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loginworksoftware-blog · 7 years ago

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How Data Processing is a Great Help to Tour & Travel Industry?

Introduction

The tour and travel industry is an unbelievably massive one. According to Jennifer Xiaoqiu Ma et al (2003), she identified tourism organizations as airlines, hotels, tour operators, visitor attractions as well as the tourism authorities. Each of these tourism organizations is a billion dollar sector both online and offline. The MIT Technology review on The Travel Ecosystem: An industry on the go estimated the growth of digitization of the tour and travel industry to be worth $305billion by 2025. The International Air Transport Association (IATA) predicts that by 2036, the number of travelers would have almost doubled passengers flying in 2017. Hence, there are so many opportunities for you as an investor, a business owner or if you want to scale up your existing business in this industry. There’s so much room for growth and data processing can give you the competitive advantage you require.

To succeed as an investor or business owner, you need to understand how data processing is a great help to tour and travel industry. PayPal co-founder Max Levchin said, “The world is now awash in data and we can see consumers in a lot clearer ways.” The key to data processing is seeing your customers more clearly. To broaden your knowledge, let’s understand data processing.

Data processing combines two words, data, and processing. Data are facts, statistics, details or specifics. Processing in this context means change. Processing is passing a raw material through a system to produce a new and useful product. The crucial word here is ‘useful’. Thus, Data processing can, therefore, be defined as collecting facts or specifics and putting those facts through systems to get important information that can be used in decision making. It is therefore important to study those results and let them give you a true picture.

Data You Need to Collect In The Tour And Travel Industry

As vital as data processing is to the tour and travel industry, the first and foremost step is collecting data and this is a huge problem. Pritesh Chauhan describes them as “often, voluminous, complex and mostly unstructured…” This industry generates tons of data daily. Amadeus, a player in the travel industry revealed that in 2016, it processed 595 million travel agency bookings with over 1.3billion passengers.

Every time someone conducts a search online or post on social media, data is generated. In an article published by Lucy Fuggle, she said that statistics can barely keep up and it is “number-heavy”. It is difficult to collect the required data and it is even more difficult to get meaningful results from the raw data. Data you need to collect include personal details such as name, phone numbers, emails, etc. other data are purchasing habits, itineraries, customer feedback and customer budget.

Purchasing habits: You need to know what travelers buy. You need information on every online and offline transaction conducted.

Itineraries: Travellers have a list of places they’d love to visit. You need access to this information. According to Anders Lundgren in his article “Micro-Simulation Modelling Of Domestic Tourism Travel Patterns In Sweden,” he revealed that visiting family and friends account for 43.9% of places travelers visit with great events taking second place in the group of social bond activities.

Customer feedback and suggestion: This is by far the easiest data to obtain. You simply get this data by asking your customer for their feedback. You need to know their experience about your services, community, or experiences in the local places they visited. This would form a bulk of what they’d post to social media. Ensure you get their feedback.

Customer budget: It is very important you know the average spending budget of your customer, so you adjust your cost accordingly to suit their budget while giving them the best experience. A quick keyword search in Google for “travelling for less than $1,000” reveals 42,900,000 results. This beats results for “travelling for less than $2,000” and other budgets. This little often overlooked information tells you that people are looking for the best experience while expending so little.

Challenges in obtaining data

The right team: This is the most significant barrier in obtaining data. Putting together the right team, and the team knowing which data to collect from travellers. According to The State of Data In Travel Survey 2017, 29.7% of the identified problem is finding skilled people as a challenge. Furthermore, over a quarter of teams are not sure of the data that they should collect from customers. This goes beyond finding the right team but also includes making sure they know what data to capture. It is therefore very important you communicate to your team the specific group of data that you require.

Incomplete and fragmented data: Sectors in the tour and travel industry operate independently and this causes data in each sector to be incomplete and data for the whole industry to be highly fragmented. For instance, a store owner can have information on what a customer buys from them. The airline industry has information on how much the customer flies and their destination. The airline, however, does not have any information on the purchasing habit of the customer in stores. Thus in this scenario, the store only has part of the information on the customer and the airline also has incomplete information.

Inaccurate Data: In the report, The State of Data In Travel Survey 2017, data quality and cleanliness accounted for 45.7% of challenges identified in obtaining data. It is important that you know that people sometimes do not make decisions rationally. Brian Tracy in his book, “The Psychology of Selling”, said most people buy because of the feel and satisfaction they hope to get from your product or service. This emotional and behavioural qualities of humans create a very large amount of variables. These variables could lead to wrong conclusions, getting results that trivialise important issues or give great attention to insignificant concerns. The director of forecasting and revenue strategy at NH Hotel Group, Javier Espinosa Navarro, said: “we are in an era of data pollution”. Ensure your data is as accurate and as clean as possible.

How can you get data?

Collecting email: The statistics of email is staggering. Over 74 trillion emails are sent every year. 2017 showed 1.8million more emails sent than 2016. It is estimated that the number of active emails worldwide is 5.59bllion. According to MailChimp, arguably the largest email platform revealed that open rate for the travel industry is 20.69% and click through rate is 2.17%. This is a very impressive number as Facebook, the largest social media network has an average of 0.17% across all industries. You can get email addresses on your website by using several services like MailChimp, Aweber, and much more. Another way is including this information in your customer biodata form. Ensure you warm up your customers first by giving them important and helpful information before telling them about updates in your services. With the huge volume of information shared over email, it is therefore quintessential to build your email list. Like it is popularly said, the money is in the list.

Engaging on social media: Social media boasts of millions of users daily and this platform cannot be ignored or you’d be leaving a lot of money on the table. Facebook alone boasts 2.19 billion users and Instagram 800million users. First, you need to understand that every social media network works differently from the other. For instance, Instagram is a highly image-driven and Twitter is content driven, with a maximum of 280 characters. You need to understand the dynamics of each platform. It is also very important that you post content that is engaging on any social platform you may choose to use or master. For Instagram, ensure your images are high quality.

Phone number: This can be effortlessly termed the easiest to get from your customer. You get this information by asking your customer. People give their phone number more easily. However, the challenge with this method is that people can easily misplace their phone or simply ignore your call. Even if they pick the call some may be too busy to talk at that moment.

Surveys: You can conduct surveys either online, via email, over the phone or through the filling of questionnaires.

You should include questions about your services. Most times surveys are kept confidential, however, they provide great information about customer satisfaction and can point you in the direction of what needs to be improved on.

How is data processing a great help to tour and guide industry?

The value that data processing adds to you the entrepreneur in the tour and guide industry is enormous. You need to know the areas and ways in which data processing is a great help to you the business owner or investor and the tourism industry at large.

Best cost-profit Margin: Dong Jim Kim et al, in his article, “A perceptual mapping of online travel agencies and preference attributes,” revealed that this is the most important factor amongst travellers. Furthermore, as we have already established earlier, people are constantly searching and looking for lower costs that would give them the best experience. Since data processing has established this fact, then, it is in your interest to provide the best service at the most affordable price unless you are a luxury service provider. Data processing can also reveal the best cost experience margin.

Security: Dong Jim et al revealed that security is the second most factor important on travelers’ mind. People want to be sure they are safe from danger. Danger can be in the form of diseases, crime, civil unrest or terrorism. No one wants to go to a place where they can be hurt, diseased or mugged. It is therefore important you stress how safe your business environment and community is to your customer. Data processing help you to have an interaction with your customers that your business environment is safe.

Personalized experience: Data processing gives you the ability to be able to create experiences unique to just one person. When you engage with your customers, you can understand their pain points and provide services that can solve their exclusive problems. This is one of the beauties of data analytics.

Build and strengthen relationships: This gives information on how best to keep in touch with your customer. Some people prefer to talk on the phone, some social media, and some email. You need to know each person’s preference. Contacting them on their most preferred way reduces resistance and makes them more responsive and open to a relationship with you. Do not forget to get in touch with travelers, keep the relationship with them alive. When they visit your state, you’d be very sure that they’d come around.

Improve product/services: Data processing gives you great insight into which feature of your service is lacking. From your customer feedback, you can understand which feature of your product or service benefits them the most and you can improve on that to make it even better.

Loyalty: Having understood the right way to reach your customer. You’d understand the right way to approach them. You know and understand what makes them tick. With this knowledge, you can solve their problem well and they’d keep coming back to you.

Improve customer experience: The first thing in improving your customer experience is having a customer experience target or goal. You should always try to anticipate problems travelers may face and put solutions in place should they actually arise. You cannot have this information without data processing.

Increase profit: Every business owner wants to save cost and increase profit, however, only those who understand data processing succeed well with this. From the results you obtain from your data, you know which of your services bring in the most revenue. You’d also know which services are not profitable and you stop them and save yourself some money.

Innovate: Innovating puts you ahead of the competition. Your data would reveal the systems that travelers are having challenges with and you can improve on them. The can help the tour and guide industry create seamless service to travelers.

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loginworksoftware-blog · 7 years ago

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Data Analytics or Data Science – Which is More Affordable for Startups?

Startups tend to collect large amounts of data to pave the path of their progress at a faster speed but have limited resources to store data. All they desire is predictive analysis because they want to track the behavior of potential customers for maybe a year or two.

WHAT ROLES DO DATA SCIENTISTS AND DATA ANALYSTS PLAY?

To answer the question of which is a more affordable option for startups, a Data Scientist or a Data Analyst, let us look at the job profile and scope of both these professionals:

The Data Science process involves

:Step #1: Answering queries

The Scientist’s machine learning model has to answer a question or solve a problem. Of course, there will be many permutations and combinations of data sets to deal with different queries. So the data model must be a comprehensive set of parameters to deal with all eventualities.

Step #2: Collecting data

Web scrapping or collection of real-time Big Data is the next step that a Data Scientist will undertake. Sample streamed data will be collected initially to test and retest the data model.

Step #3: Reviewing the Data

Even the best data models can collect irrelevant data. At times web users enter wrong information either because of typo errors or intentional falsification. This data is collected along with the rest of the information. Reviewing the data for relevancy and accuracy is the next step that a Data Scientist has to perform.

Step #4: Cleaning the data

This stage involves:

Co-relating different data sets from multiple sources for logical processes.

Checking for redundancies or unusual patterns so that, as a Scientist, you can add parameters to deal with these situations.

Evaluating the relevance of the data to the client’s needs.

Deciding whether the data collected is of any use or fresh data has to be collected for testing your machine learning model.

Step #5. Testing the Data

Storing the information so that it can be used for retesting and reporting is the next stage in the Data Science process. The common tools used by Data Scientists are R, SQL, and Python. The stored data is used in subsets for pre-processing. So you have to formulate scripts that will automatically correct the anomalies and reformat the data into logical, quantifiable data sets. This involves:

Building the data model to answer specific queries.

Cross-validating the data.

Using regression analysis to test the data.

Comparing the efficacy of your algorithm against other logical techniques.

Finalizing your model once it shows a high level of efficiency in producing the desired results.

The Data Scientist has to also consider issues like logistics, the privacy of data, and accessibility protocols while finalizing the data model. Once your data model is honed to perfection and all the parameters are in place, it is time to test it against real-time live data.

Step #6: Risk assessment

Every production unit or service industry has several key players whose hand is involved in the finished product. Suppliers of raw material, labor, warehouses, distribution systems, marketing and sales, courier services, wholesalers, retailers, and many other factors are involved in the supply chain. Assessing the risk and checking the credibility of all the external players is also a very important role that a Data Scientist plays. In fact, this is one of the most crucial roles of a Scientist. Without risk assessment, your client will not know if any of the partners have compliance issues.

The role of the Data Analyst

If Data Science is the toolbox then Data Analysis is the set of tools inside the box. The typical tasks that a Data Analyst performs are:

More focused data analysis to answer specific queries and needs of a particular.

Unlike a Data Scientist who will repopulate the databank for retesting the model, the Data Analyst will sort through the existing information to search for the data sets that would fit the desired parameters. Which means that the model is designed with a very specific query in mind and the data collected has to be relevant to that query. So the scope of mining and testing is limited compared to Data Science.

The Data Analysis process involves sorting through existing data like past experiences, current trends, desired markets that the client wants to tap, etc. The aim is solely to track customer behavior, their preferences, seasonal ups and downs in demand, etc. in order to implement short-term marketing strategies. The tools usually used by Data Analysts are R, Excel, Python, and Tableau.

So Data Science involves a number of specialists who work as a team. They use a mix-and-match of data models and techniques to get the desired information, including the tracking of customer online payment activities. Data Science uses statistical formulae to access, process, and manipulate data so that the Analyst can query it for client-specific analysis and reporting.

Based on the skillset, a Data Scientist can be a Data Researcher, Data Developers, Creative Developer, Data Businessperson, or Data Scientist. A Data Analyst can take on roles like Database Administrator, Data Architect, Operations, or Analytics Engineer.

So when you look at the Data Scientist’s scope of work you can guess that it is a more specialized field and requires a deeper knowledge of Business Intelligence techniques and programming. Data Scientists in most cases work in an agency that offers specialist services to business organizations.

Whereas, there are many companies today who employ an in-house Data Analyst to help them to globalize their market and create brand value. Since the Analysts role is limited, the remuneration expected is also lower than what a company might have to pay a Scientist. Also, there are many freelance Data Analysts who offer their expertise for affordable fees on a project basis. Startups, with their limited resources, will usually prefer to employ the services of a Data Analyst because it is a more affordable choice and because they have short-term goals that need to be met quickly.

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