The global economy is driven by data. Every sector and every function of the global marketplace is driven by our ability to collect, store, transmit and analyze massive amounts of data. In today’s competitive environment, businesses are not only collecting data, but using it to expand their market intelligence and improve their decision-making. The field of big data analytics – the practice of extracting information from structured and unstructured data sets in order to gain meaningful insights and improve business decisions – is enabling this paradigm shift to take place by helping businesses predict future outcomes.
An organization’s ability to use existing data to formulate patterns and predictions about future trends is called predictive analytics, which encompasses a wide variety of data gathering and statistical analysis techniques. According to analysts and observers of the digital economy, predictive analytics is the new competitive advantage that can help businesses leapfrog others to the top of their industry.
There are a number of reasons why more organizations are turning to big data and predictive analytics. The three main reasons include:
- Growing volume of data being generated in today’s economy
- Faster, cheaper and more accessible technologies
- A more competitive economic environment forcing companies to adopt innovative techniques to boost performance
5 practical uses for predictive analytics
Businesses looking to learn more about their market, improve operational efficiencies and increase sales should consider adopting predictive analytics. Although there are many ways predictive analytics can be put to use, the following five applications can yield the most immediate impact for businesses.
1. Customer relationship management
There are many analytical CRM applications in use today that help businesses develop a holistic view of their customer no matter where that information resides. By integrating and streamlining customer data in a single system, businesses can focus their efforts on improving their relationship with their customers through improved communication, special offers and regular engagement. Whatever your purpose, predictive analytics can create more effective customer relationship management.
2. Marketing and sales
Marketers are among the biggest users of predictive analytics. By collecting and analyzing customer data, marketing teams can determine customer responses on purchases and optimize cross-selling and upselling opportunities. In this way, predictive models help businesses attract and retain profitable customers.
3. Price optimization
Through predictive analytics, businesses can determine the direct relationship between demand and price for any product or service. Analytical pricing and revenue management has been used in a variety of industries including e-commerce, consumer goods, hospitality and air travel. Predictive analytics is one of the most effective ways to model product pricing and revenue strategies.
4. Fraud detection
Retailers, banks, insurance providers, credit card companies and many others use predictive analytics to improve cyber security and reduce fraud. By applying tools like behavioral analytics, predictable program eligibility and multiple detection methods, businesses can save millions of dollars detecting fraud before it affects the bottom line. Statistical methods can also be used after-the-fact to improve chances of recovery in the event that theft or fraud actually occurs.
5. Operations management
In today’s competitive environment, businesses are under greater pressure to cut down costs and improve efficiency. Many organizations are using predictive analytics tools to forecast inventory, manage resources and determine service capacity. Hotels use predictive analytics every day to predict the number of guests they can expect to receive. Airlines use predictive models to determine how many tickets to sell at certain prices. Manufacturers use predictive analytics to adjust production levels and improve yield.
The concept of predictive analytics is easy: use data to improve decision-making more quickly and affordably. Regardless of their industry, organizations can use predictive analytics to improve their functions and increase their competitiveness. If you have a problem to solve or are uncertain about how a certain product or service will perform, predictive models can help you put the pieces together.
 SAS. “What is predictive analytics?”
 CGI. Predictive analytics: The rise and value of predictive analytics in enterprise decision making.
 SAS. “What is predictive analytics?”
 Spotfire (May 18, 2015). “Transforming Manufacturing with Predictive Analytics.