Recover revenue through optimized ticket pricing
JCA Arts Marketing, together with our partners at Baker Richards in the UK, have partnered to provide a cost-effective price sensitivity analysis for organizations looking to recover revenue once their venues reopen post-coronavirus. This survey-based analysis will help you answer critical questions that affect pricing, including:
- How willing are customers to buy tickets?
- To what extent is pricing holding your audience back?
- Where do you have the ability to increase prices without undermining demand?
- For which segments should you consider strategic discounting?
A price sensitivity analysis will provide valuable insight into customer attitudes, allowing you to make strategic, data-driven pricing decisions.
The Need for a Price Sensitivity Analysis
As a consequence of the coronavirus pandemic, some customers will be less willing to return to venues and attractions for a variety of reasons. Some will have become acutely price sensitive due to changes in their circumstances, while others may be willing to pay more for something they have come to value even more by its absence.
These effects are likely to be temporary, but to heavily impact the 2020–21 financial year. Levels of price sensitivity will vary widely—up and down—by venue, activity, artform, and audience segment.
The Price Sensitivity Analysis tells you how your customers currently feel about your pricing, providing an essential evidence base for pricing for reopening after COVID-19.
Our Approach: Conjoint Analysis
Many research approaches to price simply produce a single figure—the "willingness to pay". This figure, however, is not useful to most organizations that recognize maximizing sales and income requires a range of different price points.
Our approach, using Conjoint Analysis, captures the different levels of willingness to pay across different segments of your market, enabling you to design optimal differentiated offers. This market research methodology is known for its high levels of accuracy at forecasting consumer behavior.