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Every concert (or for that matter, every live event), has it’s share of “secondary” market for the tickets. That market is what we call “black market” in India, where the tickets are sold at a significant premium. In a sense, this is corruption, and since tackling corruption is all the rage, concert organisers are beginning to turn to analytics to curb the grey market. The profits that are booked by the secondary market is actually the money that the event organisers are leaving on the table and that revenue upside is what is hooking their interest.
The answer to questions about how to capture the additional value is a dynamic pricing model. Under this system, every item of value - entry tickets, merchandise, refreshments - are priced differently over time, with the price at a particular time reflecting the demand at that instant. Did I hear some of you sigh and call this yet another veiled attempt at charging more money? Let me assure you that this is not about charging more, this is about charging a justified amount for a particular experience.
How can analytics help win dynamic pricing? The key to a successful dynamic pricing is to know the right price boundaries - how low is too low, and how high is too high? It has been seen from simulated and actual studies at events that setting the wrong price constraints can actually reduce the gate revenue for the organisers. By churning the past data, we can statistically model price elasticity. This would tell us just how much demand can we expect at a given price. By segmenting the market well, we can determine the price elasticities of each segment.
The end pricing model would have something for everyone. This means, that some fans who would otherwise have been left out due to a high fixed ticket price, would now enjoy an entry into the concert, even if it comes with fewer bells and whistles.
The audience that can pay more would now get a far superior experience rather than just a transaction of ticket buying and attendance. Therefore, everyone wins under such a pricing model.
Knowing, and understanding the customer is the key to the success of dynamic pricing. With oodles of transactional data available, and with sophisticated techniques such as clustering, it isn’t difficult to know the fans well. All it requires is a team of business analytics practitioners rolling their sleeves up and getting ready to dive into the numbers.
About the Author
Soumyadip Pal is a retail analytics professional and a passionate educator with more than 8 years in the industry and more than 7 years in the academia, currently working as a consultant with Manipal Prolearn.