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Predictive analytics is already taking the business world by storm, serving as a powerful and reliable way to improve customer relations and businesses in general. But as a technology, it’s always developing at an accelerated rate; and as marketers, analysts, and business owners, we need to be prepared for what’s next. Predicting the path of our predictive capabilities may seem a bit odd on the surface, but it can help you plan for the tools that may become available to you in the near future.
The Next 5 Years for Predictive Analytics
Predictive analytics will likely develop along these six lines, at a minimum:
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More specific applications. Currently, most predictive analytics software focuses on tracking consumer behavior and trying to make predictions about how they’re going to spend money in the future. But more specific applications are already being developed, and over the next five years, we expect to see even greater diversification. For example, you may see software that focuses exclusively on finding ways to help companies generate more revenue or software that delves deeply into employee productivity and future patterns of growth. We’ll also see software developed to cater to specific niches and industries, such as specific development for small business owners or restaurants.
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More flexible customization. People love to customize their own solutions, so the predictive analytics industry is likely to respond to this ever-present demand. Future platforms and apps will likely give more control to the marketers and business owners using them, enabling more advanced forms of custom reporting, build outs, and even integrations with other software. This will likely also include more malleable full software systems that integrate predictive analytics into other kinds of tracking and management software.
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Data visualization. Raw data is hard to monitor and interpret, even for experienced, trained data analysts. That’s why data visualization is such a hot rising trend; predictive analytics software will soon take giant steps forward into projecting data in a more visual format, helping users gain intuitive takeaways and more easily communicate their conclusions.
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Integration with the IoT. The Internet-of-Things (IoT) is already seeing progress in consumer adoption, and will likely become commonplace within the next few years. Consumer appliances and devices will all become interconnected, giving companies the ability to draw more personal information than ever before, including gauging the kinds of habits people have in their own homes and daily lives. Finding a way to use predictive analytics to not only draw more data from this network but incorporate it in more meaningful ways for companies, is a top priority for today’s software developers.
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Greater accessibility. Currently, predictive analytics is relatively available and affordable for business owners, but it’s going to grow even more so in the near future. As more competition floods the market and more sophisticated solutions arise, some developers will differentiate themselves by offering simpler, cheaper, stripped-down versions of these products. They’ll still do a decent job, but they’ll be easier to find, buy, and use for the average business owner, sharply increasing the number of people relying on predictive analytics for their businesses.
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Individualized attention. Currently, most predictive analytics software focuses on broad-strokes insights and high-level takeaways. For the most part, this is a good thing, but future iterations of this software may be able to dig deeper, to a more individualized level. This may include trying to predict the potential future behavior of individual customers on an e-commerce platform or analyzing the past behavior of clients to evaluate their current dispositions.
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Above and Beyond
The further you look into the future, the less certain your predictions are going to become; this is a hallmark of all areas of life, but especially in the technology sector. It’s likely that the developments we predicted will be pursued, at least in some capacity, but it’s hard to tell how they’ll develop, or for how long they’ll carry down those lines. The long-term future of predictive analytics—beyond the next five years—will probably hold some interesting surprises. All we can do as marketers is stay abreast of these changes, and incorporate them into our approaches as they become available.
