SAS Behavioral Analytics is the use of data in statistical algorithms and machine learning techniques to identify the likelihood of future outcomes based on historical data.
The main goal is to go beyond knowing what has happened to providing a best assessment of what will happen in the future.
SAS Behavioral Analytics History & Current Advances
Though Behavioral analytics has been around for decades, it’s a technology whose time has come. More and more organizations are turning to Behavioral Analytics to increase their bottom line and competitive advantage. Why now?
- Higher volumes and types of data, and much more interest in using data to produce valuable insights.
- Faster, cheaper computers.
- Easier-to-use software.
- Tougher conditions and a need for competitive differentiation.
With interactive and easy-to-use software becoming more prevalent, Behavioral Analytics is no longer just the domain of statisticians and mathematicians. Business analysts and line-of-business experts are using these type of technologies as well.
Why is Predictive Analytics Important?
Organizations are turning to Behavioral Analytics to help solve difficult problems and uncover new opportunities. Common uses include:
Detecting Fraud: By combining multiple analytics methods you can improve pattern detection and it’ll prevent criminal behavior. As cybersecurity becomes a concern, high-performance behavioral analytics examines every actions on a network in real time to spot abnormalities that may indicate fraud, zero-day vulnerabilities and advanced persistent threats.
Optimizing Marketing Campaigns: Behavioral Analytics are used to determine customer responses or purchases, as well as promote cross-sell opportunities. The Behavioral models assist businesses entice, retain and grow their most profitable customers.
Improving Operations: Now a days many companies use predictive models to forecast inventory and manage resources. Airlines use predictive analytics to set ticket prices. Hotels are trying to predict the number of guests for any given night to maximize occupancy and increase revenue. Predictive analytics enables organizations to function more efficiently.
Reducing Risk: Credit scores are used to assess a buyers likelihood of default for purchases and are a well-known example of predictive analytics. A credit score is a number generated by a predictive model that incorporates all data relevant to a persons creditworthiness. Other risk-related uses include insurance claims and collections.
Any industry can utilize SAS Behavioral Analytics to reduce risks, optimize operations and increase revenue. Here are a few examples.
Banking & Financial Services
- The financial industry, with high amounts of data and money at stake, has long embraced predictive analytics to detect and reduce fraud, measure credit risk, maximize cross-sell/up-sell opportunities and retain valuable customers.
- Banks utilizes analytics to predict the likelihood of fraud activity for any given transaction before it is authorized – within 40 milliseconds of the transaction initiation.
SAS Behavioral Analytics for Retail
- Since the now infamous study that showed men who buy diapers often buy beer at the same time, retailers everywhere are using SAS Behavioral Analytics to determine which products to stock.
- Staples will analyzes consumer behavior to provide a complete view of their customers, and realized a 137 percent ROI.
Oil, Gas & Utilities
Whether it is predicting future resource needs and equipment failures, mitigating safety and reliability risks, or improving overall performance, the energy industry has embraced predictive analytics with vigor.
Governments & the Public Sector
- Governments have been main players in the advancements of computer technologies. The SAS has been analyzing data to understand population trends for decades.
- Governments now use behavioral analytics like many other industries – to improve performance and service; detect and prevent fraud; and better understand consumer behavior. They also use predictive analytics to enhance cybersecurity.
- In claims fraud, the health insurance industry is taking more steps to identify patients most at risk of chronic disease and find what interventions are best.
- Express Scripts, a large pharmacy benefits company, uses analytics to identify those not adhering to prescribed treatments, resulting in a savings of $1,500 to $9,000 per patient.
For manufacturers it’s very important to identify factors leading to reduced production and quality failures, as well as to optimize parts, service resources and distribution.
Locus IT has worked with many industries to develop, implement, and improve on their Analytics solution and helped them overcome their challenges using SAS products. We at Locus IT provide SAS Behavior Analytics security as well as SAS Behavior Analytics staffing, SAS Behavior Analytics support, and SAS Behavior Analytics training as well. For more details please contact us.