Analyzing the price sensitivity of activities at your attraction

Has your attraction’s executive team ever wondered whether the price of tickets or activities were too low? Too high? Or wanted to understand whether increasing ticket prices would lead to an increase or decrease in visitation?

Understanding price elasticity across general admission and experiences – such as exhibitions, rides, tours, other activities and events – informs the opportunities of making price increases to enable bolder moves. It could mean fewer market changes and increased profitability or funds available to reinvest into your organization.

At the IAAPA Expo in Orlando, Keith Laba, Chief Information & Analytics Officer from the Arizona Science Center, presented on balancing attendance with visitor happiness, including a clever approach to thinking about price sensitivity. Laba’s approach creates a framework by which the visitor attraction can interpret price versus attendance insights.

A popular visualization in Dexibit plots daily average attendance on the vertical axis against price on the horizontal axis, identifying a trend line (if any) on whether higher prices reduce activity popularity. The visualization can be filtered specifically for various activity segments such as exhibitions, experience or events or date ranges and queries applied to deepen the analysis – for example, to see if sensitivity has changed over the course of time.

Do check your visitation business rules defined for each activity on whether free tickets are included (ideally these should be excluded from at least this analysis if not all results, or at least the percentage of giveaways noted within the result). As always, correlation does not necessarily mean causation – for example, timing against seasonality may have a significant impact on the results, as could marketing budgets and press coverage.

Laba’s approach provides an easy visual template to lay over your scatter plot, creating a quadrant to categorize activities into four equal sections:

 

 

  1. Home runs

Successes appear on the top right, where visitation is significant despite higher prices (activities which may even tolerate higher prices still). This quadrant can be used as a way to qualify blockbuster outcomes.

 

  1. Lost opportunities

Underpriced activities are grouped towards the top left, where visitation is high though prices are low. The strength in visitor volume indicates that the activity could support a higher price point which may depress visitor numbers somewhat while still achieving a good overall visitation result at greater total revenues.

 

  1. Price sensitive 

Overpriced activities are grouped towards the bottom right, where visitation is low against a high ticket price. There may be additional circumstances to consider, such as if particular activities are intentionally priced as premium offerings or capacity is limited (at which point the context of capacity utilization should be considered).

 

  1. Missteps 

Activities that did not achieve strong visitorship despite low prices show on the bottom left corner. Again, additional information may flag particularly activities as intentionally so, such as exhibitions hosted for important social educational outcomes aimed at select audiences (at which point the context of goal achievement should be considered).

 

Looking at these groups closely, we can see what elements they have in common, such as the activity’s categorization or resonance factor. If you have more than one venue in your portfolio, you might gain additional insights from comparing this data across multiple sites. This finding can help drive the product strategy, assuming revenue and indeed visitorship are high priorities.

By isolating each group into a separate analysis (if enough data exists), we can then see whether a more detailed linear trend exists and its strength, between price and visitation for each of these quadrants to help isolate the optimal ticket price. This may reveal additional insights, such as that the degree of elasticity changes between the quadrants – potentially home runs may be less sensitive to even higher prices than other segments.

The same can be repeated to slice various pricing blocks, especially in analyzing an anecdotal price ceiling recommendation (if data points exist over it). Machine learning models for predicting or simulating activity outcomes, such as exhibition forecasting, can be used to refine price optimization alongside its relationship with marketing budgets and other factors.

 

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