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MakeMyTrip Dynamic Pricing

Data Science
With E-commerce and online travel companies’ activity generating too much data for a team of humans to handle, Machine learning technology is now being used to help these companies make critical business decisions. ML solves this issue because it can process data faster without stopping and delivering the best-in-class end-to-end experience to its users.
MakeMyTrip Dynamic Pricing

MakeMyTrip is an Indian online travel company founded in 2000. Headquartered in Gurugram, Haryana, the company provides online travel services including flight tickets, domestic and international holiday packages, hotel reservations, rail, and bus tickets. With over 47% market share, MakeMyTrip is India’s first and biggest travel company. In fact, one in every four passengers at an airport is their customer. As of 31 March 2018, they have 7 million monthly active users with14 company-owned travel stores in 14 cities, over 30 franchisee-owned travel stores in 28 cities, and counters in four major airports in India. MakeMyTrip has offices in New York, Singapore, Kuala Lumpur, Phuket, Bangkok, and Dubai.

MakeMyTrip is planning to expand its user base to 100 million by making use of the latest tools and techniques in the field of Data Science and Analytics. Driven by the desire to provide an exceptional travel experience for its customers, they have continuously stayed ahead of the curve by developing technology and products to meet the ever-changing demands of the rapidly evolving travel ecosystem. Using data in building one of the best dynamic pricing systems to cater for the needs of the customers by providing a truly personalized experience to each of its users is the key to the MMTs success story.

Dynamic pricing for a dynamic market

A well-developed ML algorithm can even learn and make pricing suggestions in real-time, dynamic pricing is something that even small retailers and e-commerce players can now use to compete in the retail market. This allows retailers to set product prices based on supply and demand, also known as dynamic pricing. To put it plainly, ML is valuable because it automates a task that is almost impossible for humans to do manually.

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ML-powered software gets information from data to throw up dynamic pricing solutions. But before that, the retailer needs to know his customers and what data is incoming.

ML works on a simple philosophy — the larger the data sets, the better the learning process, and the better the outcome. Over time, ML-based software only improves in performance.

Analysts can take other factors into account for dynamic pricing. For example, an analyst could choose weather, demand, operating costs of the company, competition, the minimum price, and the best price, etc.

In the end, for a competitive pricing strategy, ML solutions can repeatedly scrape the web to collect important information about prices set up by competitors for similar products, what consumers’ opinions were about products, as well as the pricing history over the last number of days/weeks.

How to develop a general dynamic pricing model

The most important aspect that is to consider is the level of granularity you are aiming for. For example, are you look at a single customer level or an entire segments?

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Another crucial factor is defining strategic goals that align with business goals. Profit maximizing is obvious, right? But you could also choose goals for getting new customers or satisfying old customer satisfaction.

The ML-based dynamic pricing model can then be developed once the answers to the above points come in. The model will predict whether someone will make a purchase at a price best optimized at that moment in time. The models can be used either using the Generalized Linear Models (GLMs), or the Deep Learning methods.

  • Dynamic pricing based on groups

Customer segmentation is the practice of dividing the customers base into groups of individuals that are similar in specific ways relevant to marketing. This can be as simple as a split A/B test or more sophisticated by predicting a higher willingness to pay based on machine type, location, demographic information, etc.

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Customer segmentation relies on identifying key differentiators that divide customers into groups that can be targeted. Information such as a customers’ demographics (age, race, religion, gender, family size, ethnicity, income, education level), geography (where they live and work), psychographic (social class, lifestyle and personality characteristics) and behavioural (spending, consumption, usage and desired benefits) tendencies are taken into account when determining customer segmentation practices.

MakeMyTrip’s customer segmentation operates under the fact that every customer is different and that their price would be better served if they target specific, smaller groups such that those consumers would find relevant and lead them to buy something. MMT also hope to gain a deeper understanding of their customer's preferences and needs with the idea of discovering what price segment each group finds most valuable to and tailored to more accurately discounts and coupons toward that segment.

MakeMyTrip segregated users into different buckets to understand what they can do differently for different user cohorts and how they can build a more relevant product experience for the users. Each of the users is dissected into the following categories:

  • When a user has not historically searched with us
  • When a user has searched with us before
  • Searched for a flight
  • Searched for a hotel
  • Searched for a holiday destination
  • Searched for more than one i.e. flights and hotels both, hotels and holidays etc.
  • When a user has searched and booked with us before and is coming back again
  • When a user has an upcoming booking
  • If the booking was on the day of travel
  • When a user has completed a trip with us

In each of the above scenarios, MakeMyTrip needs to serve a very different kind of information to the user. For example, if a user has not searched with MMT earlier, they present the user with the most relevant content which inspires the user to travel. If a user has already searched, they identify the most relevant information which will aid a user in making a booking. When a user has made a booking, the most important post-booking scenarios which a user needs to be made aware of needs to be highlighted.

2. Dynamic pricing based on time

My friend used to always discuss this form of dynamic pricing — having a price go up or down based on time. In its most basic form, you’ll see this purely in a lot of industries— at the end of the month prices are lower as salespeople push for quotas. We see people wait for the festive season to book their holidays as the price usually goes down. Time-based pricing is a pricing strategy in which businesses set flexible prices for products or services based on current market demands.

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At MakeMyTrip, one of the core components to make the user experience seamless and immensely relevant is to show the right information to the user at the right time and with the right offers. Most of you would agree that travel booking involves a lot of planning, be it leisure trip or business travel. Depending on where, with whom and under which context one is travelling, the user preferences and the choice varies. MakeMyTrip strives to solve this by considering user preferences, search history, and leveraging their years of travel booking experience.

3. Price Elasticity of Demand

price elasticity of demand (PED), describes how the quantity demanded by consumers will respond to a change in price. Dynamic pricing models utilize vast amounts of historical data on demand and prices to determine how the nature of the relationship between these two variables. Generally speaking, price elasticity is determined by factors such as availability of substitutes; the proportion of the consumer’s budget that must be allocated to purchasing the item; degree of necessity; and brand loyalty.

While historical data can, in theory, give an accurate estimate of consumers’ price elasticity, the predictive ability of such estimate is often weak, and it requires more data points than most firms have available to obtain a statistically valid result. However, to increase the level of sophistication, it can be relevant to include an analysis of patterns in price elasticity determinants, to which heuristics and qualitative interpretation can be applied.

For MakeMyTrip, it is crucial to know how high the elasticity of the demand is, in which periods and how the price willingness of the customers behaves depending on it. In simple terms, this means: “How strongly do my potential guests react to price changes?”

Based on that, a well-founded decision can be made about the room price for a specific day/period to exploit the sales potential as much as possible. If the price elasticity is ignored, the hotel may be empty if the price is too high and full if the price is too low, but the rooms will be sold at far too low a price.

Ideally, the hotelier would have to recalculate the price elasticity every day or approx. once a week. Only then would he have the optimal basis to price his rooms correctly daily.

Benefits of Dynamic Pricing Model

1. It can be used as a way to boost sales

Dynamic pricing is frequently thought of as a means for firms to raise their prices.

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Although this is true to a degree, the method may also be utilised to reduce pricing. A reduced price can sometimes spark sluggish sales, allowing a company to reach its sales revenue goals for a day, a month, or even longer. A simple flash sale is one approach to encourage the usage of dynamic pricing at the location.

2. It can be used to maximize profits

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Dynamic pricing is a method that may be utilised to optimise earnings if rivals are selling goods or services at a much higher price. If you know what potential consumers desire ahead of time, you may change the pricing of products depending on their purchasing habits.

3. It can create higher levels of demand

Because empty seats equal zero revenue, dynamic pricing is commonly utilised at events.

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If seats are still available on the day of the event, certain customers may be able to get them for a reduced price. This helps you to optimise earnings while also allowing you to take advantage of whatever revenues are available at the moment. This procedure can be found at MakeMyTrip.

4. It provides more insights into customer behaviours

The demand curve for each client becomes easy to compute with dynamic pricing. This curve depicts the lowest and highest amount a customer is prepared to pay for a certain transaction. This curve is created using a variety of data points, including the device being used for shopping. More insights into customer behaviour may be gained with this additional data, which makes it more likely that a sale can eventually happen.

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Businesses that use machine learning-based models for dynamic pricing see an improvement in profits of 10% or more, as well as a 10% rise in sales. Dynamic pricing, which adjusts rates in real-time, does this for your company.

Conclusion:

Most proponents of dynamic pricing argue that we should simply state that we use dynamic pricing on our site. This is fantastic, but it still doesn’t address the cognitive dissonance of wondering if you’re receiving the best value possible.

It’s worth noting that all of the above solutions address both transparency and the sensation of being left out. That said, while dynamic pricing is unlikely to get us in legal problems, we must be mindful of our brand, image, and any potential PR reaction that might have a long-term impact. We’ve discovered that pricing transparency is critical, and we’ll continue to push for it.

It feels good to operate with a team that is so self-reliant and motivated. I would like to thanks Md Asif and Prashant Bharadwaj for their invaluable contribution to this article

References:

  • https://www.makemytrip.com/blog/tamil/node/21852
  • Mobile Application Analytics — MakeMyTrip | by Srijan Rana | MakeMyTrip-Engineering | Medium
  • Customer Story: MakeMyTrip — Databricks
  • Wisdom of the better few | Proceedings of the fifth ACM conference on Recommender systems
  • Dynamic Pricing Guide: How to Implement a Dynamic Pricing Strategy (priceintelligently.com)
  • https://www.rateboard.io/en/blog/details/price-elasticity-in-the-hotel-industry
  • https://blog.pricebeam.com/using-price-elasticity-in-dynamic-pricing-models
Dristanta Nirola
Data Science Programmer at Flutura Decision Sciences & Analytics

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