05.03.2024 11:30

Big Data For Enterprise

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Ever since the emergence of the COVID-19 pandemic, several businesses have adopted digital transformation strategies to keep up with online customer trends. Big data for Enterprise, analytics is one among the best digital strategies that have an immense potential to empower customer experience. Big data can help Enterprise businesses achieve a better and faster understanding of the customer journey or trends and create decisions to improve customer experiences.

Several technology giants such as Facebook, Google, and Amazon are well known to employ big data analysis for customer service. They have been able to predict customer behaviors to determine necessary business updates, new service generation, etc that have in turn helped them earn millions in revenue.

For the past two years, businesses have been turning their attention from just managing discrete touchpoints of the customer journey to assisting in the entire customer journey. As a result, the online journey has developed to apprehend the conception and delivery of a progressive customer experience before, during, and after a purchase or service consumption. This has been widely implemented across multiple channels, domains, and touchpoints.

For instance, Rapid enterprises Inc stamps helps eCommerce brands to send shipment notifications through messages or mail because brands can harness the big data’s capabilities to deliver a seamless customer experience. To manage customer experience efficiently, businesses will have to manage numerous touchpoints concurrently and, in doing so, reveal precise action items for business success.

The Different Types of Big Data For Enterprise Analytics

Similar to how weather analyses obtained from meteorological agencies can be interpreted and studied to predict weather patterns, big data can help in predicting customer patterns. Moreover, analysis of big data for Enterprise holds the answer to the evolution of an enterprise and in the development of successful innovative products. Big data analytics requires the application of statistical techniques such as information system software, operations research methodologies, and artificial intelligence-based data mining.

For example, the analytics process in a big data world explains how to penetrate the dominant tools in data analytics to generate a strategic advantage and recognize new business opportunities. It has extensive applications not just in customer behavior predictions but can also identify credit risk, marketing solutions, and fraud. By leveraging data and analytics services from external experts, enterprises can efficiently harness the power of big data to drive transformative business insights.

There are several different types of Big Data analytics approaches, and these can be categorized as:

Descriptive Analytics:
This is an uncomplicated statistical system that explains what is included in a data set or database. Descriptive statistics includes measuring central tendency, charts, graphs, sorting methods, frequency distributions, probability distributions, and sampling methods. In this process, it would be good to check out the global data mining software market so that you can find and use the right solutions for dealing with data.

The result of descriptive analytics can be used to determine potential business-related opportunities in order to be successful. For example, charts such as a scatter plot chart, Sankey chart, and a simple bar chart can be deployed to show the number of users that find a product influential in their daily lives, therefore helping businesses take better approaches in developing those specific products.

Predictive Analytics:
Predictive analytics is considered to be the utilization of high-level analytical, information software, or operations research techniques to recognize predictive variables. It can also help in building predictive models into a descriptive analysis system. The results here predict possibilities in which the business can take advantage to enhance their commodities and services. For example, various regression can be practiced to confirm the connection (or lack of relationship) between ease of performance, expense, and safety on merchants’ acceptance of mobile money payment.

Descriptive research methods play a crucial role in the realm of predictive analytics. These methods involve systematically collecting and summarizing data to provide a comprehensive overview of a particular phenomenon. In the context of predictive analytics, descriptive research techniques are employed to gather and organize relevant data about various factors and variables. This data serves as the foundation for building predictive models.

Understanding that connections exist helps clarify why one set of independent variables influences dependent variables such as business performance. And utilizing an Excel alternative can streamline data analysis and uncover valuable insights into these relationships.

Diagnostic Analytics:
Diagnostic analytics practices the interpretation of previous data to determine the beginning of certain events. Therefore, diagnostic analytics increases descriptive analytics by examining why particular conclusions took place by analyzing the patterns in the collected data. The diagnostic analytics process is efficiently utilized in Artificial Intelligence for IT Operations, device health monitoring and diagnosis, IoT fault detection and industrial maintenance.

The Multiple Benefits of Leveraging Big Data Analytics For Enterprise

Identify the Solutions or Services that Need Improvement

Metrics and insights accumulated from big data can give a lot of input regarding the experiences your customers are having. It can track which products are least popular among customers and help businesses make improvements in those products. The product reviews can be identified and sorted with artificial intelligence and they can be leveraged to make modifications to the existing solution or services. Longer average handling time (AHT) or low rates of first contact resolution (FCR) from the analysis of big data mostly indicate issues that need to be resolved.

Understand Customer Sentiments

Customer sentiments play an imperative function in the development of brands. Mobile app development services are now implementing big data into applications to better understand how customers respond to the company’s services or solutions. This can help quickly resolve customer issues and avoid businesses from repeating the same problem again. This is beneficial for businesses to connect to their customers on an emotional level and win their loyalty. Big Data sources for this may include, website surveys, application reviews, call transcripts, reviews on social networking sites and online communities.

Enhance Targeted Marketing Practices

Nowadays, Enterprise look for more targeted approaches in reaching out to their customers. By tracking click-through rates of links delivered through marketing emails, website call to action (CTA) buttons and other channels can disclose whether or not the marketing is leading to actual conversions. Furthermore, website geotagging data may also provide businesses the possibility to focus on customer demands based on particular regions.

This can help brick-and-mortar stores or retail marts identify and stock items that have more demand in those specific regions. Brands may use the data generated from those analytics to promote certain products more and provide extra discounts for products that do not sell well. that may be applied in-store.


Big data can help Enterprise provide personalized omni channel customer experiences that can set their business ahead of the curve. Consumers all around the globe are adjusting to new shopping and digital habits due to the current pandemic. By leveraging big data precisely, businesses can turn these insights into actionable assets providing superior retail customer experiences.

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