What maize maze operators can learn from their booking data

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What maize maze operators can learn from their booking data

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Discover key maize maze booking trends and visitor behaviour insights shared at the 2026 Maize Maze Association Conference, including mobile booking data, marketing attribution and lead time benchmarks.

What maize maze operators can learn from their booking data

The data and benchmarks behind maize mazes.

What we learned at the Maize Maze Association Conference

At the 2026 Maize Maze Association Conference, our Director of Product, Jed Woodcock shared insights from analysing booking and visitor data across maize maze operators using Beyonk.

Jed spends most of his time looking at data. As he put it during the talk, he’s a little obsessed with it - because good data helps us make better decisions. At Beyonk, it informs how we improve our platform. But it also helps attraction operators understand what’s actually working and where there are opportunities to improve.

To make the insights as relevant as possible, we analysed booking behaviour specifically from members of the Maize Maze Association using Beyonk. Around 38% of Maize Maze Association members currently use Beyonk, and all but one of these have been using the platform for over a year. That gives us a meaningful dataset to explore real visitor behaviour.

We looked at the full customer journey - from discovery to booking to the visit itself - and identified patterns that consistently influence performance.

In the talk, we explored:

How visitors actually book maize maze tickets

The marketing channels that drive the most discovery

What the data says about lead times, returning visitors and local audiences

Practical actions operators can take to boost bookings and optimise their season

Mobile booking behaviour is now the norm

One of the clearest patterns in the data is just how dominant mobile booking has become.

Across all operators on Beyonk in 2025:

86% of bookings happen on mobile

Around 40% of payments use Apple Pay or Google Pay

Visitors are typically booking while browsing on their phone - often while discussing plans with family or friends. This means the booking experience must be fast and frictionless.

However, a website survey from Rubber Cheese found that only around 32% had tested their site on mobile devices. Considering that the vast majority of visitors now book on mobile, this presents a clear opportunity - even small improvements to the mobile experience can significantly improve conversion rates.

Website speed directly impacts conversions

Because mobile dominates, website speed matters more than ever.

We tested the homepage of every Maize Maze Association member using Google PageSpeed Insights - a free tool that you can also use. We focused on a key mobile performance metric: First Contentful Paint, which measures how long it takes for the first visible part of the page to load. You should be aiming for under 3 seconds.

The results were revealing:

Average mobile load time: 3.4 seconds

Fast (0-1.8 seconds): 39% of websites

Average (1.8-3 seconds): 16% of websites

Slow (Over 3 seconds): 45% of websites

Performance varied significantly between sites. Some loaded in under a second, while others took more than 10 seconds. One standout example was Lakeland Maze Farm Park, (who have recently made some improvements to their website) which recorded a very fast 0.7 seconds.

Anything above three seconds is slow and will impact conversions. Visitors simply won’t wait long if a page doesn’t load quickly.

Top tip:

Run a free PageSpeed test on both your homepage and ticketing page. Improving load times is one of the simplest ways to improve booking conversion.

Understanding when customers actually book

Knowing when visitors are most likely to book tickets can make a big difference to your marketing performance.

If you understand when your customers are booking, you can align your marketing activity with those times. Emails, social posts and paid ads are far more effective when they appear at the same time people are already thinking about booking.

For maize maze operators, the busiest booking periods occur at very specific times:

Saturday and Sunday mornings (9am–12pm) are the peak booking window - accounting for 11% of all bookings

Weekday booking activity tends to be quieter during school drop-off and pick-up times

This makes sense when you consider how families plan activities. Weekend mornings are often when people look for things to do with children and decide how they’ll spend the day.

If your marketing messages appear during these moments, you’re far more likely to capture attention. While the exact timing will vary by attraction, analysing your own booking data can reveal clear patterns.

Top tip:

Schedule emails, social posts and paid ads to run when your visitors are most likely to book.

Lead time: an opportunity to secure bookings earlier

Another useful insight from the data is how far in advance visitors book maize maze tickets.

Across the dataset, the average lead time is around 5.5 days (which has stayed consistent in 2024 & 2025). That means many visitors are booking within the same week they plan to visit.

While this is typical for outdoor family activities, it also highlights an opportunity. Increasing your lead time - even by a few days - can significantly improve how you plan your season and manage demand.

Longer lead times help you:

Understand demand earlier

Plan staffing and operations more accurately

Reduce reliance on last-minute bookings

We’ve also seen early signs in 2026 that lead times may be shortening slightly, likely influenced by weather uncertainty. Families often wait to see what the forecast looks like before committing to an outdoor activity like a maize maze.

Because of this behaviour, operators who actively encourage earlier bookings are in a stronger position. Securing bookings earlier in the week gives you better visibility on how busy upcoming days will be.

Top tip:

Use early bird incentives, limited-time discounts, or reminders to encourage visitors to book sooner and increase your average lead time.

Most maize maze visitors are highly local

Just as understanding when your customers book helps you decide when to market, understanding where they come from helps you decide where to focus your marketing - whether that’s geo-targeted paid ads, local partnerships, or even traditional methods like leaflets and posters.

Every online booking already gives you valuable location data through the visitor’s postcode or address. This is one of the most useful insights you have for understanding your audience - but most attractions don’t use this data.

At Beyonk, we analyse the 50th percentile travel distance - the distance that 50% of an attraction’s visitors travel from. In simple terms, this shows the core catchment area for an attraction.

For maize maze operators, the pattern is clear: half of visitors typically come from within around 9.7 miles of the attraction.

There is some variation between locations:

Some mazes attract visitors from within 3-4 miles

Others draw visitors from 20 miles or more

Catchment areas are naturally smaller in dense urban areas such as London, and larger in rural regions like Cornwall. But across the dataset, one trend is consistent: maize mazes are fundamentally local attractions.

Operators who focus their marketing on nearby communities tend to see the strongest results.

Top tip:

Use postcode data from your bookings to identify where visitors are travelling from, then prioritise those areas with geo-targeted advertising, local partnerships and community marketing.

Marketing attribution is getting harder to track - so ask your customers

Tracking where bookings come from has become increasingly difficult due to privacy changes and cookie restrictions.

Tools like Meta Pixel and Google Analytics can still provide valuable insights when set up correctly and connected to your ticketing system. However, due to privacy changes and cookie restrictions, attribution can now be only around 50-70% accurate for some attractions, making it harder to clearly understand which marketing channels are truly driving bookings.

That’s why many attractions are returning to a simple but effective approach: just ask your customers directly.

Adding an optional checkout question such as: “Where did you hear about us?” can provide some of the most valuable marketing insight you’ll collect.

At Beyonk, this question has generated over 500,000 responses in 2025 alone, with an impressive 68% response rate. This creates a powerful dataset that helps attractions understand what is actually influencing bookings.

The breakdown across Beyonk attractions looked like this:

Word of mouth - 32%

Personal recommendations remain incredibly powerful. People still trust experiences shared by friends and family more than any advert.

Facebook - 21%

Still a key platform for family audiences, particularly through local community groups.

Returning visitors - 16%

A strong reminder that delivering a great experience is one of the most effective marketing strategies.

Google - 11%

Search continues to perform well for visitors actively looking for things to do.

Instagram - 7%

TikTok - 3%

Physical advertising - 2%

Despite the growth of digital marketing, personal recommendations remain the most powerful driver of bookings. Facebook continues to perform strongly for local discovery, while Instagram and TikTok are becoming increasingly influential as visitors share their experiences online.

Top tip:

Add an optional “Where did you hear about us?” question at checkout and tailor the answer options to include your key marketing channels and any local promotion you run.

AI search is beginning to influence discovery

Search behaviour is starting to evolve as more people use AI tools like ChatGPT to find things to do.

Instead of short search queries, visitors are increasingly asking more detailed questions such as:

“Can you recommend a family-friendly maize maze near Birmingham that’s good even if it rains?”

AI tools combine information from search engines, websites, reviews and location data to generate recommendations.