Our everyday lives are filled with technological innovations that seem like something out of science fiction. We have computers that fit in the palm of our hand and have more processing power than the Apollo 11 computers. We have mobile phones that can access the Internet, set up appointments, pay bills, and do all of our banking. We can look up a destination, pull up a map to it, and have our mobile devices talk to us, showing us where to go.
If all of that sounds impressive, then imagine software that can collect tons of data, use it to make predictions, and then take actions independent of human interaction, using the predictions as a means of decision making.
It’s called a predictive analysis application, and we’re about to see how it’s redefining business today. These apps are something best to understand sooner rather than later, since according to “Prediction: More Predictive Analysis Apps are on the Way”, “..more industries are expecting predictive analysis apps to be released in 2015.”
Predictive Analysis Applications Defined
It’s best to start with illustrating what a predictive analysis application actually does. The application extracts information from existing data sets so that it can ascertain patterns and forecast future trends and outcomes. It doesn’t deliver an infallible prediction of the future; rather, it provides an extremely reliable forecast of the most likely outcome.
With that said, here are some areas of businesses where predictive analysis applications stand to have the most impact.
Once a business gets a customer, the trick is to hold onto them. Predictive analysis applications can forecast customer attrition, defection, and churn. By getting a good idea on what sort of factors influence customers to leave, a business can take measures to avoid committing those sins. It becomes a case of not only knowing what actions to take to foster customer loyalty, but what things to avoid.
In direct marketing, the business takes its message straight to the consumer, then deals with them directly when it comes time to make a sale. However, it’s hard to predict how people will respond to this approach. At least, it used to be hard. Predictive analysis applications can predict the most likely customer responses, and help guide a business towards which approaches would stand the best of chance of success. Maybe it means cutting back on paper fliers and catalogs and focusing instead on an SMS campaign.
Unfortunately, email has become the 21st century’s analog to paper junk mail. Only about one in five people actually open up an email in their Inbox, and even that figure is being a little generous. Then if the potential customer opens the email, it doesn’t mean they’ll actually do anything about it. By using analytics applications, a business can get a good idea as to what kind of email text a customer will respond to.
Predictive analysis applications can even help in the area of debt risk assessment. While no institution wants to extend credit to a high-risk borrower, it also doesn’t make sense to torpedo a sale due to an unnecessary denial of credit; after all, sometimes the raw credit score doesn’t tell the whole story. By using an analysis app, a lender can get a good idea of the likelihood of repayment, based on collected data on the applicant’s history of making payments, how much those payments are for, and their timeliness.
When you’re involved with raising money for a cause or charity, every dollar counts, and it’s a bonus if the overhead is kept low. This means it’s in the best interest for a non-profit to make sure that it is targeting the right people and have the highest likelihood of positive responses, and not waste time, effort and yes, money on segments of the population that are traditionally unresponsive. Using data from past campaigns, analytics apps can forecast where the greatest responses will come from, and even what the range of the donation amounts will look like.
Big Data Enabler!
Finally, predictive analysis apps are ideal for use with Big Data, and the glut of information the latter provides. The analytic application not only has the capability of turning that massive amount of information into something useful, it actually thrives on the large volume of information. After all, the more information that the app has at its disposal, the more informed a forecast, which means a greater likelihood of accuracy.
By employing these analytics, businesses these days can analyze customer buying behavior in order to decide what promotions to launch or what coupons to produce. Things can get to the point where the application takes historical data, formulates predictions, and then uses those forecasts to automatically generate the appropriate messages and mass mailings to the public, all without anyone having to do anything.
At the end of the day, it’s clear that predictive analysis applications offer a cost-effective, efficient manner for businesses to have the greatest chance of success in their marketing campaigns.