“Consumer data will be the biggest differentiator in the next two to three years. Whoever unlocks the reams of data and uses it strategically will win.” – Angela Ahrendts Senior Vice President of Retail at Apple Inc
The Same is true for Media, Hospitality, and the transportation industry.
Media, Hospitality, and Transportation have been one of the most profitable industries as of now. Even a few countries’ GDPs become 70% dependent on the travel, tourism, and media business. Consequently, the pay scales of promising candidates in these sectors also kiss the sky. The role of data science in Media Hospitality and Transportation has been talked about in this blog.
But a pandemic hit in the last couple of years has created an immense impact on these industries. Pandemic has increased the unemployment rate in these industries. The New York Times has revealed that about 37000 media employees (highly paid) lost their jobs. Even 30 million American Citizens from these industries become jobless. If this is the image of the US, then you can easily imagine the picture of India and other Asian countries. Let us see how data science in media, hospitality, and Transportation has helped the businesses to boom.
The most depressing thing is the higher your salary is, the greater your chances of losing the job.
So, is not there any way for media, hospitality, and transportation pros to secure their lucrative career?
Certainly, there is a way.
In this article, I have described the challenges faced by the Media, Hospitality, and Transportation sectors. Based on my case study in these sectors, I believe that revolutionizing technology, such as Data Science and Artificial Intelligence, has benefited these industries in overcoming their obstacles to increasing corporate efficiency.
The media industry has been known to be one of the most lucrative industries. Due to the surge in Pandemic, many movie productions were stopped therefore incurring a lot of loss for the whole crew. The struggling actors especially had a very hard time, as discussed in this Times of India issue. The competitive algorithm of social media platforms has ultimately hit the media Industry Quiet hard.
The hospitality industry was affected gravely by the pandemic’s rise and the imposition of a global lockdown. Many small restaurant chains whose families depend on it had to shut down. E_ven hotels were affected when the majority of the people were being separated from their families due to COVID, and the hotels had to spend a large amount of revenue to equip these people whilst maintaining the integrity and safety of the hotel staff_. This had a lot of people quitting their jobs and being unemployed for a very long time.
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Transportation is not indifferent to losses. The pressure on the organization during this coronavirus pandemic is from the relocation of citizens to the core transport with the basic workforce to keep cargo and key and important workers on the move. Shifted to system maintenance. The side effect of this shift is the sudden change in the revenue sources of transportation companies, many of which are experiencing unexpected financial constraints. Organizations need to plan to ensure that the transport network is ready to return to normal if the coronavirus pandemic blockade is lifted._ An article by Deloitte has outlined all the shortcomings of the transport industry._
So what can be done? These industries need to make space for more data science jobs to revive their infrastructure. Data science tools will allow a much better understanding of the pain points in the domains and help with their growth even during a natural disaster. If this isn’t convincing, let us see the need for data science in the aforementioned sectors.
Need for Data Science in Media, Hospitality, and Transportation
Every day, digital reality presents new challenges to big players in the entertainment and media markets. Customers are more likely to seek out the best service, no matter the circumstance. This market is becoming more competitive by the day. The application of data science to various aspects of daily life is a new trend that requires entertainment and media professionals to think creatively.
- Personalized Marketing A company’s ability to attract customers’ attention is crucial, especially if it is involved in the entertainment and media business. It is more difficult to keep customers’ attention when they have had a quick and memorable online experience.
- Real-time analytics The name real-time analytics refers to the ability to process data quickly and present the results in a short time. The speed at which media and entertainment companies process the data is crucial.
- Content distribution on social media Social media has given media and entertainment companies a wonderful opportunity to strengthen their marketing strategies through the powerful tool of social content distribution. For large media companies, it is now possible to view general tendencies, user preferences, experiences, interests, and histories in one click.
- Analysis of media content usage Big media companies can use data science algorithms to make data work for them and generate profit. Media content analysis is a well-developed method that analyses the message and connotations of content. The media content analysis process consists of three main levels: capture and understand. The algorithms identify patterns and incidents within the text. The data can then be processed. These frameworks are used to define the tone of the text. Its influence on the user can be predicted.
AI and DS technologies are being used in many industries, but I am most excited about how these can be applied to the hospitality industry. Recently, however, I noticed a lot more innovation in the hospitality industry. The industry is embracing these advanced technologies with open minds.
Machine Learning systems enable hotels to analyze historical data to make better decisions. Marketers can use all these inputs to target the right audience at the right moment with targeted campaigns.
- Revenue management
When data drives revenue management, hotels can forecast demand and analyze customer behavior patterns more accurately. It automatically integrates and analyses stacks of data from multiple sources, saving a lot of labor.
- Booking engine
AI-powered booking engines collect data each time a potential guest interacts with the website. These engines often have advanced learning algorithms to analyze the data to learn more about customers and provide the best price.
- Reputation management
Reputation management systems have been gaining much traction in recent years within the hospitality industry. It makes sense, right? They help you build trust and brand loyalty. These systems can be AI-based, which increases their capabilities. Sentiment Analysis can be used as a great example. Natural language processing (NLP) is used to deduce the intent or sentiment behind an opinion or review. It is a highly effective method of gauging people’s thoughts about your brand.
The promise of data science Combines those data sources with external data sets (e.g., search, social media reviews, weather, traffic reports, weather, and weather) to solve problems, reduce costs, and predict future events.
- Enhanced Customer Service
Personalization has another advantage. This is allowing the transportation industry to improve customer service. Delta Airlines provides a Guest Services Tool for their SkyPro devices to their flight attendants. This tool and device can be used by flight attendants to improve customer service by reviewing preferences. United Airlines attendants also have access to a tool that gives them information about customers, such as last flight details, dietary requirements, and their connection schedule.
- Identifying MVCs (Most Valuable Customers) Some customers will travel more than others; it is a fact. Companies need to be aware of major players to avoid customer churn. Already, the travel industry has a huge legacy of data about MVCs from loyalty programs. Combining historical data with predictive and real-time analytics is the key to anticipating what MVCs want in the future. It is much more expensive to acquire a customer than keep an existing one.
- Up-Selling and Cross-Selling Let’s suppose you are traveling to Buenos Aires to attend a four-day business conference and that you decide to spend a weekend exploring the city. You want a flight that departs Sunday and arrives early Monday. Your airline will offer cross-selling and upselling opportunities if it has DS.
- Safer Travel DS can save lives when it comes to safety. A wide range of sensors is available in today’s automobiles, trains, and planes. These sensors provide control centers with continuous streaming data that provides real-time information on all aspects of the journey (e.g., driver behavior, environment, performance, etc. ). Transportation data scientists have this information and develop complex algorithms to predict and even prevent problems.
What are data science tools used in the media, hospitality, and transportation industries?
There are various DS tools used in all these industries. These are some of the tools and their uses.
It is one of the data science tools designed specifically for statistical operations. SAS is a closed-source proprietary software used by large organizations to analyze data. SAS uses the base SAS programming language to perform statistical modeling. Professionals and companies that develop reliable commercial software use it extensively. SAS provides many statistical libraries that Data Scientists can use to model and organize their data.
It is another popular Data Science Tool, BigML. It offers a cloud-based, fully interactive GUI environment for processing Machine Learning Algorithms. BigML is standard software that uses cloud computing to meet industry needs. Companies across their business can use Machine Learning algorithms. It can be used for product innovation, sales forecasting, and risk analysis.BigML is a leader in predictive modeling. BigML uses many Machine Learning algorithms, including classification, clustering, and time-series forecasting.
MATLAB is a multiparadigm numerical computing environment that processes mathematical information. It is a closed source software that allows for the implementation of matrix functions and statistical modeling. MATLAB is used most often in many scientific disciplines. Data Science uses MATLAB to simulate neural networks, fuzzy logic, and more. You can create stunning visualizations with the MATLAB graphics library. MATLAB can also be used for signal and image processing. Data Scientists will find it very useful as they can use it to tackle any problem, including data cleaning and analysis, advanced DeepLearningalgorithms, and even data extraction.
The most popular Data Analysis tool. Microsoft originally developed Excel for spreadsheet calculations. Today, Excel is used extensively for data processing, visualization, and complex calculations. Excel is a powerful analytical tool for Data Science. Excel is still a powerful tool for data analysis. Excel is an excellent analytical tool for Data Science. Excel is still a powerful tool for Data Science. Excel has many formulae, tables, and filters. Excel allows you to create custom functions and formulae. Excel is not the best tool for large data sets, but it can create powerful visualizations and spreadsheets. Excel can be connected to SQL to manipulate and analyze data. Many Data Scientists use Excel for data cleaning because it offers an interactive GUI environment to pre-process information quickly.
Companies Hiring Data Scientists in the Media, Transportation, and Hospitality Industry
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Source: Glassdoor Salary Insights
Data scientists’ salary in India is between Rs 4.9 Lakhs and Rs 28.0 Lakhs, with an average salary of Rs 13.2 Lakhs.
ABC offers about 19.2 Lakhs per annum for a data scientist with more than 10 years of experience.
Zee Entertainment offers 15.9 Lakhs per annum for DS specialists with 1-5 years of experience.
Times Internet offers 11.4 lakhs per annum for Data scientists with 2 to 3 years of experience in the field.
Fork Media offers 7.4 lakhs per annum for data scientists with 2 to 3 years of experience.
Pratilipi offers 30.9 lakhs per annum for DS specialists with 3 to 5 years of experience.
The average Data Scientist’s salary in the hospitality industry is in India at Rs 14.9 Lakhs per annum. This includes employees with less experience than one year and up to five years. Data Scientist salaries range from Rs 10 Lakhs up to Rs 22 Lakhs.
Oyo Rooms offers about 14.9 Lakhs per annum for a data scientist with more than 10 years of experience.
Taj Coromandel offers 27.1 Lakhs per annum for DS specialists with 1-5 years of experience.
Citadel offers 12 lakhs per annum for Data scientists with 2 to 3 years of experience in the field.
Marriott offers 9.4 lakhs per annum for data scientists with 2 to 3 years of experience.
ITC offers 31.8 lakhs per annum for DS specialists with 3 to 5 years of experience.
Image by Author
Source: Glassdoor Salary Insights
Data scientists’ salary in India, with less than one year experience or more than 12 years, is Rs 7.0 Lakh to R 33.6 Lakh. Based on 76 salaries, the average annual salary is 18.4 Lakhs.
- Uber offers about 29.5 Lakhs per annum for a data scientist with more than 10 years of experience.
- ElasticRun offers 9.3 Lakhs per annum for DS specialists with 1-5 years of experience.
- Citadel offers 12 lakhs per annum for Data scientists with 2 to 3 years of experience in the field.
- RedBus offers 15.6 lakhs per annum for data scientists with 2 to 3 years of experience.
- Indian Railway Catering and Tourism offers 31.8 lakhs per annum for DS specialists with 2 to 4 years of experience.
How can Learnbay help you? Learnbay offers one of the best data science courses in Bangalore and also offers an array of domain electives. One of which is Media, Hospitality, and Transportation. Let’s see some of the features of Learnbay and its domain electives.
Objectives of the electives
- How to identify hotel problems
- Data collection, storage, and manipulation
- Processing data
- Model selection algorithms
- Security and deployment
- Interpretation of data
- Implementation of data insights
- Taking the right business decisions
- Improved business strategies
- Improved identification of the target audience
- Satisfying customer needs
Learnbay teaches you practically through projects. These are some of them.
1. Ola/Uber Taxi Demand Prediction (Transportation Domain)
Taxi-hailing companies must predict taxi demand to optimize their fleet management and understand their needs.
We would build a model that would use users’ ride request data. It would include attributes such as ride-booking time, pickup location, and drop point latitude/longitude. This model would predict the demand for taxis in a specific city area. It would also help companies optimize taxi concentration to meet users’ needs.
2. Netflix Movies and TV Shows (Media and Entertainment Domain)
Discover what other insights you can get from the Netflix list of movies and tv shows available as of 2021. This project is Data Analysis with Python. It will analyze a data set of Netflix movies and TV shows. This data set was derived from Keras. To analyze the data and visualize the information about the movies and tv shows, you will be using Keras. This project uses Python and pandas and NumPy, NumPy, and matplotlib to analyze. It is also intended to help you complete the Jovian’s Data Analysis using Python – Zero to Pandas course. This course is well-structured and was delivered with great interest for the learners.
3. Airbnb New User Bookings(Hospitality Domain)
Users who are new to Airbnb can book a space to stay in over 34,000 cities spread across more than 190 countries. With the ability to predict accurately where the user is likely to book their first trip, Airbnb can share more customized content with their customers and reduce the length of time before booking their first trip and improve their forecast of the demand. This project mainly focuses on the advanced application of XgBoost.
The role of data science in media, transportation, and hospitality is huge. The media, Transportation, and Hospitality industries have a great future with data science. That includes even natural disasters like Pandemics or even a tsunami. With the reasons mentioned above and tips, we’re sure you will get a good chance in this domain.
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