Conditions in communications service provider marketplace have experienced a drastic shift in the past decade. Most businesses are now focusing on engaging with their clients in real-time, online or through contact centres. Customers have become highly tech-savvy and expect unprecedented access to recommendations and product information.
Digital technology has also made them extremely conversant with competitive products, choices and offers available in the market. Such customers even demand for a higher level of seamless client service excellence and personalization. Moreover, it gives the customers a marked prerogative of switching between different service providers for a better and efficient deal, particularly when their demands are not met. This is where the service providers need to learn to take decisions based on how the customers will behave in the near future. This can be best done with the help of predictive analytics. Predictive analytics is the key to success for any service provider and its rewards for improving accuracy are just great.
Unravelling Real Time Predictive Analytics
When a predictive model (mostly built/fitted on a set of aggregated data) is integrated to perform run-time predictions on a continuous stream of event data, it is called real time predictive analytics. This enables decision making for the clients in real-time. This entire system involves two key aspects that combine to form its backbone. The first one is the predictive model which is built by a Data Scientist via a stand-alone tool (R, SAS, SPSS, etc.) and is further exported in a consumable format (PMML).
Second, a streaming operational analytics platform that is responsible for consuming the model (PMML), and further translating it into the necessary predictive functions. The deployment of such a complex predictive model, from the key machine learning environment to an operational analytics environment makes for a continuous run-time prediction in real-time.
Most of the predictive analytics systems make use of traditional statistical techniques which are no doubt outdated in the current market scenario. Nevertheless, such systems promise excellent results when synced with the latest computational intelligence techniques. Besides, such systems overcome several challenges and barriers by allowing the businesses to get instant results from cutting edge predictive analytics positioned live in real-time.
Stay Ahead in the Niche with Real Time Predictive Analytics
Business can now consume all the available and relevant data sources, and further define the dynamic micro-segments through the advanced modelling capabilities of real time predictive analytics. This even suggests the best course of actions for the businesses, such as offer of early equipment up gradation, sale of an add-on feature, presentation of a retention incentive and many more.
By objecti vity