Predictive Analytics: Taking Big Data to a whole new level

Although predictive analytics is a buzzword right now, the concept has actually been around for a long time. Academics developed the statistical tools for making predictions years ago, but back then, neither they nor businesses had the amount of real-time data they needed to use it in commercially useful ways.

Recently, that’s changed. Thanks to an explosion of big data from tracking customer activities online, companies have more information about their prospects than ever before - and they're putting it to good use.

The Impact Of Big Data On Predictive Analytics

A software company called New Relic recently demonstrated the power of data-driven predictive analytics. They used predictive analytics tools to boost conversions by an order of magnitude, blowing their previous methods out of the water. Because predictive analytics provided insights about what their customers wanted, the firm was able to more effectively target those who would genuinely benefit from its solutions.

Other companies, such as Swedish car maker Volvo are using big data for different purposes. Volvo has stated that it’s mission is to reduce the number of fatalities among people driving its cars to zero by 2020. When this goal was first announced back in 2008, it seemed hopelessly lofty and out of touch with reality, but the company hopes that big data will provide the necessary insight required to make it happen. Given what we’ve seen from predictive self-driving car software so far - for instance, when a Tesla Model S anticipated a crash up ahead between two cars in California before it happened, slowing down to avoid the danger - it seems entirely feasible that Volvo will achieve its objectives.

Big Data-Driven Predictive Analytics Are Boosting Marketing Efforts

Big data is also having an enormous impact on the way that businesses market products to their customers. A study by Radius in 2016 called the B2B Demand Generation Benchmark Study found that marketers who did not use predictive analytics reached their objectives 30 percent of the time, while marketers who did use predictive analytics achieved their goals 55 percent of the time.

The reasons for this gulf in performance are easy to understand. Collecting vast libraries of data on customers allows companies to segment their markets on the basis of evidence, not guesswork. Understanding a buyer persona in real time gives companies targeted marketing options they never had before because they know a prospect’s history and what it is that they are likely to respond to positively.

Likewise, big data is bringing down the cost of marketing by enabling companies to focus only on the most promising leads. Rather than spamming the internet, hoping advertising messages will stick, companies are increasingly able to seek out the very people who are most likely to buy and funnel their marketing dollars towards them.

Challenges Of Big Data

Though initial results are promising, migrating a company away from traditional data analytics to predictive analytics is a challenge. Companies need to get comfortable using new methods, such as predictive regression analysis and data mining. And they need to be able to integrate data about individual prospects from multiple streams - something which is still difficult to do in many instances.