As big data continues to explode, many companies are capturing and analyzing the flow of information in real time to predict outcomes, and they are using these insights to prevent fraud, create new growth opportunities, and improve customer loyalty while achieving operational excellence.
The most competitive firms have embraced the key capability driving this transformation: predictive analytics. Approximately 73 percent of companies surveyed recently indicated that they have adopted predictive analytics into their big-data strategies. Today’s advanced business intelligence platforms can process and collate all relevant available data sources and identify patterns and anomalies. These anomalies can then be used to detect fraud, foresee deviation from expected outcomes, and finally, to isolate dynamic micro-segments within the large data sets.
Case in point: Recently, big-data analysis helped a large European mobile service provider to detect fraudsters making free international calls through their network. They were able to control the issue before it spread throughout the system, thereby avoiding significant bottom-line impact and improving network security.
Companies can leverage the competency to identify dynamic micro-segments in several different ways. They can recommend next-best-action (NBA) or next-best-offer (NBO). These can be applied to improve customer retention or for cross- and upselling, and marketers can isolate micro-segments for campaigns and identify decision-characteristics of target customers. They can run more targeted batch campaigns, make more compelling offers to customers, and create a greater ROI.
For example, a service provider has an enhanced ability to predict subscriber churn based on combinations of a large number of predictors, such as date of service initiation, number of customer contacts after setup, and measurement of subsequent service usage. With this information, the system initiates a retention action, such as triggering an outbound customer care call or sending a prompt apology e-mail for service interruptions.
Moreover, predictive analytics through real-time scoring can predict which offer a specific customer will respond to and utilize that insight to increase the probability of a positive response. This means higher customer retention and increased revenue per-customer at a lower sales costs.
T-Mobile, for example, actively listens to social media to turn unhappy customers into happy ones, thus transforming happy customers into recommendation engines. Furthermore, near real-time customer sentiment analysis has helped them tweak their marketing campaigns and make them more effective.
The future competitive edge with real-time data, predictive analytics, and big data
Globally manufacturing continues to grow, accounting for 16 percent of global GDP and 14 percent of employment. Corporations plan and procure products and services globally through complex multi-tier supply chains. The true competitive advantage comes from their ability to leverage their supply chain to reduce costs and working capital while improving response times.
Increasingly, logistics equipment and production machinery have become connected to the Internet of Things (IoT), giving rise to connected logistics and connected manufacturing. Quick and effective actions are now possible to get parts to the right place at the right time in the right quantity.
Real-time visibility and predictive capabilities help boost on-time delivery, optimize inventory, accelerate production, and improve the process overall with the potential to positively and radically transform it. Some high-tech companies are now planning to assemble products on ships while they are en route from component factories to their destination, for example, thus reducing lead time and cost of capital.
Mass customization of products represents another opportunity that digitally savvy enterprises can offer to create new experiences for customer segments of one. Finally the continuous optimization in supply chains is being leveraged to reduce landed cost while maintaining high quality.
At this point big data, data science, and predictive analytics (known by the acronym DPB) have emerged as crucial planks in the platform of overall operational excellence. Now these three fields have moved in the direction of forming a holistic, comprehensive, and integrated approach to performance measurement and analysis. Currently, these disciplines use mathematical, statistical, and behavioral science as well as computer science to harness the volume, velocity, and variety of big data. With the requirement to make sound decisions by leveraging big data in the IoT era, business leaders are seeking to connect their products and entire value chains to systematically gather and analyze data with applications built on top of DPB functions.
A growing urgency to embrace the above trends is revealed by a survey showing that 88 percent of executives expect to integrate a big data or analytics platform within three years. Many of them will do so with the support of their boards in order to stay ahead of their competition.
Real-time exploitation of big data carries great promise for innovation, and its mastery may determine the winners and survivors of the global business landscape in the years to come.
For more thought leadership on how future tech will influence business, see Big Data, The Internet Of Things, And The Fourth V.