How Data Analytics Helps In Business Growth

Introduction

In this fast-growing world, there are a lot of data coming from large-scale enterprises, small-scale enterprises, and individuals so there is a huge need to analyze this data. Well, there is where the ‘Data Analytics’ term comes into the picture. Data analysis is the process to examine the data and extracting useful information to help organizations. It is significant because with the help of data analytics organizations can find new opportunities and proper insights to run their business. In this article, I will provide an analysis of the different types of analytics and tools used in different organizations.




Types of Analytics


Sports Analytics

Sports analytics refers to the use of analytics and statistical techniques in sports to get the proper insight into the game. With the help of sports analytics, the team and individual can make better decisions. It is very useful to enhance team performance and the organization’s business performance. It is also used to analyze the health and injury of the players. Sports analytics is widely used in different sports organizations. According to some research, its market will reach $4.7 billion by the end of 2021. (McCafferty, 2015)


Mobile analytics

The mobile phone has surpassed the desktop computer in 2015 and mobile phones are the most used platform for the internet nowadays. There is a lot of data-generating by the use of mobile phones so, to get the proper insight, Mobile analytics comes in. Mobile analytics refers to understanding the needs by analyzing the behaviour of the user. In mobile analytics, it captures the data by recording the behaviour of the user, tracking the journey, and getting a record of the performance of the app. It is quite similar to web analytics and is used to improve conversions. There are three different uses of mobile analytics performance analytics, marketing analytics, and in-app analytics which companies and organizations use to get insight into their app efficiency and mobile users respectively.


Web Analytics

Web analytics is the process of analyzing the behaviour of website visitors. It can also be used as a tool for businesses and market research. The steps web analytics contains are data collection, data processing, KPI development, formulating strategies (online), experiment, and testing. There are two kinds of web analytics off-site web analytics and on-site web analytics. Web analytics also helps to form better user experiences for website visitors. For example, due to web analytics, websites will show you the most rated products and show you the most appropriate path to buy them. The most popular tool for web analytics is google analytics although there are many other tools as well.


Visual analytics

It is the science of analytical reasoning facilitated by the visual interface. (Thomas, 2005) Visual analytics is a very unique kind of analytics because it involves statistical work and data mining. For example, the visualization that is done by the human hand can be defined as information visualization, on the other hand, an interface that displays the result of the algorithm would be described as visual analytics. Visual analytics interface helps humans to understand the result of analysis better.


Agile analytics

Agile analytics is a term used to describe the model for exploring the data which has the main focus on finding insights into the dataset rather than by hypothesis. It is built on the idea of flexible and easy analysis. The major application of this agile analytics is in the financial industry. With the help of agile analytics, we can perform a more open analysis of data which shows different activities can be connected, even if it is not obvious at first. When we pair agile analytics with the data visualization tool it becomes a very excellent tool for the investigators.





Sports analytics: Business growth


Organizations using sports analytics

In today’s world, industries are using a data-driven approach for business success. However, In the sports industry, professionals are using this kind of approach as well to analyze the performance of the players.


Analytics in sports has been around since 1970 but nowadays it gathered uninterrupted attention worldwide. Sports analytics is not just tracking the data on paper but, its scope is very wide. All the major professional sports have analytics experts on their teams to analyze all the aspects affecting player performance. For example, the National Football League (NFL) is using sports analytics to track the performance of their players by placing a sensor in each player’s shoulder pads. They understand the importance of sports analytics and are performing well for the last few seasons. They shared, the information generated by the pads, with all the clubs to get the proper understanding of the strength and weaknesses of their players and competitors as well as design the strategies of the game accordingly.

IBM Wimbledon is also tracking the performance of its tennis players in the game for years. According to some executives of the IBM Wimbledon, they are using a staff of 150 people and gathered 3.2 million data points throughout the championship. Their data analytics team has 48 people among them some are tennis players and most of them are courtside. They placed the sensors on players' bodies to analyze the speed, server location, and rally count. The information generated by these sensors is not only for the benefit of the players but they also made it public.


In basketball also professionals used 3D trackers to analyze every shot, shooting accuracy, and different patterns. By analyzing these attributes, coaches can understand the position, accuracy, and performance better.





Sports Analytics is not only used in ball sports. It is also used by the Ducati for its motorbikes. Data analysts placed different kinds of sensors on their motorbikes to analyze what changes should be made to increase the performance of the bike during the race. Professionals also used different kinds of racing scenarios in which they analyze the track and weather conditions. They also used machine learning techniques so that they can inform the motorbike engineers to make changes accordingly to achieve faster lap time.


Formula 1 (F1) is also using sports analytics for analyzing car performance. Their car is an intelligent data system that contains many sensors that collect the data like lap time, g-force, break and tire temperature, driver biometrics, and engine performance. This generated data helps the professionals to make better decisions and performance of the car. They also used different kinds of sensors to analyze the heart rate variability, recovery time, breathing rate, and temperature of the body so that they can modify the fitness training for the drivers because in a race like this every second counts. The huge amount of data collected is also used in the simulation of the car system. By using sports analytics professionals can analyze different attributes that will help them to make better decisions for the betterment of the performance of the drivers and their automotive engineers. If we talk about winning a race driver is not the only factor responsible for it. Car, weather, and track also play a very important role which is why many Formula 1 racers and companies are using data analytics and sports analytics.





How sports analytics drive business growth

If you are in the sports management team then it must be very important for you to understand where the team or organization is generating revenue. When you understand data properly then only you can truly analyze what drives business growth or revenue. Three ways can drive business growth in sports industries. The ways are the following:


Understanding trends in ticket sales

By using the different data visualization tools and different statistical techniques professionals can understand the trends in ticket sales whether it is improved or not. For example, how many third-party websites sold the tickets, which website generated the most sales, which team brings more fans, and how many fans purchased mobile tickets. By analyzing these attributes professionals can understand what drives ticket sales to grow better and this will help the organizations to grow well in business.


Give fans what they want

With the help of data visualization, organizations can understand which promotions are working and how fans are behaving and responding. So, the team has to organize its promotions and coupons distribution accordingly based on the people’s responses.


Concessions

Many organizations are collecting data from the concession factors like digital menu boards, heat map technology that helps to understand where to cut wait time, and mobile phones for ordering in seats. This kind of data helps professionals to understand if their prices are in line with the demand of the fans or not.


Moneyball: the art of winning an unfair game

This is the book by Michael Lewis (Lewis, 2003), published in the year 2003. It is based on the application of sports analytics into sports with various examples provided. This book is about the Oakland Athletics, one of the poorest teams in US baseball teams. In this book, it is mentioned that the Oakland Athletics team won 20 consecutive games with the help of an analytical and sabermetric approach despite having a low budget. It tells us about the life of Billy Beane from his college day till he became the manager of the Oakland A’s team. Billy Beane analyzed the existing culture and found out that it is subjective and based on gut feelings. He then tried to make metrics that reflect the total capacity of the players. He relies on the analytical and sabermetric approaches. His approach was system-dependent rather than people-dependent. This book is the perfect example of sports analytics and how sports analytics can help teams and organizations to grow exponentially in their respective sports.





Use of Data Analytics in different organizations

Organizations have invested a lot of money in data analytics. Organizations are trying to convert themselves from knowing the business to learning business. They are spending lots of resources just to get the proper insight and new opportunities. There are many uses of data analytics in organizations. Some of them are customer acquisition and retention, advertising and marketing, product development, and risk management (Kopanakis, 2018).


Customer Acquisition and Retention

Customers are the most important asset for any given business. Organizations cannot grow if it does not have a large or solid customer base. If the organization is not adaptable to the customer demands then it will be more critical for the organization to grow and unknowingly organizations can produce poor quality products. In the end, this will affect business success. So, there is a huge need that organizations should work on their customers. With the use of Data, analytics organizations can understand the hidden trends and patterns of their customers. Observing customer behaviour is very important for loyalty. The more data organizations collect there is a huge chance that professionals will be able to identify the hidden patterns. In this era, it is very easy for organizations to collect all the customer data they need. With the proper data analytics techniques and tools, organizations will have the capability to gain critical behavioural insights so that they can understand the customer better and have the ability to retain the customer. The biggest and real example of customer acquisition and retention is the Coca-Cola company. In 2015, Coca-Cola builds a digital-led loyalty program that helped them to strengthen their data strategy and customer retention. According to Coca-Cola in an interview, said it is very important to know our customer’s opinions via email, social media, and phones because they can analyze them in a better way. Data is also helping them to create content according to a different audience.


Advertising and Marketing

Data analytics can help businesses to change all of their operations. This includes customer expectations, and product and marketing campaigns. Organizations have lost millions of dollars on irrelevant advertising and marketing. Now, many organizations embraced data analytics for their advertisements and marketing campaigns and organizations can do more sophisticated analyses. The sophisticated analysis involves analyzing the online activity, monitoring the transactions, finding patterns and trends of customers, and analysis of customer data. This is done with the help of data analytics. So, this will result in more targeted and focused campaigns. By these means, organizations can save a lot of money and produce campaigns with better efficiency. Data analytics is good for advertisers and marketers since the organization can understand customer purchasing behaviour better. The perfect example of the use of data analytics for advertising and marketing is Netflix. Netflix is using data analytics to get insight into its subscribers. If you ever used Netflix you can understand how Netflix suggests the content to watch. It is based on past searches and watches data. This data helps Netflix to understand which show or movie interests the subscriber.


Product development

Data analytics can provide a huge advantage in developing and innovating the organization’s products. Data analytics can create additional revenue through product development and innovations. Organizations are gathering as much data as possible and applying analytics for designing new products and existing products. Now, the main aim of the organizations is to design a product that fits the customer's needs. Organizations need to collect huge data to improve the quality of products and their manufacturing performances. After collecting the data, analyzing it would be the next step. Decisions based on gut feelings are not reliable nowadays. That means the organization should come up with means for tracking its products, feedback from the customers, and competitors. The most accurate example of product development using data analytics is Amazon's fresh and whole foods. Data analytics enables amazon to create the product with greater value. By the use of Data Analytics, Amazon tried to understand how the customer buys products, and how suppliers interact with the grocers. This kind of data enables Amazon to make changes whenever necessary.


Risk management

For any given organization risk management can be the most important factor for business growth. To foresee potential risk and mitigate it, is very critical and it can help the organizations to be more profitable. Data Analytics helped a variety of organizations to cope with potential risks and develop a proper risk management solution. Different data analytics tools allow organizations to quantify and model risks that they face. Due to the high availability of data analytics tools, organizations can enhance the quality of their risk management models. For example, UOB bank from Singapore uses data analytics to mitigate its risks. It is a financial institution so it includes a lot of risks. Recently, the UOB bank tested a data analytics risk management model to identify and mitigate the potential risks. Before it took 18 hours to identify risks but with the help of data, analytics organizations can identify them in just a few minutes and with proper accuracy.




 


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References

Davenport, T. H. (03, june 2014). What Businesses Can Learn From Sports Analytics. Retrieved from sloanreview.mit.edu: https://sloanreview.mit.edu/article/what-businesses-can-learn-from-sports-analytics/


Kopanakis, J. (2018, june 14). 5 real world examples of how brands are using big data analytics . Retrieved from mentionlytics: https://www.mentionlytics.com/blog/5-real-world-examples-of-how-brands-are-using-big-data-analytics/


Lewis, M. (2003). Moneyball: the art of wining an unfair game . New York: W.W. norton.

McCafferty, D. (2015, 08 14). 9 Fascinating Facts About Sports Analytics. Retrieved from base line mag : http://www.baselinemag.com/analytics-big-data/slideshows/9-fascinating-facts-about-sports-analytics.html




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