Travel Package Purchase Prediction.

Background and Context

You are a Data Scientist for a tourism company named "Visit with us". The Policy Maker of the company wants to enable and establish a viable business model to expand the customer base.

A viable business model is a central concept that helps you to understand the existing ways of doing the business and how to change the ways for the benefit of the tourism sector.

One of the ways to expand the customer base is to introduce a new offering of packages.

Currently, there are 5 types of packages the company is offering - Basic, Standard, Deluxe, Super Deluxe, King. Looking at the data of the last year, we observed that 18% of the customers purchased the packages.

The company in the last campaign contacted the customers at random without looking at the available information. However, this time company is now planning to launch a new product i.e. Wellness Tourism Package. Wellness Tourism is defined as Travel that allows the traveler to maintain, enhance or kick-start a healthy lifestyle, and support or increase one's sense of well-being, and wants to harness the available data of existing and potential customers to make the marketing expenditure more efficient.

You as a Data Scientist at "Visit with us" travel company has to analyze the customers' data and information to provide recommendations to the Policy Maker and Marketing Team and also build a model to predict the potential customer who is going to purchase the newly introduced travel package.

Objective

To predict which customer is more likely to purchase the newly introduced travel package.

Data Description:

  • CustomerID: Unique customer ID

  • ProdTaken: Whether the customer has purchased a package or not (0: No, 1: Yes)

  • Age: Age of customer

  • TypeofContact: How customer was contacted (Company Invited or Self Inquiry)

  • CityTier: City tier depends on the development of a city, population, facilities, and living standards. The categories are ordered i.e. Tier 1 > Tier 2 > Tier 3

  • DurationOfPitch: Duration of the pitch by a salesperson to the customer

  • Occupation: Occupation of customer

  • Gender: Gender of customer

  • NumberOfPersonVisiting: Total number of persons planning to take the trip with the customer

  • NumberOfFollowups: Total number of follow-ups has been done by the salesperson after the sales pitch

  • ProductPitched: Product pitched by the salesperson

  • PreferredPropertyStar: Preferred hotel property rating by customer

  • MaritalStatus: Marital status of customer

  • NumberOfTrips: Average number of trips in a year by customer

  • Passport: The customer has a passport or not (0: No, 1: Yes)

  • PitchSatisfactionScore: Sales pitch satisfaction score

  • OwnCar: Whether the customers own a car or not (0: No, 1: Yes)

  • NumberOfChildrenVisiting: Total number of children with age less than 5 planning to take the trip with the customer

  • Designation: Designation of the customer in the current organization

  • MonthlyIncome: Gross monthly income of the customer

Data Transformation:

Key transformations include:

  1. Feature Creation: Variables were identified and transformed to capture important customer characteristics, such as income, passport ownership, age, and city tier, all influencing the likelihood of purchasing the travel package.

  2. Binning and Encoding: Customer characteristics like "City Tier" and "Designation" were transformed to categorical formats, aiding in capturing demographic nuances in the predictive model.

  3. Imputation: Missing values in certain columns were handled, through imputation strategies, to ensure a consistent dataset for analysis.

  4. Normalization: Monthly income and other continuous variables were normalized, ensuring the model captures relative differences without being influenced by scale.

  5. Feature Engineering: New features, such as interaction terms, were created to reflect customer preferences and lifestyle, enhancing model accuracy in predicting package purchases.

Business Recommendations

To maximize the effectiveness of marketing strategies for the new travel package, the company should focus efforts on high-income customers, as they are more likely to make a purchase. Personalized offers or premium packages could especially appeal to this demographic. Additionally, target customers who already hold passports through tailored email campaigns or special offers, as they are more prepared and inclined to travel. Age-based customization can further enhance appeal, with adventure packages for younger travelers and luxury or relaxation options for older customers. Corporate targeting is another opportunity, where discounts or tailored packages for executives and senior managers could increase engagement. Partnering with companies to offer travel incentives for employees may also drive interest. Frequent travelers would benefit from loyalty programs or discounts, encouraging repeat purchases through incentives for multiple bookings or referral rewards. Regional promotions based on city tiers can also be effective, with budget-friendly options for certain areas and luxury packages for more affluent regions. Finally, consider marital status-specific offers, such as group tours for single travelers or romantic getaway packages for married couples, to align with distinct travel preferences across demographics.