Predictive analytics is an ad hoc analysis. It assists organizations to apply the most effective solutions for their business growth and predict possible future scenarios. Currently, several organizations are deploying advanced solutions that can help them in the development of impactful business strategies in real time by improving their day-to-day decision-making. These solutions also help organizations implement business intelligence (BI) and analytical tools to acquire accurate insights from predicted data, which further helps in boosting the financial performance and credibility of companies who adopt these solutions.
Massive data dependence and adoption of advanced technologies such as AI, machine learning, and big data is expected to drive the demand for predictive analytics solutions. These solutions transform collected data into insights; for instance, they detect patterns to determine a suitable course of action. They have also been instrumental in the development of search advertising and recommendation engines. Growing awareness about the importance of available data and its analysis for predicting the future scenario is offering a competitive edge to end users, thereby driving the demand for these solutions.
Globalization, coupled with economic growth in various countries, also plays a crucial role in higher data generation across the globe. With the exponential rise in organizational data, companies are increasingly hiring teams of analysts and data scientists for the processing and analysis of collected data. Hence, organizations are increasingly deploying predictive analytics solutions for appropriate usage of available data for the prediction of future trends. Additionally, the high revenue-generating potential of these solutions is accelerating the adoption of these solutions.
The ecommerce sector has improved the traditional shopping experience of customers. Dedicated online/social media advertising, email campaigns, and customer perception analysis are the key factors improving the sales of companies. Growing popularity of connected devices has resulted in retailers focusing on real-time analysis of the shopping behavior of consumers. The outcomes of real-time analytics can further be utilized for developing tailor-made offers to enhance customer retention.
Key factors driving the demand for predictive analytics solutions are enhanced product usability, adoption of new technologies, massive data deluge, and newer levers of differentiators. In addition, growing need for database management, data warehouses, forecasting, data mining, smart logistics, CRM, and data visualization in dashboards is supporting the growth of the market. These solutions help map the various stages of the buyer journey, which would help organizations adopt suitable marketing campaigns, subsequently leading to higher sales and customer retention. Increasing demand for these solutions from organizations to equip their business functions with analytical capabilities are also expected to drive the growth of the industry.
Techniques for implementing predictive analytics include machine learning, deep learning, regression, regression modeling, mathematical, text mining, neural nets, genetic algorithms, clustering, decision trees, & data exploration techniques to gain insights from historical & present data. These techniques are increasingly being adopted by organizations for various applications such as CRM, direct marketing, risk management, portfolio management, fraud detection, underwriting, & credit scoring.
These solutions find applications in various end-use industries such as BFSI, healthcare, retail, manufacturing, government, sports, transportation & travel, IT, energy & utilities, and entertainment. These solutions help manage diversified applications across the aforementioned verticals. For instance, in the manufacturing industry, these solutions can be used for equipment maintenance management, workforce management, and cross-selling and up-selling. Moreover, the IT and telecom industry can adopt these solutions in sales, marketing, and CRM by implementing churn & pricing optimization.
Prominent players operating in the predictive analytics market include Microsoft Corporation; SAS Institute, Inc.; IBM Corporation; General Electric Company; Schneider Electric SE; Oracle Corporation; SAP ERP; Siemens AG; NTT Data Corporation; and Tableau Software, Inc. These players have implemented various organic and inorganic growth strategies such as product launches, mergers, acquisitions, collaborations, and partnerships.
IBM Corporation has been an innovative developer of predictive analytics solutions for various industry verticals. The company offers SPSS solutions that predict future outcomes for enhancing the performance of an organization. Moreover, manufacturers operating in the market have started to offer cloud-based solutions to their customers. For instance, in February 2017, Oracle Corporation launched and added four new cloud-based solutions in its IoT portfolio. The new solutions launched will aid businesses to take advantage of digital supply chains.