According to American satirist Mark Twain, “Prophesy is a good line of business, but it is full of risks.” At RevelX, we take this maxim to heart. We look into the future, but try to do it based on cold, hard data.

The time is over that a mere gut feeling is enough to make a business decision. A more modern way to create a forecast of the future is with predictive analytics. This technique uses data, predictive models, mathematical algorithms, and machine learning to discover patterns. Predictive analytics can offer companies many advantages. Below I describe 5 great applications, each illustrated with an instructive case study.

Predictive analytics solutions can help you find out when a customer is in the mood to buy a certain product. You can send an effective message exactly at the right moment during the customer journey. In other words, you’re at the right place at the right time.

Case Study: Amazon

Amazon.com stores the search behavior of customers on its website and uses data to create a tailor-made offer via email.

According to a case study by Juvlon, there are 2 types of recommendation emails that Amazon sends out. The first is generated when a customer has viewed a particular product multiple times but without making a purchase. The second one is a list of products that are related to their browsing patterns.

As an avid Amazon customer myself, I know how relevant these emails are. Many times, they are the last push to make me buy something I had considered before. These are products I really would like, so everybody wins.

Products themselves can also be developed or iterated on the basis of customer data. Historical data can be used to predict consumers’ future behavior: will they like this new product, or not?

Case study: Netflix

Executives at Netflix knew director David Fincher’s TV series House of Cards would be a hit. Of course, the viewing public’s preferences are fickle, so in showbiz, nothing is really certain. But Netflix had dived deep into its data and, using this, bought a show their audience would love.

In a great piece for The New York Times, journalist David Carr wrote:

“Netflix, which has 27 million subscribers in the nation and 33 million worldwide, ran the numbers. It already knew that a healthy share had streamed the work of Mr. Fincher, the director of The Social Network, from beginning to end. And films featuring Mr. Spacey had always done well, as had the British version of House of Cards. With those three circles of interest, Netflix was able to find a Venn diagram intersection that suggested that buying the series would be a very good bet on original programming.”

Carr also spoke with Rick Smolan, writer of the book The Human Face of Big Data. Smolan had the following insight to offer: “Netflix and Amazon know when you stop and start a program, whether you wanted the whole thing, all of that. Programmers have been wandering out and shooting a shotgun into the night sky and hoping they hit something, and I end up paying $150 for channels full of nothing I want to watch. These guys know what they are aiming at.”

Predictive analytics can show which customers will be the most valuable in the coming years. This is very important, because roughly 20% of customers are responsible for 80% of a business’ profit. Likewise, a predictive solution can tell you which customers might expire.

Case Study: American Express

The Australian arm of American Express looked in its customer data for indicators that could predict loyalty. Subsequently, advanced predictive models were developed to predict customer expiration. AmEx believes it can identify 24% of Australian accounts that will close within the next four months. The goal is, of course, to take action in time.

On the basis of historical and real-time data, algorithms can help with the efficient execution of tasks. This will result in lower costs.

It’s often the same data streams that can be used for predictive maintenance: you will receive a notification when maintenance on a device is required. This prevents downtime, which means lower costs, too.

Case Study: Imbema Group

The Imbema Group is a group of technical trading companies in the Netherlands, Belgium, and Germany. One of their products is the Flamingo app. This solution identifies and validates vital hose connections that are used in port operations. This results in a more efficient process. But that’s not all. The collected data also predicts when a hose has to be replaced.

Predictive analytics are also useful in the field of human resources planning. By combining various data (sick reports, work assessments, and employee satisfaction), staffing could be optimized. A smart HR solution is also able to predict who is planning to resign. The goal in both cases is to look for a replacement in time.

Of course, the privacy of employees has to be respected, so a prudent policy has to be drawn up before these plans can be carried out.

Case Study: Xerox

Global corporation Xerox invests thousands of dollars in the training of each new call center employee. To make that investment worth it, the company wished to cut down the turnover rate at its 48,500 call center jobs.

They assessed their hires and combined that with information about their background and personality types. The surprising outcome? Creative types stayed longer at the company than analytical types.

Since this research had been done, Xerox began targeting employees that were more creative as opposed to inquisitive. This was ascertained through personality tests. The turnover rate within the Xerox call centers decreased by 20 percent.

Are these the kind of results you would like to achieve, too? At RevelX, we develop and implement winning growth strategies that are insightful, innovative, and practical. Do you want to grow your business?