The power of machine learning to benefit mobile apps is remarkable. Although machine learning technology was initially conceived by tech pioneer Arthur Samuel while working at IBM in 1959, it has only recently entered the mainstream. An increasing number of app developers are opting to use machine learning in conjunction with other cutting-edge technologies such as artificial intelligence and predictive analytics.
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?
Predictive analytics (PA) are becoming more and more mainstream. According to an October 2017 projection from Stratistics MRC, the worldwide predictive analytics marketplace is expected to grow from $3.89 billion in 2016 to $14.95 billion in 2023. PA technology is already a noticeable part of everyday life, from product recommendations on Amazon to the search queries autofilled on Google and the advertisements you see while surfing the web.
Predictive analytics (PA) have transformed our online interactions in powerful ways. You’re continuously shown ads for products or services related to what you’ve just browsed or purchased. When perusing profiles on a dating site, you’re offered a list of people who have interests and lifestyles that are likely to be compatible with your own. And, searching the web has never been easier since you’re given suggestions for similar search queries that may lead you to the information you’re seeking.
As processing power increases, we’re seeing tremendous growth in the realm of predictive analytics. Predictions of future outcomes are developed using historical data, statistical data, predictive modeling and even artificial intelligence or machine learning in the case of more sophisticated PA platforms.
It took a bit of exploration, but you finally found an app with the specific functionality that you need. You were excited to explore the UI, but now, you can’t seem to find—much less master—the feature that originally prompted you to click the “install” button. Oh, and there it goes. It just crashed. Forget it; you don’t have time for this. You quit the app for the first and last time as you embark upon another search for a new app. Read More
Big Data and analytics (and especially predictive analytics) are powerful inventions of modern technology. Inventions that influence our lives by assigning our credit scores, protecting us from fraudulent credit card charges, showing ads relevant to our interests and more. But, these tools are not innately objective. In fact, if they are not developed correctly, they can reinforce discriminatory stereotypes, creating massive disadvantages for affected individuals. Read More