Develop A Movie Ticket Pricing System Using Machine Learning

We live in an era of personalization, as the days evolve nothing remains static. Through Machine Learning and Data Science nowadays it is possible to suggest, create a product according to the user’s choice and preferences. Personalization, that is recommendations are somewhat static-like Spotify or Netflix uses these algorithms to recommend users what they want to see or listen based on their previously played playlist.

There are further optimizations we can do using data science is to offer more personalized services, such as dynamic pricing used for Movie ticket booking.

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Dynamic Pricing:

Dynamic pricing is used to create different prices for different customers, based on their location or other circumstances. This method was first used for American Airlines in the year 1980. They figured out that not all customers are the same, they all have different concerns and different priorities. While some customers want tickets at a cheaper price while others want good service. This method is now used in all movie booking systems. There are some parameters, depends on which the price varies, like whether the customer wants to book a ticket in the front row or the back, or the customer wants an executive seat or a couple of seats. Not only the seats, but prices also depend on where the movie hall is located and which show timing the customer has chosen.

All ticket booking apps whether it is PVR or BookMyShow uses dynamic pricing. At times of high demand, when a new movie launches BookMyShow increases the price of the tickets.

The other way is to come up with discounts or to give users with customized offers for a limited period. In other words, dynamic pricing is a prediction problem, where machine learning is the best tool to tackle it.

Concepts used:

1. Python programming language
2. Machine Learning algorithms

Hardware and Software used:

1. A suitable OS
2. A desktop or laptop
3. Enough RAM to run the program without any interruption
4. Python 3 or upwards installed on your system 

Implementations:

1. Decide the public you are aiming at.
2. Mention the type of customer you are aiming
3. Collect historical data from on different price points offered in the past as well as the demand for these points
4. Build a model to predict the different parameters
5. Use an optimization algorithm to discover the optimal price and product features, to maximize the probability of purchasing. This method can also be used for creating product bundles and discounts.

Conclusions:

Dynamic pricing has a huge impact on sales and profits earned by companies. It can increase the profit from 10 to 20 per cent and also can increase the sales up to 200 per cent. This is why most of the leading companies and apps, be it BookMyShow or Flipkart or Walmart use dynamic pricing.

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Movie Ticket Pricing system using Machine Learning