Predictive Analytics
Predictive Analytics
Uses historical data to predict future events. Typically, historical data is used to build a statistic model that captures important trends. That predictive model is then used on current data to predict what will happen next or to suggest actions to take for optimal outcomes.
Banks
Predictive analytics can help identify potential fraud by analyzing the most common operational patterns regarding trades, purchases, and payments. This works with both structured data (transactions) and unstructured data (emails, reviews, forum entries) to uncover hidden patterns.
Insurance
Predictive Analytics allows insurers to convert data into valuable insights on customers, agents and markets. It plays a critical role in guiding effective, fact-based strategies. When it comes to insurance distribution, the use of predictive analytics is still evolving.
Telecom
Telecom Data Analytics Allows Better Use of Investments With the easy-to-use data analysis that comes from a telecom data analytics solution, a company can make decisions based on data, not guesswork. The role of data analytics in telecom is to give each company a unified view of their data across departmental lines.
The problems is solved by Predictive Analytics
Diminish Challenges in Customer Service
The analytics software can be used to offer better customer services and deepen their relationships. Analytics can play a vital role in decreasing and eliminating customer problems before they occur.
Improving Operations
Many companies use predictive models to forecast inventory and manage resources. Airlines use predictive analytics to set ticket prices. Hotels try to predict the number of guests for any given night to maximize occupancy and increase revenue. Predictive analytics enables organizations to function more efficiently.
Making Sense of Unused Business Data
With the reducing cost of cloud storage, enterprises today are accumulating more data. There is no data collection, but the reality is that enterprises only use about 1% of their stored data to make valuable business decisions. It is because they cannot find the appropriate data. The competency to search and retrieve data is the most vital action for enhancing business and realizing the power of big data. In addition, the search technology should be fast, and contextual to read complex information so it is usable for employees across the entire levels of an organization. This is where the analytical solution steps in. It streamlines data to be accessible and consumable to everyone.
Reducing Risk
Credit scores are used to assess a buyer’s 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 person’s creditworthiness. Other risk-related uses include insurance claims and collections.
Predictive Analytics Features
A Clear Business Objective
The first priority of any predictive analytics project is a clear understanding of the business objective being supported. Predictive analytics has been applied to customer/prospect identification, attrition/retention projections, fraud detection, and credit/default estimates.
Defined Performance Metrics
A critical factor for successful development of predictive analytics projects is a well-defined set of business performance metrics specific to the organization's business objectives.
Sufficient Data
Predictive analytics projects commonly make use of an approach referred to as "supervised learning." Developers utilize a set of historical data to complete their analysis. Each record in this data set consists of attributes of the individuals under analysis, and a 'desired output' attribute that corresponds to the behavior supporting our business objective.
Without sufficient data, it is impossible for us to make use of the techniques in predictive analytics. However, there is often a misconception about the volume of data required. For many business opportunities, as little as a few thousand records can be utilized to develop quantitative models to significantly enhance business performance.