5 Real World Examples of How Brands are Using Big Data Analytics

Big data analytics involves the analysis of huge quantities of data. This is done in order to find patterns and correlations that are hidden and as well to provide insights that allow for the making of good business decisions. In essence, companies have recognized that they must evolve from being a well-informed company to one that is learning. In essence, companies are seeking to be more transparent and data-driven and therefore, they are taking advantage of the capabilities of data and technology.

The idea of big data has been in use for many years. Before that first reference to big data companies utilized analysis to the data they gathered to gain insight and discover patterns. It was as simple as capturing the numbers on a spreadsheet and then manually looking over the data.

Big data analytics are carried out with the help of advanced software systems. This helps businesses reduce the time required for analytics to speed up decision-making. In general, the latest big data analytics platforms enable fast and efficient processes for analysis. This capability to work more quickly and to be more agile provides a companies with an edge in competition. While doing so, businesses benefit from lower costs when using big analytics software.

Businesses have made investments into big data analysis. Imagine a company you've worked with that requires swift and quick decisions to stay in the market. This article will provide five examples from the real world of big companies making use of large-scale data analysis. Read on to get more insight.

#1: Using Big Data Analytics to Boost Customer Retention and Acquisition

Customers are the primary asset that any business can rely on. There is no one business which can boast of success without having an established base of customers. But, even having a loyal customer base, businesses cannot afford to ignore the fierce competition it is facing. If a company isn't able to comprehend what consumers are looking for It is simple to start offering low high-quality products. In the end, a loss of customers will occur which has an unintended effect on the success of the business.

Big data can allow businesses to identify diverse patterns of customer interaction and patterns and. It is essential to observe customer behavior to build loyalty. The more information that a company collects, the greater patterns, trends and patterns that the company is able to detect. In today's business environment and the age of technology it is possible for businesses to obtain all the information about customers it requires . It is extremely easy to comprehend the current customer. The only thing required is a large data analytics strategy that can maximize the amount of data available. With a well-designed customer data analytics process in place, businesses will be able to draw crucial behavioural insights should be utilized in order to keep the customers.

Knowing the insights of your customers can help your business provide what your customers expect from you. This is the first way to achieve high retention of customers.

An example of a business that makes use of Big Data for Customer Acquisition and Retention

An excellent instance of a business which uses large-scale data analysis to improve loyalty of its customers can be found in Coca-Cola . In in 2015 Coca-Cola was able to improve its strategy for data by establishing an online-based loyalty program. Coca-Cola Director of Data Strategy had a conversation with ADMA executive editor. The interview proved the importance of big data in determining what are key to retention of customers at Coca-Cola. Below is an excerpt of the complete interview about what Coca-Cola has to say regarding the role played by big data in the process of achieving customer retention.

How important is the role of data contribute to Coca-Cola staying relevant and connected to its customers in this digital age? What is the relationship between data and the development of products?

Data plays an increasing part in the process of marketing and product development. Consumers do an excellent job of sharing their thoughts to us via email, phone or social media platforms which allow us to hear their voices and adapt our strategy. We often discuss the reason there are two ears as well as a mouth, but it's best for us to pay attention more often than communicate. This is also true for our approach to incorporating input from consumers. Data also helps us develop more relevant content for various groups. We are focusing on creating content for advertising that is different for different groups. Some people love music. Others are fans of every sport regardless of the time of the year it is. Our brands are visible in these spaces We're working using data to deliver brand-name content that is in line with people's interests.

#2 The Use the Big Data Analytics approach to solve Advertising Problems and Provide Marketing insights

Big data analytics could help improve the efficiency of all business operations. This is a good example of the ability to adapt to expectations of the customer, the changing products, and, of course, ensuring that marketing campaigns are successful. Let's be honest here. Companies have lost millions in advertising campaigns that don't yield results. What is the reason for this? It is likely that they did not complete this step of research.

In the past, after years of timid optimism the marketing and advertising technology sector is now able take on big data in a massive way (Medal 2017). The advertising and marketing sector is now able to conduct more thorough analysis. This includes observing activities on the internet, tracking the transactions at the point of sale and immediate detection of shifts in the customer's trends. Understanding the behavior of customers involves collecting and analyzing information of the customer. This is accomplished by using the same method used by advertisers and marketers as shown. This enables marketers and advertisers to produce targeted and focused campaigns.

A targeted and personalised campaign can help businesses reduce costs and increase the effectiveness of their campaigns. This is because they can target customers with high-potential clients by offering the most appropriate products. Big data analytics can be beneficial for companies as they are able to use the data to better understand the buying habits of customers. It is important to address the massive fraud issue that ad fraud causes. By using predictive analytics, it's possible for companies to determine their ideal customers. Thus, companies can achieve an effective and appropriate reach to avoid the massive loss that is incurred because from Ad fraud.

A Brand example that utilizes Big Data for Targeted Adverts

Netflix provides an excellent illustration of a large company that makes use of big data analytics to target advertising. With over 100 million users they collect massive amounts of data that are crucial to achieve the status of an industry Netflix increases. If you're a subscriber, then you are used with how they provide you recommendations for the next film you need to take in. It is essentially with your search history and watching data. The data used can be used to provide information about what is appealing to the viewer most. Below is a screenshot of how Netflix collects large amounts of data.

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#3 Big Data Analytics for Risk Management

The current and extremely uncertain business environment requires improved risk management practices. A risk management strategy is a crucial investment for all businesses, no matter the industry. Being able to anticipate the possibility of a risk and mitigate the possibility of it happening is essential if your business is to keep its profits . Business experts will suggest that risk management for enterprises is more than just ensuring that your company has the appropriate insurance.

As of now the big data analytics have been a major factor in the development of risk management strategies. The tools available enable companies to measure and analyze their risks they face each day. With the growing accessibility and variety of data Big Data Analytics is a great opportunity to enhance the quality model for risk mitigation. So, companies will develop more effective risk mitigation strategies as well as make strategic choices.

But, they must be capable of implementing an organized evolution in order to take into account the wide range of data. In order to achieve this, they gather internal data first to obtain precise insights that can help them. Most important is the integrated method of analysis that an organization employs. An efficient big data analytics system can to ensure that weak points or risks are identified.

A Brand's Example that utilizes Big Data Analytics for Risk Management

UOB bank in Singapore provides an illustration of a company which makes use of big data to guide risk management. As a financial institution, there is a huge chance of losing money when risk management isn't properly thought out. UOB bank has recently tried the risk management system which is built around big data. The risk management system that uses big data system allows the bank to cut down on the time to calculate the risk's value. In the beginning, it took around 18 hours, however, with the risk management software which makes use of big data it can be done in a couple of minutes. Thanks to this initiative it is possible that the bank will be able carry out live risk analyses in near time (Andreas 2014).

4. Big Data Analytics As a Motor for Innovations and Product Development

Another benefit for big data lies in its capacity to assist companies in the process of developing and improve their products. In general, big data has been an avenue to generate additional revenue streams , allowing improvements and innovations in product. Companies begin by adjusting all the data that could be technically feasible prior to establishing new product lines or redesigning the existing ones.

Every design procedure must start by determining what will satisfy the needs of the client. There are many avenues that an organization can use to examine the needs of customers. The business then can determine the best way to meet this need, based on large data analysis.

"Gone were the times where you had the freedom to go with intuition" (Rampton 2017, 2017). To improve the quality of your product and optimize your manufacturing process, you must collect massive amounts of data. The gut feeling is no longer valid when an organization is trying to be competitive with the competition of 21 ^21st century. That means that companies have to come up with a system to monitor their competitors, products and feedback from customers.

After the data has been accessed and analyzed, an analysis is carried out to make sure that logical reasoning is used before a plan is formulated. The good news is that product makers of all sizes have advantages when it comes to collecting and harnessing massive data. This means that they are able to easily enhance their product lines by creating unique products.

A case study of the use of Big Data to Drive Innovations

You've probably heard of Amazon Fresh and Whole Foods . This is a great illustration of how big data can be used to improve the efficiency of innovation and development of products. Amazon makes use of big data analytics in order to grow into a market that is huge. The logistics driven by data gives Amazon the expertise needed to allow the creation and attainment that are more valuable. By focusing in big data analysis, Amazon entire foods are equipped to comprehend how customers purchase groceries as well as what suppliers do to interact with retailer. The data provides insights when it is necessary to make additional adjustments.

#5 The Use of massive Data for Supply Chain Management

Big data can provide supplier networks with more clarity, accuracy and insights. By utilizing Big Data Analytics, companies can gain context-based intelligence across supply chains. Through big data analytics, suppliers are able to overcome the restrictions that they faced in the past.

This was due to the use of older enterprise systems, as well as those that manage supply chains. These older applications did not make use of big data analytics so suppliers suffered massive loss and were vulnerable to make mistakes. With modern methods that are based on big data, suppliers are better able to benefit from higher levels of contextual insight that is essential to ensure the success of supply chain.

The modern supply chain system built on big data can create more intricate supplier networks. They are built on the sharing of knowledge and collaboration at a high level in order to attain contextual intelligence. It is also important to be aware that supply chain executives view the use of big data analytics as disruptive technology. It is based on the belief that it will provide the foundation for the management of change in businesses.

A Brand example which makes use of Big Data for Supply Chain Efficiency

PepsiCo is a manufacturer of consumer products that is dependent on massive amounts of information for effective supply chain management. The company is dedicated to making sure that they stock the retailer's shelves with the appropriate quantities and varieties of merchandise. Clients of the company provide reports that contain their inventory at their warehouses as well as the POS stocks to the business. the information can be used to calculate and reconcile the needs for production and shipping. This way, the business makes sure that retailers have the correct items, in the appropriate quantities and at the appropriate timing. Watch the webinar in which our Customer Supply Chain analyst discusses about the significance for big-data analytics within the PepsiCo supply chain.

The most important takeaway

The use of big data analysis is an essential investment for businesses that are growing. With the implementation of big data analytics businesses can increase their competitiveness and reduce costs of operations and improve customer retention. There are a variety of sources of customer information which businesses can make use of. With the advancement of technology data becomes easily accessible to all businesses.

In terms of technicality, it's acceptable to state that companies already have data available. It is the responsibility of individuals to ensure that they put in place appropriate systems for data analysis capable of handling the massive amounts of data. Do you have a data analysis system in place? Study the previous examples of brands that have succeeded and start implementing one now.