Big data analysis is a trending practice that many companies are implementing. However, organizations must first become familiar with the landscape before they can jump in and buy tools that handle big data.
The analysis process, including the deployment and use of big data analytics tools, can help companies improve operational efficiency, generate new revenue, and competitive advantage over their competitors.
However, there are different types of analytical applications that need to be considered. For example, descriptive analytics focuses on describing what has already happened and suggesting its root causes.
Descriptive analytics typically rely on a basic query, which remains the lion’s share of the analysis performed.
The use of big data is critical for many leading companies to outperform their competition. In many segments, new entrants and established competitors use data-based strategies to compete, capture and innovate. You can find examples of big data being used in virtually every sector, from IT to healthcare.
Photo by Franki Chamaki
What Is Big Data, And How Can It Help To Leverage Your Business?
Simply put, “big data” is a combination of all the processes and tools involved in using and managing large data lists.
The big data concept was born since the necessity to understand trends, preferences, and patterns in a vast database created when people interact with different systems and with each other.
Business organizations can use analytics and calculate their most valuable customers with big data. It can also help companies create new experiences, services, and products.
You can use big data to outperform your competition by collecting and innovating with big data. That’s why today, you’ll find examples of its use in virtually every industry.
Companies use it to identify gaps in their services and products, suppliers and customers, and consumer intentions and preferences to create newer, better ones. To be honest, it has created many new growth opportunities since companies began using it in 1997.
Among other advantages, big data is often obtained in real-time. It is very important because they can measure indicators, including customer loyalty – something that in the past was processed independently of each other.
Now they are not only processed sequentially but also more widely, which makes them even more compelling factors for your business as you can now test your theories in real-time.
Here are just a few examples of how big data can help businesses in both the public and private sectors.
1. Using Big Data Analytics to Boost Customer Acquisition and Retention
A client is the most valuable asset for any business. No business can claim to be successful without first establishing a strong customer base. However, even with a client base, the company cannot afford to ignore the high competition.
If the business slowly finds out what customers are looking for, it is effortless to start offering cheaper quality products. In the end, it will lead to the loss of customers, which in general will harm the success of the business.
The use of big data permits the business to observe different models and trends related to customers. Watching customer behavior is essential to stimulate customer loyalty. In theory, the more data a company collects, the more models and patterns it can identify.
In today’s business world and the era of modern technology, a business can easily collect all the data it needs about its customers. This means that it is straightforward to understand the contemporary client. In principle, all that is required is to have a big data analysis strategy. It will maximize the data at your disposal.
With a proper client data analysis mechanism in place, the business will be able to draw critical behavioral conclusions that it needs to consider to maintain its client base.
Understanding customer needs will enable your business to get what customers want from you. This is the most fundamental step to achieving a high level of client retention.
Coca-Cola is a real example of a company that uses big data analytics to retain clients. In 2015, Coca-Cola was able to strengthen its data strategy by building a digitally managed loyalty program.
Coca-Cola’s Data Strategy Director interviewed the Managing Editor of ADMA. It was clear from the interview that big data analytics has contributed significantly to customer retention at Coca-Cola.
Below is an abstract overview of what Coca-Cola said about the role of big data in achieving client retention.
Data is taking an increasingly important position in marketing and product development. Consumers are doing a great job of sharing their opinions with us – by phone, email, or social media – allowing us to hear their voices and adjust our approach.
It is often discussed why we have two ears and one mouth – it’s better to listen more than to talk. This also applies to our approach to consumer contributions.
The data also helps us create more relevant content for target customers. We want to focus on creating advertising content that speaks differently to diverse audiences.
Some people love music. Others watch every sport regardless of the season. Our brands are already visible in these areas, and we are working hard to use data to create branded content that matches people’s passions.
2. Use of Big Data Analytics to Solve Advertisers Problem and Offer Marketing Insights
Big data analytics can help evolve all business operations. This includes being able to meet customer expectations, changing a company’s product line, and providing powerful marketing campaigns.
Let’s face it. Businesses have lost millions of dollars to launch ads that have not borne fruit. Why is this happening? There’s a good chance they missed the research phase.
After years of cautious enthusiasm, the marketing and advertising technology sectors are now able to take big data (Medal, 2017) and can do more sophisticated analysis. This includes monitoring online activity, monitoring point-of-sale transactions, and tracking on the fly dynamic changes in customer trends.
A more targeted and individualized campaign means that businesses can save money and ensure efficiency. This is because they target high potential customers with the right products.
Big data analytics is good for advertisers, as companies can use this data to understand customer buying behavior. We cannot ignore the enormous problem of advertising fraud. With predictive analytics, organizations can identify their target customers.
Companies can have proper and effective coverage, avoiding the enormous losses incurred as a result of advertising fraud.
Netflix is an excellent example of a large brand that uses big data analytics for targeted advertising. With over 100 million subscribers, the company collects big data, which is key to achieving Netflix’s industry growth status.
If you are a subscriber, you are familiar with how they send you suggestions for the next movie you should watch. This is mostly done using your past search and view data. This data is used to give them an idea of what the subscriber is most interested in.
Photo by imgix
3. Big Data Analytics for Risk Management
Unprecedented times and a high-risk business environment require improved risk management processes. A risk management plan is an essential investment for any business regardless of its industry.
The ability to anticipate potential risks and reduce it before it occurs is critical if the business is to remain profitable. Business consultants will advise that enterprise risk management involves much more than ensuring that your business is adequately insured.
So far, the analysis of large volumes of data has contributed significantly to the development of risk management solutions. The tools available allow businesses to quantify and simulate the risks they face every day.
Given the increasing availability and diversity of statistics, analysis of large volumes of data has great potential to improve the quality of risk management models. In this way, businesses can achieve smarter risk mitigation strategies and make decisions accordingly.
However, organizations should be able to implement a structured evolutionary approach that takes into account the full range of big data. To achieve this, organizations should first collect internal data to gain a clear understanding of what will benefit them.
More important is the integrated analysis process that the company uses. A proper system for analyzing large volumes of data helps identify weaknesses or potential risks.
UOB Bank, in Singapore, is an example of a brand that uses big data to manage risks. As a financial institution, there is an enormous potential for loss if risk management is not adequately thought out. UOB Bank has recently tested a risk management system based on big data.
The risk management system based on big data allows the bank to reduce the time it takes to calculate the value at risk. Initially, it took about 18 hours.
However, with a risk management system using big data, it took only a few minutes.
Thanks to this initiative, the bank may be able to carry out risk analysis in real-time quickly.
4. Big Data Analytics As a Driver of Innovations and Product Development
Another massive advantage of big data is the ability to help companies innovate and reimplement their products.
Big data has become a way to create additional revenue streams through innovation and product improvement. Organizations start by adjusting as much data as possible before developing new product lines and redesigning existing products.
Each design process must begin with determining what is right for the customer. There are various channels through which an organization can explore their customer needs. Then the business can determine the best approach to capitalize on this need based on the analysis of big data.
“Gone are the days when you can go your way” (Rampton, 2017). To improve quality and optimize performance, you need to collect vast amounts of data. Going with your gut feeling virtually ceases to be reliable if an organization wants to compete in the 21st century. This means that these organizations must come up with tools to track their products, competitors, and customer feedback.
Once the data is used, analytics is performed to ensure that logical thinking is applied before a plan of action is developed. Fortunately, manufacturers of any size have a unique advantage when it comes to collecting and using big data. This means that these organizations can quickly improve their product line by producing innovative products.
You’ve probably heard of Amazon Fresh and Whole Foods. This is an excellent example of how big data can help improve innovation and product development. Amazon uses big data analytics to enter a big market. Data-driven analytics give Amazon the expertise it needs to create and achieve higher value.
By focusing on big data analytics, Amazon can understand how customers buy food and how suppliers interact with the grocer. This data provides insight into when further changes are needed.
5. Use of Big Data in Supply Chain Management
Big data offers networks of suppliers greater accuracy, clarity, and insight. By using big data analytics, suppliers get context information across their supply chains.
Basically, by analyzing big data, suppliers can avoid the constraints they’ve previously faced. This is made possible by using traditional enterprise management systems and supply chain management systems.
These legacy applications didn’t use big data analytics, so suppliers suffered huge losses and were prone to error. However, with today’s data-intensive approaches, suppliers can use the higher level of contextual analytics required for the success of their supply chain.
Today’s data-intensive supply chain systems allow for more complex supplier networks. They rely on high-level knowledge sharing and collaboration to obtain contextual information.
It is also important to note that supply chain executives see big data analytics as a disruptive technology. This is based on the idea that it will lay the foundation for change management in organizations.
PepsiCo is a consumer goods company that relies on vast amounts of data to effectively manage its supply chain. The company wants them to add volumes and types of goods to retailers’ shelves.
The company’s customers provide the company with reports that include inventories and POS materials, and this data is used to verify and predict production and shipping requirements.
In this way, the company provides retailers with the right products, at the right volumes, and at the right time.
Key Takeaways
Any business can benefit from using big data analytics. There is a quote that says, “Without data, you’re just another person with an opinion.”
Whatever business decision you take, it is better to have data to support it. Even a small amount of data is better than no data at all. The kind of data you need depends on your particular business and business objectives.
This may include sales data, accounting data, marketing data, operational data, personal performance data, etc.
A lot of analytical data is an essential investment for a growing business. By implementing big data analytics, companies can achieve a competitive advantage, reduce operating costs, and retain customers.
There are various sources of customer data that enterprises can use. As technology advances, data becomes available to all organizations.
From a technical point of view, it is fair enough to say that organizations already have data at their disposal. Individual organizations need to ensure that they have appropriate data analysis systems in place that can handle large amounts of data.
Does your business have a system for analyzing big data? Learn from the above examples of successful brands and implement it today.
__
Author’s bio: Dmitrii B. is the founder of GRIN tech – full service agency.
