Built-in tools are usually black boxes that rely on generic hotel algorithms, while a bespoke LSTM lets you add campground-specific signals like fire-ban days, fishing tournaments, or the impact of adding glamping domes mid-season.
Q: Could dynamic pricing anger loyal guests?
A: Transparency is key: publish clear rate ranges, lock in quoted prices once a reservation is made, and frame weekday discounts as limited-time perks rather than arbitrary price drops so regulars feel rewarded, not penalized.
Q: What does it cost to maintain an LSTM forecast once it’s running?
A: Cloud compute charges are negligible—often pennies per retrain—so ongoing expense is mostly staff time to review outputs and adjust business rules, typically an hour or two per month.
Q: Will this work for seasonal parks that close in winter?
A: Yes—closing days are simply zeros in the dataset; the model learns that occupancy flatlines every off-season and focuses its predictive power on the months you’re open.
Q: Can I override the model if I learn a big RV rally is coming to town?
A: Absolutely—treat the forecast as a smart advisor, not an autopilot; you can manually adjust rates or staffing for one-off events and then let the next retraining cycle absorb the new data point for future seasons.