Campasun: eight campsites, one pricing engine.
How a campsite operator in the South of France cut its admin, put one process in place across its sites and got a dynamic pricing tool built for campsites.
These are the figures Campasun reported to us after the work. Results at another business will depend on what it does today.
Eight sites, too much typing.
Campasun runs eight campsites across the South of France. Three things were holding the group back.
Emails answered by hand
Staff read and replied to every email themselves. Replies were slow and uneven, and in peak season the team could not keep up.
Prices set once and left
Fixed prices could not respond to competitor changes, the weather or local events, so revenue was left on the table.
A different process at each site
Each of the eight campsites did things its own way, which made the group harder to run and harder to grow.
Map it, automate it, then price it.
We took the problems one at a time, starting with the processes and ending with the pricing.
Process mapping
We mapped every business process across the campsites in Miro, found the slow spots and agreed one standard way of working.
Email automation
A custom Python application, connected to the OpenAI API, that reads, sorts and answers customer emails around the clock.
Operations automation
We automated the repetitive admin tasks and put the same workflows in place at all eight sites.
A dynamic pricing engine
Pricing software built for campsites. It watches competitors, weather, local events and booking history, and suggests prices.
Built for campsites, from scratch.
Pricing tools made for hotels did not fit how campsites work, so we built one. It is the biggest piece of the project.
Competitor tracking
The engine finds competitor campsites within a radius you set, using camping-and-co.com data, and tracks their prices and availability. For Campasun it follows more than ten competitors.
Data enrichment
Booking history is enriched with weather (temperature, rainfall and sunshine hours), holiday calendars, school breaks and calendar features such as day of week and season.
Price predictions
A machine learning model reads the booking history, capacity and market conditions and predicts prices 14 to 60 days ahead, against the occupancy target you set.
Pricing strategies
Pick Conservative, Balanced or Aggressive, then tune demand sensitivity (30 to 90%), price aggression (40 to 100%) and the occupancy target (65 to 90%).
Five screens from the tool.
Competitor discovery, data enrichment, strategy controls, the price timeline and the forecasts. Click a screenshot to enlarge it.
"Jengu built something that simply didn't exist in the campsite industry: an AI-powered pricing engine that actually understands our market. The system monitors competitors, overlays weather and holiday data, analyzes years of booking patterns, and uses machine learning to predict optimal prices. It's transformed how we manage pricing across our 8 locations. This level of intelligence was previously only available to hotels, and now we have it tailored specifically for campsites."
Campasun management, South of France
What would this look like at your place?
The calculator gives you a rough estimate of the hours and money automation could save you. Or book a call and we will talk it through.