Introduction

Big data has changed the way companies operate, giving new and improved meaning to digital business transformation. Companies gather data through online searches, analyses of consumer buying behavior, and other techniques, and use this data to make profit and to provide an overall better experience to customers. While big data is being used by many companies across the globe, the travel industry stands to gain a tremendous amount from its use.

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The travel industry includes a wide range of business such as rental car companies, hotels, airlines, tour operators, cruise lines, etc. Each of these companies makes use of big data to improve overall customer experience and to meet the needs of users. Through the use of big data analytics, travel companies are able to target individuals for their unique needs, tailoring special promotions, deals, and experiences.

For example, a resort in Las Vegas may determine that its customer base is largely comprised of youngsters, so it may host a concert with a famous pop star to attract more visitors and to increase its profit, while at the same time, giving customers a better experience.

The travel industry may also use big data to improve operations in numerous ways. Some data analysis may reveal that one aspect of marketing is ineffective, so a company can improve their marketing strategy. Another company can learn that customers are choosing the competition more heavily because of better price, promotions, or a perception of better quality.

Big data can also provide real-time travel assistance to companies by providing insights about the current locations of customers, via mobile devices, as they travel around the world. For example, if a travel app verifies that your smartphone is situated by a well-known amusement stop, eatery, or other key location, it might highlight special offers or arrangements that you can use to get a good deal on a visit to these popular spots. Similarly, customers may also utilize supportive travel tips or connections, which may be more accommodating in some cases.

Application of Big Data Analytics

  • Personalized Customer Experience — Big data analytics help make travel more responsive and focused on the traveler’s needs and preferences.
  • Superior Pricing Strategy — Big data analytics adequately supplanting conventional manual fare analysis with smart robotization by gathering, ordering, sifting, and breaking down existing and real-time data from different sources. Dynamic analysis of competitors pricing will help travel companies create a better pricing technique for travel. Big data analytics permits travel sites to conjecture price change over time, for better serving their customer needs.
  • Customer Analytics & Service Augmentation — Observing customer purchasing trends, protests, and inputs by breaking down information collected from online forums, social media platforms, front desk, call center conversations, etc. will distinguish client purpose and help with planning business techniques.
  • Marketing & Sales Optimization — Big data analytics is increasingly being utilized to optimize marketing efforts on targeted travelers by modifying offers in light of traveler necessities. By analyzing a vast quantity of unstructured data, service providers will gain valuable insights that will empower them to deliver targeted offers at the right time, place, and through the right channel. Service providers can also track their clients and make location-relevant, real-time offers by enabling GPS technology with data analytics.

Challenges

  • Data Aggregation

One of the most significant obstacles of using big data in the travel industry is capturing data. A huge amount of data is generated daily, and the challenge lies in converting this data to derive customer value. There are numerous reasons why a data set can be deficient or erroneous, however. Customer data is divided over numerous, diverse frameworks, which can either be exclusive or off-the-rack. Many companies do not have a common storehouse of data on their customers.

  • Data Quality and Cleanliness

The travel industry largely revolves around human behavioral analysis – a complex field where the number of variables is extremely high, Therefore, there is huge potential for big data management programs and analytics hubs in light of the fact that currently, many simply draw the wrong deductions and overemphasize connections that may have little or no bearing.

  • Rising Customer Expectations

Travelers expect personalized experiences to reflect their likes and dislikes, but the insights generated from the collected data are limited.

Bringing all this together into one reliable data warehouse requires a great deal of investment, especially for bigger companies, which rely upon legacy technologies for customer loyalty data, complaint logs, and transport tasks.

Conclusion

Predictive analytics through big data is primarily reforming all aspects of the travel industry, and companies that do not adapt will be lagging in the data-led industry. Big data analytics is enhancing customer experience, expanding business productivity, and revenue management in travel industry. Mastech InfoTrellis – a leading Data & Analytics company – is helping companies rise to the forefront of this change. Wouldn’t you like to?

 

Technical Consultant Aradhana

Aradhana Pandey
Technical Consultant, Mastech InfoTrellis



Descriptive analyses or statistics do precisely what the phrase infers; they “describe”. Descriptive analysis summarizes raw data and makes that data easily deciphered. It describes the past, where the past refers to any point of time when an event has occurred, whether it was one minute ago, or one year ago. The technique uses data aggregation and data mining to provide insight into the past and answer the question, “What has happened?” This, in turn, helps us understand how the past might influence future outcomes.

Descriptive statistics are valuable to describe items like total stock in inventory, average dollars spent per customer, and year-over-year change in sales. Common instances of descriptive analytics are reports that provide chronicled bits of insights in to a company’s production, financials, operations, sales, finance, inventory, and clients. Descriptive analytics can be utilized when we have to comprehend, at an aggregate level, what is happening in the organization, and when we want to outline and portray different aspects of the business.

When Should You Use Descriptive Analytics?

Descriptive analysis is an appropriate way to understand attributes of particular data. Deeper analysis provides the following:

  • It estimates and outlines the data by organizing it in tables and graphs to help meet targets
  • It provides information about the fluctuation or vulnerability of the data
  • It provides indications of unexpected patterns and perceptions that should be considered when doing formal analysis

Basic Principles of Descriptive Analytics

Data given by descriptive analytics end up as prepared inputs for further developed predictive or prescriptive analytics that deliver real-time insights for business decision making. Descriptive analytics seldom endeavors to explore or set up circumstances and connections to end-results. Some of the common methods employed in descriptive analytics are observations, case studies, and surveys. Accumulation and translation of a substantial amount of data is involved in this type of analytics, with most statistical calculations generally being applied to descriptive analytics.

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Descriptive Analytics Illustrations

Here are some common applications of descriptive analytics:

  • Summarizing past events such as territorial customer attrition, sales, or success of marketing campaigns.
  • Tabulating of social media metrics such as Facebook preferences, tweets, or followers.
  • In an analytics study conducted by McKinsey in 2016, the US retail (40%) industry and GPS-based services (60%) showed rapid adoption of descriptive analytics to track teams, customers, and assets across locations to capture enhanced insights for operational efficiency. McKinsey also claimed that in today’s business climate, the three most critical barriers to data analytics are lack of organizational strategy, lack of involved management, and lack of available talent. Another report suggests that descriptive analytics has made great strides in Supply Chain Mapping (SCM), manufacturing plant sensors, and GPS vehicle tracking, to gather, organize, and view past events.
  • Investors and brokers perform analytical and empirical analysis on their investments, which helps them in settling on better investment decisions in the future.
  • Descriptive analysis can also be called post-mortem analysis. It is utilized for almost all administration reporting, such as marketing, sales, finance, and operations. To gain the competitive edge, organizations utilize advanced analytics, which likewise underpins them in estimating future trends. The forecasting allows companies to make optimized decisions, thus increasing their profitability.

Descriptive analytics can be utilized in future analysis as data-driven organizations keep on using the outcomes from descriptive analytics to optimize their supply chains and improve their decision-making power. Data analytics will now, however, move further away from predictive analytics toward prescriptive analytics, or rather, towards a “blend of forecasts, simulations, and optimization.”

Mastech InfoTrellis – a pioneering data and analytics company – is a leader in the implementation and use of advanced analytics applications across a wide array of industries. Get in touch today to learn more about how you can gain actionable insights through an analytics hub, and thus, improve your organization’s decision-making capabilities.

 

Technical Consultant Aradhana
Aradhana Pandey

Technical Consultant