Big Data has been the most significant idea to have infiltrated itself into every aspect of the business world over the last several years. Every company wants to say that they’re making data-driven decisions, have a data-driven culture, and use data tools that non-data people have probably never even heard of. But while all this data is of course a valuable resource, and analyzing it can bring tremendous benefits, it is effectively rendered redundant in terms of gaining a competitive edge if the predictive analytics to leverage it are not in place.
In the ‘How Predictive Marketing Analytics Boost B2B Performance’ report, commissioned by predictive analytics firm EverString and carried out by Forrester Consulting, just 36% of respondents cited velocity as the main challenge they faced with their data. Companies have, in the main, already worked out how to accumulate and store large amounts of data. And most know how to put it in reports that can be analyzed to see where they’re going wrong. The next challenge for many is the move away from such a reactive report-based method of working, and start to use it instead for making predictions that can impact the business’s bottom line months, days, or even seconds in advance.
To do this, predictive analytics has to provoke action. It cannot simply be used to make forecasts, it needs to be deployed directly into software applications and business processes so that it can be be leveraged immediately. The tools to do this are becoming more easily available, primarily thanks to advances in Cloud technology, which enable the kind of speed and scalability that such software requires.
The real secret to successful predictive analytics, however, is context. According to a recent Boxever study, context is almost as important as price in consumers’ thinking when they are making purchasing decisions, and offers have the greatest impact when they address something the customer is already doing. Data scientists are increasingly finding new ways to create machine-learning algorithms that predict in real-time, what kind of highly personalized offers will work for different customers. Machine-learning algorithms identify patterns that know which profile a particular person fits at any given time and provide an accordingly enhanced and relevant experience. Such algorithms are one of the central reasons for Amazon and Netflix’s tremendous success, with VentureBeat claiming that 35% of product sales at Amazon resulted from their recommendations engine.
One of the real difficulties with predictive analytics lies in measuring its success. An obvious way to do it is comparing your business’s position before a prediction to its position after. So, if your data shows that certain customers may be interested in buying a product at a certain time of year and you build a campaign accordingly, if sales in that product are high then it is generally a good indicator that the prediction was accurate. However, there is no comparison with another product that you offer, and it could well be that would have sold equally well - if not better - if you had built your campaign around that.
Comparisons with other firms offer a better insight, although these are harder to come by. The Forrester and EverString report found that ’Predictive Marketers are 2.9 times more likely to report revenue growth at rates higher than the industry average,’ 2.1 times more likely to ‘occupy a commanding leadership position in the product/service markets they serve’, and 1.8 times more likely to ‘consistently exceed goals when measuring the value their marketing organizations contribute to the business,’ compared to the Retrospective Marketers in the survey. This sort of success, while perhaps limited in that EverString is a predictive analytics company, should be evidence enough that basic analytics is no longer enough - predictive analytics is the future.