July 8, 2021

Machine Learning Enhances Travel Industry

As the economy begins to reopen post-COVID, the travel industry will boom. The demand for faster, better, cheaper, and more scalable artificial intelligence (Ai) will be vital to keep up with customer demand.

Business leaders everywhere recognize that machine learning is the optimal way to accelerate and drive digital transformation but are unsure how to get started. Do you start your own data science team to custom-build machine learning models from scratch? Or do you partner with an experienced firm that has pre-built machine learning models ready to go?

If you choose to partner with a firm, ElectrifAi has the domain knowledge necessary to help you meet demand and handle:

  • Increased travel
  • Problems with intended destinations (i.e. continued shutdowns)
  • Personalized marketing
  • Brand loyalty
  • Regulatory and border concerns

At ElectrifAi, our pre-built machine learning models provide fast results. Whether you are trying to increase customers or optimize your operations, we can help you analyze large datasets to:

  • Understand the customer lifetime value
  • Gain insights into and strategically segment customers
  • Acquire and retain the right customers
  • Create a dynamic pricing strategy
  • And much more!

Let’s look at five machine learning models proven to be very valuable to the travel industry.

Customer Lifetime Value Optimization

The pre-built machine learning model Customer Lifetime Value Optimization predicts a customer’s remaining lifetime monetary value (in months) based on their status in the customer lifecycle. The model recommends the best action to maximize the customer lifetime value.

By using historical data points, you can measure a customer’s brand loyalty and estimate the timeframe they will disengage as a customer. By using this machine learning model, you can:

  • Know when to target customers and how much effort to expend.
  • Alter your marketing message, products, and promotions to align with the customer’s stage in the buying cycle.

Create a great customer journey and maximize the ROI for all your marketing activities. By focusing on optimization throughout the customer lifecycle, you can improve the customer’s brand loyalty and hopefully create lifelong customers.

Dilution Prevention

The pre-built machine learning model Dilution Prevention identifies customers most likely to purchase discounted upgrades to travel first-class without purchasing those tickets at full fare. This leads to a decrease in revenue.

By using this machine learning model, you can determine which customers are likely to do so and block that sale, thereby preventing and reversing future dilutionary behavior.

The travel industry can greatly benefit from this machine learning model for:

  • Airline and train seats
  • Hotel rooms and car rentals
  • Entertainment Experiences

Increase your top line revenue by holding out on offers not sold at full price so that customers willing to pay premium prices are able to do so. For example, say there is a customer who desires first-class treatment and has the money to pay the full fare. But, because all the first-class tickets were sold at discounted prices, that full fare ticket cannot be sold.

Destination Propensity

The pre-built machine learning model Destination Propensity identifies past or prospective customers more likely to book a trip for a specific destination. You can then create a personalized destination set for those customers and increase customer satisfaction.

With the predicted destination propensity score the machine learning model outputs, you can:

  • Increase buyer conversion with one-to-one target marketing of the right destination to the right customer.
  • Send marketing assets with relevant content, products, and promotions that are likely to resonate with that customer.

Encouraging customers to book a specific destination can help you increase your revenue through sales but also promotional incentives from those destinations seeking travelers. Knowing which destination is likely to entice a customer to follow through with a purchase is made a lot easier with machine learning’s fast and accurate recommendations.

Dynamic Pricing

The pre-built machine learning model Dynamic Pricing helps solve fluctuating supply and demand problems by changing the price of an item or service to meet consumer demand. This model predicts the vase sale price that is then adjusted when the variables change.

The benefits of this machine learning model include:

  • Create a dynamic pricing strategy to maximize profit and increase customer satisfaction.
  • Increase top line revenue by identifying the right price point to convert a shopper into a buyer.

Dynamic pricing can be especially helpful for the following scenarios:

  • Seasonal traveling
  • Ride-hail surge pricing
  • Random fluctuations in market conditions
  • Time-based pricing

Pricing within the travel industry is ever-evolving to match both economic patterns and to match customers with what they are willing to pay for. To accurately predict this, machine learning can provide you with recommendations that take thousands of data points into account.

Trip Narrative

The pre-built machine learning model Trip Narrative predicts customer complaint behavior to better address customer satisfaction. To do so, a trip journey dashboard is created and recommendations are provided that enable you to take immediate action.

The benefits of this machine learning model include:

  • Proactively contact dissatisfied customers after an event that may have led to a negative customer experience.
  • Reduce the chances of negative reviews through word-of-mouth and on travel websites.

Brand loyalty is made possible through positive experiences. One bad experience can cause a customer to never use your service or product ever again. To reduce the chance of this customer leaving, it’s crucial to reach out for immediate resolution to a problem.

ElectrifAi Can Help You Gain Insights into Your Data

Machine learning has the predictive power to increase your revenue, decrease your risk, and reduce your costs. By analyzing large datasets, the machine learning model can generate very personalized recommendations for your customers.

We have covered how machine learning can help you with Customer Lifetime Value Optimization, Dilution Prevention, Destination Propensity, Dynamic Pricing, and Trip Narrative. Each of these pre-built machine learning models is an excellent way to create long-lasting, satisfied customers.

Want to learn more about how your data can work for you? Contact us today for a custom demo!

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