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Why Businesses Need to Adopt Predictive Analytics?

By CIOReview | Wednesday, September 28, 2016

Future holds an unflinching intrigue and fascination for everyone, from ordinary citizens to large business enterprises. The continuous proliferation of data is driving businesses to place data at the core of their business strategies. Businesses are trying their best to stay ahead of the pack by using predictive analytics and exploit various growth opportunities.

Challenges abound as enterprises tend to make sense of this astronomical amount of data. This is where Predictive analytics comes into the picture. Predictive analytics literally means the process of making sense of the large amount of data, developing a model based on the gathered information from the data and predicting the probability of certain outcomes in the future. In this way it helps you to be ready for the future—whether it’s catering to the target audience or getting prepared for some misfortune or downturn in the near future.

A number of predictive modeling techniques are available in the market such as artificial intelligence, machine learning, and statistics to develop a relevant predictive model to suit the needs of an enterprise. Through testing, validation, and evaluation, businesses select the right model apropos to their business needs. Each model has its own strengths and weaknesses and they cater to specific needs. Integrated predictive analytics to understand the drivers of demand and tactical course correction is the way forward and this is where the future of predictive analytics lies.

The modeling techniques are playing pivotal role in building innovative applications across the industry verticals. Some of the use cases are discussed below:

Health Care: Predictive analytics helps in predicting the patterns in patients with chronic illnesses such as asthma, diabetes and other such diseases. It also helps in taking critical decisions at the time of care.

Customer Relationship Management (CRM): Predictive analytics is used extensively in the marketing arena. Validated predictive rules and models and efficient event processing platforms can be used for enterprise applications such as Customer Relationship Management (CRM) and Enterprise Resource Planning (ERP) for getting a glimpse of the future. CRM can be applied throughout the customer lifecycle—from the initial acquisition to the future growth and retention of customers.

Detection of Fraud and Other Malpractices: Predictive analytics can be used in detecting fraudulent practices and can be a great asset in the insurance sector and other financial transactions.

Marketing: Marketing is another area where predictive analytics is being used to good effect. It helps in determining the nature and preferences of the customers and their behavior based on the data and the previous history. This helps in determining the future course of action and the ways in which one can retain the customers and increase productivity. Predictive analytics can help a great deal in increasing ROI through targeted marketing campaigns, and intelligent management, leading to an increase in revenue and profit.

Countering Risks: Predictive analytics can also play pivotal role in charting out roadmap for the organization. By successfully predicting future outcomes, it helps organizations to reduce risks.

B2B companies deploying predictive analytics for their use are doing well compared to their competitors and are well on their way to understanding their customers and their needs better by personalizing the content to suit their needs. With a well-informed use of predictive analytics, companies are well placed to understand the market forces better and mould them to their advantage in today’s changing world. Getting to predict the future outcomes can be of help to organizations and deploying successful predictive models can also help companies to cut down on risk factors and make a substantial gain.

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Machine Learning

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