Uber, Ola and AirBnB all are known to have surge pricing in place. So do cineplexes. They increase their prices anytime a new movie of a superstar hits the theatres. Hotels, airlines are known to have done this for ages and done well.
In a press release yesterday, the ministry of Railways in India announced that they are “Introduction of Flexi Fare system for Rajdhani/Duronto and Shatabdi trains“. Though they refer to this as Flexi fare, this indeed is a move towards surge pricing or is it.
What has changed is that Toronto International Film Festival did a similar thing – they announced a 2 to 7 USD surcharge on their 49 USD tickets for movies that were the most popular one’s.
Increasing sophistication of software powering the application of pricing and ubiquitous nature of surge pricing is today fuelling adoption of variable pricing.
Current models follow the predictable path – have a base price, create an algorithm that determines interest and tracks sale and applies a price surge at a particular threshold. Once the sales/interest falls back within the threshold, the surge is removed.
In my opinion this is just the V1.0 of surge pricing. I will not be surprised if we find that there are more sophisticated mechanisms will be developed to make the surge more effective and will operate both ways – increase the price when the interest/sales cross a specific threshold and reduce the price when the lower end of the threshold is crossed as well.
Some thoughts where this could move towards:
Predictive model of surge pricing
In this model, the surge is applied based on an expectation of a surge of interest/sales.
For example if an online influencer tweets about an event, the system can anticipate a surge in interest. A surge in pricing could then be potentially applied even before the spike of interest and subsequent sales.
Artificial intelligence led surge pricing
We could potentially find ourselves in a place, where the price for a service could be completely determined by an algorithm. This could potentially take into account the person buying and their affiliation and connection with the organisation selling.
Let’s take the example of an upcoming movie starring a specific actor or directed by a specific director.
Now, if I am following the actor on Twitter, Liked the Facebook page for the film, watched and shared the trailer of the film, participated in a contest held for promoting a film (etc), I am assigned a score which is relevant for my interaction to buy the tickets for the movie.
The algorithm could learn about all of this and decide what should be the price of the ticket for me – could be higher than normal (assuming that I will be more than willing to pay a premium to watch a movie that I have been so long engaged with and talking about) or could be lower than the normal (assuming that I have helped in promoting the movie and for my loyalty to my star or the director), etc.
Auction Model led Surge Pricing:
This is the model that Google has made all its fortune off of. This is when instead of having a single price for a particular product, you get your prospects to out-bid other prospects for the previlege to own/experience the product.
For example, a theatre could run an auction for every seat in the theatre for a performance. The customers can pick a specific set of seats and place their bids for the same. The auction engine runs this auction and the highest bidder for a specific seat get that seat.
This could be an option for airlines seats, cineplexes, conferences, railway/bus tickets, car parking, workshops, fund-raisers, limited edition items, etc.
Whatever form this takes, I surely believe that we will only see an increase in both the number of products and services that will employ some sort of surge pricing and in the sophistication of the algorithm that will govern the surge pricing as well.
Every industry that is in the process of selling perishable services will apply some form of surge pricing in their pricing strategy.
Do you agree with my assessment of the situation?