Product Managers and engineers are using big data analytics to assess the behavior of users to identify where they’re dropping off, where they spend most their time, and find other patterns of usage in order to test new features, improve product adoption and increase monetization opportunities.
The secret to identifying new revenue streams lies in analyzing current product usage and creating new, data-driven products or services from those insights. For example, combining customer purchase data, product support logs, and server logs can predict when a server might run out of storage or memory capacity before it actually happens, opening the door to a new predictive maintenance offering for your company.
Consumers today interact with companies through lots of interaction points – mobile, social media, stores, e-commerce sites, and more – which dramatically increases the complexity and variety of data types you have to aggregate and analyze. When combined and analyzed, you can quickly and easily identify who are your high-value customers, what motivates them to buy more, how they behave, and how and when to best reach them. Armed with these big data insights, you can improve customer acquisition and drive customer loyalty.