Big Data Customer Analytics Explained in Plain Terms

Large companies understand that they can benefit significantly from data if they collect the data and apply special processing techniques. They can capture all the streamed data by observing the trends and insights into their domains to maximize their business. Such processing techniques are referred to as Big Data analytics.

Thanks to technological advancements and methodology, many people in the information technology industry and company conference halls are talking about “Big data” these days. Many people believe it will unveil new organizational capabilities and worth for those who get big data right. But what does “big data” mean, and how will its insights differ from those acquired through traditional analytics ?

This article includes all you need to know about big data customer analytics, including how data is acquired and analyzed using specialized approaches that aid people in marketing and customer insights analytics.

What is meant by Big Data?

The term “Big Data” refers to large datasets with a complex structure. They are challenging to store, process, and analyze. It is a tedious task to visualize the outcomes and consequences. 

Big data can also be defined as large volumes of data growing exponentially over time. The process of analyzing such bulks of data and identifying the corresponding patterns to predict favorable outcomes of data is known as big data analytics. 

The study of big data is at the heart of modern research and industry. These data come from emails, online transactions, photos, videos, audios, and clickstreams. In addition to these, the data originates from different sectors such as posts, logs, social networking, health records, search queries, sensors, and mobile applications. 

The data is then kept in databases as they expand enormously, making it impossible to gather, form, store, and manage information. Typical database software tools can share, analyze, and view data.

Big Data Customer Analytics
Photo by Olya Kobruseva from Pexels

Where is Big Data Analytics Used?

It is widely used in various fields such as government, business and finance. Such large volumes of data prove advantageous for business analysts providing them valuable insights about the products. This allows firms to have competitor analysis and have an advantage over other competitive organizations. Therefore, it is crucial to implement and save such large volumes of data effectively and efficiently.

These bulky, complex, and large datasets are regarded as “Big Data.” It comprises complex volumes of data that traditional data processing techniques cannot process. The concept of “Big Data” laid its foundations on the three V’s that is velocity, volume, and variety. 

The concept can be broken down into three distinct characteristics. The three Vs. of Big Data, i.e., velocity, volume, and variety, are critical to comprehending how we can monitor big data and how it differs from traditional data.

The speed through which the data is transmitted has become an essential factor in Big Data. The volume in big data focuses on the amount of data that needs to be processed and analyzed. The last V in big data stands for variety means the types and formats of data in which it is delivered. 

What is Big Data Customer Analytics?

Big Data Customer Analytics is defined as the process in which the data retrieved from the customer’s behavior is utilized to make effective and intelligent business decisions. 

Predictive analytics and market segmentation are used for making predictive business decisions. Customer analytics helps improve customers’ experience with the companies or brands by segmenting the customers according to the insights obtained from customers’ data.

With the advancement in technology and digitization, businesses are subjected to volumes of data that need to be analyzed and utilized accurately. That is why Big Data Customer Analytics is becoming popular in the business world, and large companies are using it extensively to learn customer behavior. 

Big Data Customer Analytics helps businesses to strengthen their relationship with their customers and categorize the customers according to the products and services they prefer.

The basic concept of Customer Analytics is that the companies gather data from their customers. They use some technology to perform analytics on it. As a result that, they get insights about their customers. These insights can boost their marketing strategies for products and services and help in return on investment (ROI) for their services.

The Framework of Customer Analytics

Customer Analytics help raise strong campaigns based on customer behaviors, such as purchase history, demographic details, and browsing details. It helps learn customers’ likes and dislikes. As a result, the companies can categorize their customers and suggest services according to the likings of a specific category of customers.

Hence, this would not be wrong to say that a firm’s data collection and analytical strategies play an essential role in marketing and uplifting an organization from its competitors. The framework of Customer analytics laid its foundation from three points explained below:

  1. Understand the preferences of your customers

The first and foremost step of getting started with big data Customer Analytics is to know who your customers are. And how is their relationship with your organization? This is done by creating a diagrammatic mapping known as “Customer journey mapping.” This includes all the customer preferences from your products, repeatedly bought products, and their post-purchase history.

This helps firms get to know their customers better and get the answers to the questions such as who their customers are, their preferred products, mode of purchase, and communication. Organizations can create profiles for their customers by answering the questions mentioned earlier using big data customer analytics.

  • Collect and analyze relevant data about them

After completing the first step, you have identified which you want to collect the data that is leading to your goals. Gather and organize the customer data you retrieve from various touchpoints such as websites, user activities on the application, social media, and Customer Relationship Management systems (CRM).

Moreover, the firms can also provide data to the customer analytics framework via third-party purchases and conduct surveys. 

  • Define the goals you wish you achieve

The most critical section of the customer analytics framework is to define the outcomes you wish to achieve from the data insights. The strategy to perform analytics is determined based on the required outcomes , and results are achieved.


Customer Experience Management (CEM):

Customer experience management (CEM or CXM) is defined as a set of processes that an organization uses to analyze, supervise, and arrange every connection a customer has with the firm throughout the customer journey.

CEM describes how a company appears to customers’ journey. CEM is a marketing, sales, and consumer service approach emphasizing business consumers to improve brand awareness and retention. 

Voice of the customer programs, which analyze consumer perceptions regarding their experiences with an organization, are widely used in CEM projects. If we want to address negative perceptions, a CX management system will adjust portions of the customer satisfaction that create negative responses over time.

Importance of Customer Experience Management

Customer views and earnings are determined by the user experience or satisfaction with a company. It can be favorable or unfavorable, depending on which aspects of CXM are overlooked.

Developing and adopting a CXM strategy has several benefits:

  • Customer loyalty and satisfaction are much less costly than customer acquisition. According to statistics, a 5% improvement in client loyalty, retention, and satisfaction might result in a 25% increase in profit. This is due to several reasons, the most important of which is that holding a client saves a lot of money on financing costs, and thus the happy and loyal consumers prefer to buy again. User experience response motivates us to improve and learn from our mistakes.
  • Customer feedback, such as online surveys, questionnaires, mobile device feedback, and telephone and chat conversations, serves as a roadmap for better customer experiences and loyalty.
  • Customers will have a better product perception and experience if their personnel are happy. According to research, there is a substantial correlation between staff and client satisfaction. Organizations and businesses that provide the most satisfactory customer service focus on assessing employee voice data to better understand their engagement and satisfaction.
  • Satisfied, regular customers knowingly and willingly recommend a company’s products to colleagues. User suggestions are much more influential in purchasing decisions than business promotion efforts. Customer sentiment research reveals details about opponents. When buying certain products and providing reviews, users tend to compare firms and their products. Having this vital information can assist a corporation in obtaining an advantage over a competitor.

What is Customer Insight Analytics?

Customer Insight Analytics is defined as the interpretation of the data collected from customers, their associated behaviors, and feedbacks leading to the solutions and helping to improve the products and services of any organization. Gathering customer insights aligns customer needs with the outcomes expected from a particular organization.

Customer Insight analytics refers to the strategies and technologies that provide organizations the customers’ behavioral insights necessary to convey suggestions that are expected, related and well-timed.

Big Data Customer Analytics
Photo by PhotoMIX Company from Pexels

How can Big Data Customer Analytics impact business decisions?

Keeping in view the competition in today’s market, all the organizations in the corporate sector need to make their decisions based on proper research and analysis of the data collected from their customers. The customers’ insights, thus obtained, are subjected to Big Data analytics strategies to derive the proper outcomes and take decisions that uplift the firm from its competitor organizations in the market.

Another thing to be noted here is that every action we take these days generates data that can be used for various purposes. Every time a person touches their phone screen or interacts on the internet, a data point is created. All of these digital interactions, when combined, form a large number of datasets that the companies in corporate sectors can use to develop a complete profile about their customers.

Moreover, many ways impact the business decisions of large firms as a result of analytics performed on the data explained below:

  • Build Customer Persona

Big Data Customer Analytics is used to unite structured and unstructured data in different formats. The retailers can create complete users’ profiles, including their likes, dislikes, purchase history, etc. 

  • Ensure Customer Satisfaction

Big Data Customer analytics positively correlates by enhancing the customer experience. This is done by accurately and smartly marketing the targeted audience with accurate services that they like the most.

  • Provide personalized services to the customers

In addition to profiling customers, customer analytics can also help personalize the services for customers based on their customer journey mapping. This allows users to always see their most frequently accessed products and services instantly they log in to the sites.

  • Thoroughly analyze the reviews

Reviews are one of the best sources to get customer insights about the products and services of any organization. This is the source in which users express their sentiments, satisfaction, and overall experience with the service. It is advised to go through the reviews more often to get proper customer insights for analytics and help in better decision making.

  • Analyze the competitor organizations (Competitor analysis)

Competitor analysis is concerned with analyzing the products, services, and strategies of competitors to see what are shortcomings on your side and how you can improve them. In this way, you can devise better ways to decide which measure favors your business.


Big Data analytics is a paradigm-shifting innovation. Many companies use analytics to make smarter decisions and better user experience and satisfaction. Most firms resort to big data because even the smallest increase in efficiency or cost savings can add a considerable profit.

In this article, we discussed big data and big data customer analytics that help the corporate sector make decisions in the best interests of their business. The data is collected from customers and is analyzed using technologies to get insights that provide customer satisfaction by aligning customer needs with the organizational goals.

Emidio Amadebai

As an IT Engineer, who is passionate about learning and sharing. I have worked and learned quite a bit from Data Engineers, Data Analysts, Business Analysts, and Key Decision Makers almost for the past 5 years. Interested in learning more about Data Science and How to leverage it for better decision-making in my business and hopefully help you do the same in yours.

Recent Posts