From data point to decision - how theme parks gain tangible insights from visitor data

Part 3 of understanding visitor flows - How amusement parks use smart technology to optimise experience, efficiency and revenue

In Part 1 of this series, we looked at the fundamental importance of visitor flow analysis and why it is becoming a decisive competitive factor for amusement parks. In Part 2, we showed how such a system can be easily implemented using modern POS systems and ticket scanning technology.

Now we go one step further: What actually happens to the collected data? What information can be obtained from the scanned tickets at the ticket offices or points of sale? And above all: How can amusement park operators derive concrete, operationally relevant decisions from this data?

Data alone does not add value. This only comes through analysis, interpretation and implementation.

From checkout to behaviour patterns: the basis of visitor flow analysis

When visitors scan their admission tickets when purchasing food, drinks or souvenirs – for example, to receive a discount or collect points – a data point is created. This consists of various components:

The exact time of the transaction
The point of sale (location in the park)
The items purchased (including quantity and price)
The ticket ID (anonymised)
Optional: ticket category (e.g. child, family, group, VIP)
Optional: additional parameters (e.g. weather, event day, time of park entry)

Each time a ticket is scanned at a different location, the database becomes richer. Over hours, days, weeks or entire seasons, this creates a detailed picture of visitor movements and consumer behaviour.

What insights can be gained from this data?

When analysed correctly, the information collected opens up a wealth of insights for strategic and operational decisions – from personnel deployment and product range to space planning. Below, we present the most important areas of analysis.

1. Visitor frequency and movement patterns

The fundamental goal of visitor flow analysis is to understand how visitors move through the park. Which routes they choose, which areas they frequent most – and which they tend to avoid.

By evaluating the time and location data, so-called ‘heat maps’ can be created. These clearly show:

Which areas are particularly busy (high-frequency zones)
Which attractions, sales outlets or paths are less frequented (low-frequency zones)
Whether certain visitor groups behave differently in the park (e.g. families with small children vs. teenagers)
Whether seasonal or weather-related differences in visitor behaviour can be identified

Real-life example:

By evaluating its scan data, an amusement park discovered that a centrally located souvenir shop had significantly fewer walk-in customers than other shops, even though the product range, location and opening hours were comparable. The analysis revealed that access to the shop was located in a bottleneck that most visitors avoided because it was an unattractive dead end. After redesigning the route, including new signage and better wayfinding, footfall at the shop doubled – and so did sales.

2. Length of stay, dwell time and rhythm

A time profile can be created using multiple scan points for a visitor (e.g. at 11:15 a.m. at the snack bar, at 1:05 p.m. in the restaurant, at 3:30 p.m. in the souvenir shop). This shows:

How long guests stay in the park on average
Which areas they spend time in and for how long
Which routes they typically take
How their visit progresses throughout the day

Benefits for the park:

Staff and cleaning planning based on actual usage
Optimisation of opening and operating hours of sales outlets
Equalisation of peak times through targeted offers during off-peak times
Identification of ‘through zones’ without longer dwell times (potential for upgrading)

Example:

A park noticed that visitors were ‘rushing through’ certain attractions in the morning but then taking longer breaks. The operators decided to place new seating areas, shaded rest areas and smaller catering outlets along the typical midday route – resulting in a sharp increase in the length of time spent in these areas.

3. Consumer behaviour and product range management

If the POS system is linked to ticket scanning, it is possible to analyse precisely which products are purchased by which visitor groups – and at what times of day.

Possible insights:

Which products sell particularly well at what times?
Which items are often purchased together (cross-selling potential)?
Which age groups or ticket types prefer which products?
Which sales outlets work particularly well for certain product groups?

Example:

A park analysed its sales data and found that combo meals sold particularly well to young adults between 2 and 4 p.m. – but only at a specific sales stand. The reason: the stand was located near a water attraction that was mainly used by this target group. By specifically promoting this menu at other locations with similar visitor profiles, sales of the menu increased by 18% across the park.

4. Differentiate target group behaviour

The ticket category (e.g. adult ticket, family ticket, school ticket) can also be used to identify differences in the behaviour of different visitor groups:

Which areas of the park appeal to which target groups in particular?
How does the purchasing behaviour of individual ticket buyers differ from that of groups or annual pass holders?
What times do certain groups prefer?
What is the typical length of stay?

Example

Families with small children stayed in the park for an average of only around four hours, but spent an above-average amount of time in a specific themed area.

An additional snack point in this zone, with a range of products specifically tailored to children, achieved a 35% increase in sales in its first season.

5. Compare seasonal capacity utilisation and visitor behaviour

The data collected can also be viewed over time:

Are there significant differences between holiday periods and school terms?
How does behaviour change in different weather conditions?
Are there differences on event days?

Example:

On hot days, visits to the park were shorter, but significantly more was consumed at certain sales stands – especially cold drinks and ice cream. The operators set up mobile sales points on busy routes, especially for hot days – with consistently positive feedback and high additional sales.

How data-based decisions are made

Data analysis does not have to be complicated – with the right tools, many of these insights can be derived directly from the POS system. The ToucanTix POS system offers the following for this purpose:

Real-time reporting
Location- and item-related evaluations
Time analyses (daily progress, weekly comparisons)
Export functions for further analysis
Visualisations such as heat maps and progress charts

The trick is to derive concrete measures from these evaluations – whether short-term (e.g. adjusting staffing levels, advertising offers) or long-term (e.g. investments, product development, renovations).

Conclusion: Data becomes knowledge - and knowledge becomes impact

The real strength of visitor flow analysis lies not in the technology, but in its practical applicability. If you know how visitors move through the park, what they consume when and where, and how different groups behave, you can:

Design offers more specifically
Use resources more efficiently
Increase sales potential
And noticeably improve the visitor experience

Thanks to modern POS systems, the effort required to collect data is minimal – but the benefits are considerable.

In the fourth and final part of this series of articles, we will show how these insights can be used for targeted marketing, visitor loyalty and offer management.