The pocket guide to ticketing analytics in visitor attractions
For most attractions, ticketing forms the core of the business model, be it general admission or activities such as events, exhibitions or experiences.
As a data domain, ticketing has particular requirements and hidden insights resulting from the ticket lifecycle and relationship to the customer record.
Using data to understand the ticket lifecycle
Tickets have a lifecycle – start by analyzing as they are:
- Sold (or “booked”) – how well are you performing
- Scheduled – how many visitors to expect
- Redeemed (or “scanned”) – how many showed up
Some may have exceptions, such as a gift voucher which will show as sold but not scheduled and can be redeemed or recognized within 12 months.
Tickets may also begin with an allocation, which might go through one or more issues as capacity is released. If things don’t go to plan, tickets can also be transferred, updated, canceled or refunded.
Ticketing and recognition rules
Given tickets can be used to recognize numbers for different business metrics, we suggest using the terms:
- Visitation to the venue (for general admission)
- Attendance to an activity (such as an event, exhibition or experience)
- Note the visitation or attendance count may differ in its recognition rule to the revenue from the ticket (we recommend using scheduled or redeemed for both)
When setting recognition rules, pay careful attention to what tickets should be counted, when and by how much towards these metrics.
Ticket data structures
Tickets have a common structure – to begin, dive into their:
- Product (what the visitor did, eg GA)
- Type (who the visitor is, eg Adult)
- Channel (how the visitor bought, eg Online
You’ll find all sorts of other attributes in the ticket record, such as categories, campaign codes, discount codes, price, quantity, tax, totals. This attribute list will depend on your ticket vendor.
Ticket metrics to manage
Top indicators for retail in visitor attractions:
- Visitation or attendance growth
Any analysis on ticketing performance should begin with understanding the topline tickets, by sold and scheduled/redeemed, by products, types and channel, with comparison to the prior period and – due to its seasonal nature – yearly growth.
- Average Order Value (AOV)
Analyze the average amount spent over a ticketing transaction, where the order may include multiple tickets. This can then be analyzed by other dimensions, such as visitors from out of town versus locals or online versus onsite purchases.
- Party size
Analyze the average number of visitors entering in a ticket transaction, where the order may include multiple tickets. This can then be analyzed by other dimensions or the order composition, such as the number of grandparents visiting with grandchildren.
- Capacity sold
Analyze the amount of capacity or issue onsale in terms of the percent sold down and the tickets remaining. This can be monitored for the percent sold by the number of days out, and analyzed aspects such as the ticket product or the day of performance to gauge if sales are on track.
- Redemption versus attrition rate
Advance passes who do not show up are known as ticketing attrition (the reverse of redemption). This derived metric helps understand opportunity cost of lost visitation or issue overage on capacity.
- Booking lead time
Advance passes that are booked earlier than the same day can be analyzed with booking curves to understand how far in advance visitors book and the analysis of dimensions such as channel or origin. This is a derived metric between the sold and scheduled date.
- Performance against goal
Ticket targets by product, type or channel, or by activity, set all the way down to a daily granularity.
- Conversion rate
Where experiences are cross or upsold to general admission (such as a special exhibition or fast pass experience), including a conversion rate against visitation for the activity.
- Price paid
Particularly if ticket pricing is variable or dynamic, analysis around pricing such as per caps or average price paid will also be useful.
Enriching ticket data
Ticket data can be enriched with data from other data sets, either your own or third party:
- Use the ticket ID to enrich with other interactions, such as a survey result or location analytics
- Use the member ID to enrich with customer data
- Use the zip code to enrich with demographic data
Common ticketing insights
When looking for insights from ticketing analytics in visitor attractions, try:
- Assessing the influence of weather, seasonality and holidays
- Analyzing the entrance taken by the visitor if multiple
- Checking ticket cannibalization between parallel activities
- Analyzing the performance of various promotions and discounts
- Attending to revenue assurance such as checking the use of specific discount codes or the volume of refunds
- Analyzing local, interstate (potentially divided into drive in versus fly in) and international visitors and comparing zip codes with member origin for targeted marketing campaigns
- Contextualizing per cap or other metric trends and patterns elsewhere to who is visiting, such as based on ticket types or visitor origin
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