Predictive Analytics: All You Need to Know
Predictive analytics is a combination of Business Intelligence (BI) and Big Data, the two dominating concepts in today’s technology realm. Predictive analytics is often the only answer to two fundamental questions: What should a business do when it has amassed humungous volumes of data? How can it extract valuable insights from such data to improve its operations?
Gather More and More
Organizations today are using business applications that gather mountains of market, social interaction, new customer, cloud, real-time app, product performance, and other kinds of data. If you are looking for a way to leverage from all that information, predictive analytics is the right tool to gain new insights, use them to make adjustments to existing plans, and get a competitive edge.
Before you delve into the importance of predictive analytics and where it is making a big impact, let’s have a look at what predictive analytics is all about.
What is Predictive Analytics?
Predictive analytics refers to using data, machine learning, and statistical algorithms to precisely identify and forecast future outcomes. It involves extracting data from new and existing data sets in order to assess customer patterns and trends. The goal is to carefully analyze the historical data and come up with the best assessment of what is likely to happen in the near feature. While it would be an overstatement to call predictive statement an absolute science, it does provide big and small organizations with the ability to accurately and reliably predict future behaviors and trends.
What Makes Predictive Analytics So Important?
Predictive analytics can be used in a variety of ways, from data mining and predictive marketing to applying artificial intelligence and machine learning algorithms to streamline business processes and learn about new and changing statistical patterns. Predictive analytics can help with a number of important aspects in the business landscape, including, but not limited to:
• Streamlining Marketing Campaigns: With predictive analytics, companies can determine customer purchases or responses and optimize their marketing campaigns accordingly to increase their impact. It also works well for promoting cross-sell opportunities. A predictive model can help a business to attract, grow, and retain their most loyal and profitable customers.
• Detecting Fraud: By combining different analytical tools and techniques for predictive analytics, companies can drastically improve pattern detection, allowing them to prevent or catch criminal behavior. Cyber fraud is a growing concern in this digital age – that’s why behavioral analytics should be used for scrutinizing all actions taking place in real-time on a network to detect unusual activities and abnormalities that may lead to zero-day vulnerabilities, fraud, and advanced threats, like ransomware.
• Improving Operations: Another important use of predictive analysis is to effectively manage resources and inventory by forecasting demand. Hotels use predictive analytics to determine the number of guests in different seasons to optimize occupancy and increase profit. Similarly, airlines use predictive analytics to study consumer trends and set ticket prices accordingly. By using predictive analytics in the right way, organizations can make their operations significantly more efficient.
Common Applications of Predictive Analytics
1. Collection Analytics: With the help of predictive analytics, companies can dramatically improve collection resource allocation by recognizing the effective legal actions for enhancing the recovery, contact strategies, collection agencies, and cost reduction tactics.
2. Healthcare: One of the biggest leaps in the medical science is the application of predictive analytics in determining the development of certain diseases in patients. Doctors have successfully used predictive analytics for asthma, diabetes, heart diseases, and other chronic illnesses.
3. Customer Relationship Management: Through predictive analytics methods, businesses can effectively achieve their CRM goals, such as customer services, sales, and marketing campaigns. Such methods can be applied on the entire customer lifecycle, from acquisition and relationship growth to customer retention.
4. Risk Management: Companies can use predictive analytics to make predictions about maximizing returns in different capital asset pricing models while analyzing the risk for accurate forecasting of financial investment moves.
5. Cross-Selling: Businesses can examine customers’ usage, spending, shopping, and other behaviors, allowing them to improve cross sales, as well as coming up with ways to sell additional products to existing customers.
With volumes of data increasing at an unprecedented pace, predictive analytics can provide organizations and stakeholders with the right insights and direction to achieve their goals and reach their destination. By having a clear idea of what is going to happen, companies can steer their efforts in responding appropriately to the changes in the safest, profitable, accurate, and efficient way.