In recent years, human resource teams have begun leveraging predictive analytics (PA) to automate certain tasks while helping employees complete their work at a higher level. From improving recruitment to helping companies overcome bias, predictive analytics tools for HR are playing an integral role in creating more balanced work environments far and wide. Today, we’re taking a look at several key ways businesses are using PA to improve their HR operations.
The world of retail is changing, with heavyweights such as Macy’s, Sears and Kmart shutting down many stores. So, how can retail businesses adapt in order to stay relevant? One strategy is to leverage predictive analytics (PA).
Traditionally, the apparel retail industry has consisted of customers going into stores, trying on clothing, and then deciding whether or not to buy. However, now that people frequently buy apparel online, businesses are leveraging the power of predictive analytics (PA) to improve their customer conversion rates and reduce returns. Machine learning, predictive modeling and data analysis are the backbone of PA technology, which uses historical shopping data to help businesses make well-informed decisions.
The travel industry is one of the most technologically advanced markets in the world, adopting big data and predictive analytics (PA) when the tech was still in its early days. The highly competitive nature of the industry has prompted airlines and hotels to develop apps that leverage this cutting-edge technology in order to satisfy and retain customers.
We live in an increasingly digitized world where developers are using data to help companies gain valuable insights regarding their business. This information can be leveraged to determine flaws in an organization’s structure and operations, churning out smart solutions that reduce costs and increase revenue. This is especially true in the healthcare industry, where predictive analytics (PA) plays a key role in bolstering a patient’s treatment plan and reducing out-of-pocket costs.
Predictive analytics and big data are two forms of cutting-edge technology that are commonly integrated into apps and software, but many individuals are confused when it comes to how these two technologies compare. In fact, many are unsure which data-handling technology is best for their development project, which can make hiring the right developer a challenge.
Predictive analytics (PA) have lots of different applications, from weather forecasting to contextual advertising, business projections and more. But there’s one area where this technology really shines, and that is the realm of ecommerce. By using predictive analytics for ecommerce, you’ll have the ability to anticipate a shopper’s wants and needs, increasing sales by a significant margin in many cases. So what are the best strategies for using predictive analytics for ecommerce? And how do you implement this technology with your existing virtual storefront?
In nearly all areas of life, we consider the facts and probabilities before making a decision. This is true whether you’re making important investment choices or simply wondering whether to park on-street in a metered space or inside a parking garage. So it seems rather illogical—and even downright reckless—to make critical business decisions without a probable and factual basis. Yet company leaders make such determinations every day, with some choices carrying a multi-million dollar impact.
Over the past decade, predictive analytics technology has been introduced into the mainstream. This application of predictive analytics has some useful features, including eBook recommendations based on your reading history and cutting-edge matchmaking systems that leverage societal trends and specific user information to identify potential soulmates. And while the end user may not realize a particular mobile app feature is powered by a predictive analytics engine, this technology’s impact can go a long way toward helping companies achieve their goals and guiding customers to products and solutions that actually interest them.
You’ve developed the perfect app concept. The market research has revealed promising data, and you’ve come up with some killer branding. You feel pretty confident going into the development process—except when it comes to the predictive analytics engine. That’s a region of uncharted territory, as you’ve never integrated this technology into a mobile app interface before. So, how do you ensure you’re on a path toward success?