Evolve from Transactional Data to Real-time data for Complete Customer Experiences
Evolve from Transactional Data to Real-time data for Complete Customer Experiences

Whenever a customer interacts with a brand, data is generated. This interaction could be anything – buying a shoe online, watching the latest TV shows, financial transactions, downloading music, clicking on a product link, and more. All useful data regarding that interaction is saved like the name of the customer, billing information, shipping status, claims, etc. This data is known as transactional data and the interactions can be considered as transactions happening between a customer and a business.

Transactional data is very important for a business as it provides valuable insights into improving and enhancing the business. This data helps businesses get a 360-degree view into customer interactions and their behaviour allowing them to make efficient and more data-driven decisions. Useful transaction data can prevent expensive customer support calls, reduce incorrect order dispatches, detect and prevent fraudulent activities, and adapt to the change in customer demands. Capturing clean transactional data can be very beneficial for an organization.

Evolution of Real-Time Data

While the importance of customer data can’t be undermined, the timing is crucial as well. In 2022, customer demands, market trends, product supply, and other factors change in an instant, accelerating the need for making real-time data-driven decisions. However, transactional data can take days, weeks, and even months to get analyzed and deliver results. Fast-moving businesses need to know what their customers want and are looking for right now, not a week later. This delay can render the insights unusable and can be the key deciding factor between failure or success.

With the world’s data estimated to be reaching the 175 zettabytes mark by the end of 2024 and millions of data touchpoints being generated every second, it has become vital to store and analyze data in real-time. There are billions of mobile devices connected through the internet and interacting with different businesses around the world. Trends, products, features, and even technologies can become obsolete in the span of just a few days highlighting the importance of real-time business intelligence and analytics. Organizations can use real-time business intelligence to stay sharp in their decision-making, constantly monitoring the industry, and minimize the risk of outdated processes or customer insights for critical business decisions.

Benefits of Real-Time Business Intelligence

Capture Time-Sensitive Opportunities

While transactional data can help in creating long-term strategies for a company and its different domains, some activities require real-time insights. For instance, you’ve launched a new A/B marketing campaign to target young customers. You’d want to understand how both of the strategies are working in real-time in order to make instant decisions. Whether something needs to be changed, the rate of contact needs to be increased/decreased, is the ad capitalizing on the holiday spirit or is there something lacking? All these questions can be answered from real-time insights.

Harness Insights Efficiently

Collating and analyzing data are not enough. Organizations need to understand how this data can be used to improve the business, provide superior customer experience, and increase profits as well. With real-time insights available across departments, true data democratization is enabled where employees have access to customer data in real-time and they can make better and more informed data-driven decisions.

Detects Critical Issues Early

One of the biggest benefits of getting business insights in real-time is that it can help you detect critical issues early and prevent them from happening while there’s still time. These issues can be anything from a product about to go out of stock, problems with online payment, fraudulent or suspicious activity happening on the website, wrong order dispatched, service downtime, and more. These issues can be vital in building customer experience as nobody wants to order something online with high hopes but be informed later that the product is actually out of stock and that it wasn’t updated on the website. With real-time intelligence, however, you can keep a close eye on the inventory levels, place the “Out of Stock” label in front of a product before somebody orders it, and provide a tentative date when the product will be back in stock as well.

One of the best ways to understand the importance and benefits of real-time intelligence is to look at some actual use cases from some of the biggest companies in the world.

Use Cases of Real-Time Business Intelligence


The finance and banking sector can leverage real-time insights for a number of purposes. It is expected that by 2024, the credit card industry would reach $1.82 trillion in annual volume. That means an enormous amount of data is being collected and analyzed. Credit Card company American Express (Amex) is collecting and analyzing transactional data from both the merchants and customers in real-time. This insightful data is helping them serve their customers better, analyze shopping trends and spending habits of the customers, detect any anomalies in card activity, and protect customers from fraud as well. They’re also employing data analytics to build efficient algorithms for attracting and retaining customers by providing them customized offers.


Which delivery route the driver is taking, what are the pick-up and drop-off times, the current location of the vehicle and other such real-time data is crucial for freight forwarding companies as well as ride-sharing platforms. Ride-sharing platform Uber harnesses the power of real-time data and advanced technologies like AI and ML to efficiently manage changing variables and adapt with the same. It helps them make critical business decisions like surge pricing for certain areas, estimated time of arrival for cabs, demand forecasting with respect to the place, destination, time of the day, etc. They analyze customer data as well to provide customized offers or discounts enticing the user to choose their app over competitors.

Media and Entertainment

The media and entertainment sector has a lot of use for real-time customer data. Not only there are hundreds and thousands of content pieces to watch, but every person also has a different preference, demographic, favourite language, and other criteria when it comes to watching something online. Streaming companies like Netflix analyze user data in real-time to suggest shows that are popular in their region, are similar to the content they’ve watched, or belong to the genre they watch the most. Similarly, Facebook runs continuous data analysis to prevent dangerous content like fake news to spread across the website, people from abusing each other online, identifying malicious activities and more.

Other Examples

Shell combines real-time data with machine learning algorithms and machine vision-equipped cameras in their forecourts to detect and warn people who are smoking. Similarly, the ZSL and WWF Wildlife Insights program in South Africa’s Kruger National Park analyzes video footage taken and uses machine learning to detect any suspicious activity and raise alarms indicating a danger of poaching. Tesco used real-time data analytics to discover that customers aren’t generally liking the way products were hung together. They understood their customer needs and brought changes to not only reduce their distribution costs but bring down food wastage as well.

Final Thoughts

Customer data is growing every day. Nearly 2.5 quintillion bytes of data in just 24 hours. Managing this large amount of data, extracting useful information from it, and scaling on demand has become a critical business demand. Agilisium provides data analytics solutions by migrating data from on-premises to the AWS cloud in real-time. We do a detailed assessment of the data and suggest adequate solutions that are efficient, reliable, scalable, secure, and less costly as well. Any growth in data can be handled easily by scaling the solution. We are an AWS Advanced Tier Services Partner who aims to accelerate the Data-to-Insights-Leap for organizations.

“Agilisium architected, designed and delivered an elastically scalable Cloud-based Analytics-ready Big Data solution with AWS S3 Data Lake as the single source of truth”
The client is one of the world’s leading biotechnology company, with presence in 100+ markets globally, was looking for ways to maximize impact of their sales & marketing efforts.

The lack of a single source of truth, quality data and ad hoc manual reporting processes undermined top management’s visibility of integrated insights on sales, sales rep interactions, marketing reach, brand performance, market share, and territory management. Understandably, the client wanted to align information that has hitherto been in silos, to gain a 360-degree product movement view, to optimize sales planning and gain competitive edge.