How Big data Analytics helps to discover market trends and customer preferences

5Oct - by aiuniverse - 0 - In Big Data

Source: techiexpert.com

Every business around us in the market is successful because of the analysis they make. When they make the analysis, the review is on large volumes of data. The study made should be in-depth enough. When a large amount of data is provided to analyze, the analysis that is being made should be accurate, and all the patterns that are hidden inside the data should be understood. For this process Big Data analytics, a trending technology is used. It helps to discover the market trends very quickly. The future of big data analytics and it’s usage in various fields is going to help to discover the market trends and customer preferences.

What is big data analytics?

As the name itself says big data is nothing but large amounts of data, complex data form new sources. Since this data is so voluminous, it is not easy to make any applications on this data or to analyze this data. So for this bulk amount of data software called Big data analytics is used. It analyses large amounts of data, processes it, finds the hidden patterns, correlations, and big data trends. It also helps to uncover various information such as customer preferences which in turn helps to increase the growth of a business. 

History of big data analytics:

Analyzing data is never new. All of us do the analysis. If we look through our childhood, we all used to have report cards in our school. It is nothing but the data of our performances in each subject using which we used to improve ourselves and know regarding ourselves. Coming to the business perspective of analyzing data earlier, they used to analyze data using EXCEL sheets, which is a very difficult process and if the data is in bulk, then it is a tedious process. 

Working with large amounts of data was never new. In the 1950s and 70’s itself, people used to work on it, but the evolution of Big Data Analytics helped to get results faster and accurately. Big data analysis came into the picture in the year 2005, and Hadoop was developed in the same year. Hadoop is an open-source framework that is created to store massive data and run processes on it, analyze it and get desirable results. Along with Hadoop, NoSQL also started gaining its popularity. With the development and emergence of other technologies like the Internet of things(IoT) and cloud computing the requirement of big data analysis has increased enormously. 

Importance of big data:

We have seen what big data is and how it came into the picture. Every time we see regarding the big data, we know that it is essential. Reasons, why big data is necessary, is as follows.

  • It helps to store a large amount of data. Tools like Hadoop and cloud computing help to store large amounts of data.
  • It helps in cost reduction. Since it provides the analysis of the data present, we can know where we can minimize our cost and where we can invest more and earn a profit. Besides that, doing analysis itself is very cost-effective using big data analytics.
  • It helps to make faster decisions. Using big data analysis, we can extract big data trends, and using this, and we can make quick decisions. Since the software used also provides us with quick information, it helps us to make smart and accurate decisions.
  • It also helps us to launch new products and services according to the information about the customer.
  • It also helps to stay in the competition and also provide healthy competition for rivals.

How can we understand our customers with big data analytics:

When we do any business, our first plan is to get profits along with providing what a customer wants and giving them satisfaction. By using big data analysis, we can get a detailed report of what our customer needs. For example, if we manufacture a product and send it to sales, then if we can store the information regarding the customer who brought our products if we use big data analytics we can do miracles in our business.

The big data analytics will help me to obtain the information regarding the age group of people using my product, gender, employment status, financial status, lifestyle, marital status, their interests, their location, the reason behind them choosing my product, how do they use my product, what made them purchase a product, what are their interests, why they are choosing my product because of the brand or the product. When we have the information regarding all these things, it will be easy for us to take any forward steps.

Some of the areas of decision making in which big data trends help us are:

  • Customer acquisition costs:

It helps us to analyze how much we have spent to get this type of customer. In case if we spend more then we have to make sure that the customers are loyal to us, make more purchases from us and we have to provide them with services that keep them bound to us. In case if this is not going to happen, then we can use the feedback we got using big data analytics, and we can change the acquisition costs.

  • Customer retention costs:

It helps us to analyze what our customers need from us to stay with us. It helps to gain the information whether they need customer service or they are expecting the improvement in the product quality, or they are expecting it to be manufactured differently. In any business, customer acquisition costs are more compared to customer retention costs. Using big data analytics, we can find out the information using the previous data available regarding our customers and make the decisions accordingly.

  • Average purchase amounts and behaviour:

It is necessary to know how much our customers are willing to spend for a certain kind of product. We need to know if they are happy to spend more if we are going to make any changes to our product to launch a sibling for our product. Sometimes it is not just the cost on which the sale of the product is dependent on sometimes; it also depends on the value. By using big data analytics, we can get the demographic representation of our customer’s interests.

  • Customer satisfaction and happiness:

Once we step into any business, the first step is to make sure that our customers are happy so that we can make them loyal to us. We have to analyze whether our customers are happy or unhappy. In case we find out that our customers are unhappy, we need to dig out the reason behind their unhappiness and provide them with what they want. To dig out the reason, the easy way is to compare a bunch of happy customers with a bunch of unhappy ones. The differences can be easily figured out using the results provided by using big data analytics.

  • Value alignment:

Whenever we start a business, we intend that some group of customers should buy our products. Once our product is out in the market, we have to check whether they are buying it. In case they are not buying, we have to dig the reason behind that. If we do this, it will help us to track our needs to increase our customer sales. This kind of information can also be extracted using big data analytics.

  • Lifetime value:

We need to calculate the amount of money that the customers are ready to invest in our product and on our company in their full lifetime. So this will help us to know when we are going to stay in the market and when there are chances for us to see a downfall. Once we have this information, then we can provide our customers with what they need, and in turn, we can stay happy. To get this information, we need to analyze a very bulk amount of data, and big data analysis is going to help us for the same.

  • To provide effective customer service:

   The success of any business depends on the customer service they provide. We can convince any customer to buy our product if we have good marketing skills, but in case if you are expecting the customer to come back and buy your product repeatedly, we should be able to provide the best customer service. To analyze the kind of customer service to be provided to the product, also big data analytics will help us.

The future of big data analytics is bright. It helps to analyze the market trends and analyze customer trends. It is being used currently in many fields like life sciences, banking, manufacturing, retail, by government, and in many other fields.

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