Using Predictive Analytics and Predictive Marketing in Any Business  

Using Predictive Analytics and Predictive Marketing in Any Business  

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Predictive analytics has been the most substantial idea to have infiltrated itself into almost every facet of the business world over the last years. Every company wants to say that they’re making decisions that are data-driven, have a data-driven culture, and use data tools that non-data people have probably never even heard of. While all this data is a valuable source and analyzing it can convey incredible assistance, it is redundant in gaining what is known as the ‘competitive edge’ if the predictive analytics to control it are not in place.

History and current advancement

All though predictive analytics has been around for many decades, it is a technology whose time has come. Every day more businesses are turning to predictive analytics to help increase the bottom line and give them the competitive advantage. Why now?

Types of data

Growing and volumes and the types of data, and more interest in using data to produce insights that are valuable:

  • Cheaper and faster computers;
  • Software that is easier-to-use;
  • Tougher economic conditions and a need for competitive differentiation.

 

Easy to use software

With easy-to-use and interactive software becoming more dominant, predictive analytics is no longer only the field of statisticians and mathematicians. Business analysts and line-of-business experts are using these technologies as well.

Why is this important?

Businesses are turning to predictive marketing to help solve problems that are difficult and to find new opportunities. Common uses include:

  • Detecting fraud;
  • Optimizing marketing campaigns;
  • Improving operations;
  • Reducing risk.

What businesses are using it?

Almost any industry will make use of predictive analytics to decrease risks, improve operations and increasing revenue. Here are several examples:

  • Banking & Financial Services –

These services use predictive analytics to detect as well as decrease fraud, measure credit hazard, exploit cross-sell/up-sell occasions and hold on valued customers;

  • Retail –

Retailers are using predictive analytics to control which products to stock, how effective are promotional events as well as which offers are most appropriate for consumers;

  • Oil, Gas & Utilities –

Predicting failures of equipment and future needed resources, modifying safety and reliability risks;

  • Health Insurance –

Detecting claim fraud, identify patients at risk of chronic diseases, and what interventions or treatments are best.

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