Pivoting in a Pandemic for Leisure Travel Firms

Posted on May 12, 2020

Photo by Aaron Birch on Unsplash

Shiny App | GitHub Repository | LinkedIn


The spread of COVID-19 has catastrophically damaged the travel industry based on the data collected throughout the pandemic. With obliterated revenues, many travel companies are making drastic decisions: “How can we stay afloat until the travel industry resumes?” These choices often include massive layoffs, massive loans, or bankruptcy. How can a travel company pivot their business to stay viable during this pandemic? Can pivoting prepare the company to lead the pack when the travel industry bounces back looking and functioning very differently than it did pre-pandemic?

Benefits of pivoting a business model versus cutting back until travel becomes viable again include generating revenue when future revenue from the old model is uncertain, retaining employees, their expertise, and the costs of their training, and retaining existing connections with networks in travel destinations and with vendors.

Instead of targeting people who travel temporarily, a business can target people who are more likely to continue to travel during a pandemic for reasons such as work relocation, education relocation, or retirement relocation.

Traveling for work, school, or retirement is often a large ordeal, a long time in the making. People traveling for these reasons may be less likely to cancel their plans than people traveling for mere days or weeks - visas, jobs and academic programs have set start dates and timelines, homes are sold/rented, possessions are sold/temporarily stored until ready to move, and financial commitments at the destination may be ineligible for refunds, like purchasing a home.

Despite the differences between permanent and long-term migrants and short-term travelers, many needs and wants overlap, like exploring the destination, making local connections, and accessing local resources.

New Offerings

Offerings that the travel company might design for its new groups of customers may include:

  • Many of the same types of travel offerings made to short-term travelers:
    • Geographic destinations
    • Cultural attractions
  • Recommendations and discounts on moving resources:
    • Neighborhood/housing help
    • Locating grocers, banks, hospitals, and other essential businesses.
  • Connecting migrants:
    • Guides, organizations, social sponsors
    • Ex-pats from their own countries
    • Other recent migrants

Data Collection

Deciding how to pivot in an effective and efficient manner requires data. While one case study cannot be extrapolated out to all destinations, it can illustrate the questions and analysis needed to successfully shift. The case study included here focuses on migration to New Zealand, and includes a sampling of extensive and ongoing data collection. It is not a comprehensive analysis, and leaves many questions. The insights it does provide are an excellent starting place, and the questions it leaves allow for a deep dive into the expanded data collection and related data sets from other sources.

The data tables used for this case study were acquired from the New Zealand government:

The regional shapefiles were also acquired from the New Zealand government:

The data needed minimal processing - there were few null values to eliminate and no statistical methods were run on the data.

Interactive Visualization App

The app is a Shiny Dashboard app, the charts were made with ggplot2, and the map was made with Leaflet.

The skills I demoed here can be learned through taking Data Science with Machine Learning bootcamp with NYC Data Science Academy.

About Author

Sita Thomas

Knowledge is power. I leverage fierce curiosity and creativity to deliver immediate and impactful results for mission-driven companies by weaving together 20 years of statistics, engineering, and business development. I've worn many hats across a variety of industries,...
View all posts by Sita Thomas >

Related Articles

Leave a Comment

No comments found.

View Posts by Categories

Our Recent Popular Posts

View Posts by Tags

#python #trainwithnycdsa 2019 2020 Revenue 3-points agriculture air quality airbnb airline alcohol Alex Baransky algorithm alumni Alumni Interview Alumni Reviews Alumni Spotlight alumni story Alumnus ames dataset ames housing dataset apartment rent API Application artist aws bank loans beautiful soup Best Bootcamp Best Data Science 2019 Best Data Science Bootcamp Best Data Science Bootcamp 2020 Best Ranked Big Data Book Launch Book-Signing bootcamp Bootcamp Alumni Bootcamp Prep boston safety Bundles cake recipe California Cancer Research capstone car price Career Career Day citibike classic cars classpass clustering Coding Course Demo Course Report covid 19 credit credit card crime frequency crops D3.js data data analysis Data Analyst data analytics data for tripadvisor reviews data science Data Science Academy Data Science Bootcamp Data science jobs Data Science Reviews Data Scientist Data Scientist Jobs data visualization database Deep Learning Demo Day Discount disney dplyr drug data e-commerce economy employee employee burnout employer networking environment feature engineering Finance Financial Data Science fitness studio Flask flight delay gbm Get Hired ggplot2 googleVis H20 Hadoop hallmark holiday movie happiness healthcare frauds higgs boson Hiring hiring partner events Hiring Partners hotels housing housing data housing predictions housing price hy-vee Income Industry Experts Injuries Instructor Blog Instructor Interview insurance italki Job Job Placement Jobs Jon Krohn JP Morgan Chase Kaggle Kickstarter las vegas airport lasso regression Lead Data Scienctist Lead Data Scientist leaflet league linear regression Logistic Regression machine learning Maps market matplotlib Medical Research Meet the team meetup methal health miami beach movie music Napoli NBA netflix Networking neural network Neural networks New Courses NHL nlp NYC NYC Data Science nyc data science academy NYC Open Data nyc property NYCDSA NYCDSA Alumni Online Online Bootcamp Online Training Open Data painter pandas Part-time performance phoenix pollutants Portfolio Development precision measurement prediction Prework Programming public safety PwC python Python Data Analysis python machine learning python scrapy python web scraping python webscraping Python Workshop R R Data Analysis R language R Programming R Shiny r studio R Visualization R Workshop R-bloggers random forest Ranking recommendation recommendation system regression Remote remote data science bootcamp Scrapy scrapy visualization seaborn seafood type Selenium sentiment analysis sentiment classification Shiny Shiny Dashboard Spark Special Special Summer Sports statistics streaming Student Interview Student Showcase SVM Switchup Tableau teachers team team performance TensorFlow Testimonial tf-idf Top Data Science Bootcamp Top manufacturing companies Transfers tweets twitter videos visualization wallstreet wallstreetbets web scraping Weekend Course What to expect whiskey whiskeyadvocate wildfire word cloud word2vec XGBoost yelp youtube trending ZORI