Remembers when marketers launched a campaign and waited for weeks to know if it was working? Real-time analytics has changed all that.
Every second we create 2.5 quintillion bytes of data, of which 90 per cent has been created just in the past two years. This is referred to as Big Data, simply because of the sheer size of information, we generate every time we are online. So what is the connection between this Big Data and real-time analytics? How can businesses leverage this data to create enhanced customer experiences?
So what is real-time analytics? To put it simply, real-time big data analytics means that data is processed as it arrives, and a business user gets very specific insights from this without losing the vital time available for decision-making. In some cases, a pre-programmed analytical system uses this real-time insight and triggers an action or a notification for improved customer interaction. This is a generic definition, and there are multiple parameters for this process such as – is the data structured or unstructured, which industry is this data being analysed for, and what are the timelines for each sector.
To understand better how different industries use real-time analytics, let’s dive into some case studies and clarify any questions that may have arisen because of the standard definition.
Real-time Analytics in Banking and Finance
The world of banking and finance was one of the first sectors to adopt real–time analytics aggressively. The reason? This sector deals with vast volumes of data, highly time-bound functions, extreme vulnerabilities, and the need to detect complex patterns in real-time and act on them immediately. Banking and finance companies across the world use real-time analytics for several functions, including detecting any money laundering activities, suspicious transactions, account hacking, stock-market surveillance, credit score generation, and risk management. Based on behavioural data from individuals and markets, they can ascertain if a certain exchange looks like a case of phishing or irregular activity. This has resulted in more and more secure banking and financial services like password regeneration, face recognition, finger impressions, UPI transactions and more. Not only security but also real-time analytics has enabled banking and finance institutions to provide personalised offers and services that adds tremendous value to the customer.
Real-time Analytics in CRM
Marketing conditions are tough today with brands aggressively reaching out to customers with unique propositions of their own. In such a scenario, when a company adopts a real-time analytics tool to enhance their Customer Relationship Management (CRM) system, then they are sure to be one step ahead of the competition. How? By knowing what the customer wants even before he does. That is what historical data can help you figure out. Real-time CRM analytics refers to the methods used to source and exploit this information. Quick response to changing market trends, meeting consumer demands in time (or even before), providing personalised services, and driving brand loyalty has been made possible by studying and using consumer data via real-time analytics.
Real-time Analytics in Retail Promotion
Real-time analytics has made it possible to target individual customers in retail outlets with promotions and incentives, while the customers are in the store and next to the merchandise. With so much new data available, executives in the retail sector can adjust their business strategies and cater to consumers in ways they never could. Consumers are impulsive and have always been so, but with real-time analytics retail brands can address this nature by catering to their every desire and don’t have to stick with the ‘take it or leave it’ attitude. You can be efficient by introducing new products, delivering personalised shopping experiences, and be a part of the customer’s journey.
Real-time Analytics in Fraud Detection
As is technology is evolving, so is the opportunity for fraudsters. Fraud is a high-cost threat to the integrity of any organisation, reason why companies have to invest massive amounts of money and efforts to protect themselves against fraudulent behaviour. Real-time analytics is a revolutionary way to be proactive in preventing such frauds rather than taking measures after the incident. By applying algorithms that use historical data to detect anomalies and patterns, data scientists can discover a potentially fraudulent activity before it occurs and act against it!
These are just four broad applications. However, the benefits of real-time analysis are huge. There are many more avenues where real-time analytics works like the Internet of Things (IoT), mobile marketing, manufacturing industry, traffic management and more. Today, companies can’t afford to avoid Big Data Real-time analytics can transform your business and increase your effectiveness while improving the customer experience. It provides an immediate company vision that can be used daily to make improvements and find real success.