Everything that happens in the digital world can be recorded and converted into data. Whether you’re visiting a website, buying something online or tweeting about something, it’s all data. Not surprisingly, with the sheer volume of online activity, brands are flooded with data.

The levels of brand and customer interaction are ever increasing especially with customers active on various communication channels. Brands are building their online presence and interacting with customers on multiple platforms resulting in cross channel marketing.

In order to keep their eyes and ears open on all relevant platforms, brands are employing social media analytical tools that are also bringing in increasing amounts of customer data. In order to ensure proper utilization of all this data, along with the current need for personalization, you also need to consider implementing a system that will assist in understanding future customer behavior.

While brands are dealing with incredibly large amounts of data, the good news is that this data can be used to powerfully direct marketing activities with a high degree of effectiveness.

Enter predictive analytics.

Predictive analytics is a form of advanced analytics studies current data, detects patterns and predicts possible future trends.

It helps you make the best use of the behavioral data available by predicting what customers or prospects will do and the best option you can provide them. For example, if data suggests that whenever a particular baseball team loses, sales of beer goes up amongst its fans, the brand can make special offers as soon it knows that the team is doing badly. The brand can make its offer depending on what it wants to achieve – it can ask consumers to try a new variety of beer or try and get consumers to switch brands etc.

Predictive analytics helps bridge the gap between what you think your customers want and what they actually want. Their current behavior patterns show where they are headed so you can offer your brand when they are primed and ready.

Predictive analytics points out the instances that are most likely to convert into actual sales in the near future. For example, if data indicates that 1 year after buying a smart phone, customers buy a tablet, then it pays to be ready to be the first brand to offer a tablet a year after the customer has bought a smart phone.

Instances of Predictive Analytics Application

Predictive analytics can be used in many ways. If data shows that men increase their investment in life insurance after they turn 30, it’s a good idea to target customers with life insurance as soon as they celebrate their 30th birthday.

Predictive analytics also helps you predict what products customers would like bundled together. Life insurance and health care for example or even Deep Purple and Pink Floyd albums.

In online targeting, predictive analytics can help identify which prospect is most likely to react to which ad and which particular offer. An important benefit of predictive analytics is that it helps identify which customers are likely to churn and what’s the best way to stop them.

If your company is a large corporation with a huge database of customers, it is tough to manually identify opportunities for cross selling and up selling. Predictive analytics can help by tracking customer behavior when they show interest about other products/services, studying their behavior patterns and predicting future conversions.

However, you need to be mindful of the fact that when you draw data from social media, you will end up with thousands or more mentions. To know the ones that are relevant, you need to build a relevant search terms and keywords database that ensure you focus only on the mentions that are important.

Have you implemented predictive analytics? What is your opinion about it?