What is the purpose of predictive data analytics?
Predictive Data analytics as the name suggests enables one to predict any event on the basis of past data about the event using statistical data modeling to be fairly accurate in finding patterns about the data.
Let’s say you own a company, to increase revenues and have a better market positioning for your product, what would your approach be? Would you like to predict and formulate ways through which you could acquire more clients, or would you prefer to predict which existing customers would attrite and if given a chance what could you surely do to retain them?
What if you had access to the best marketing strategy accurately targeting the potential audience, or if you knew the most accurate prediction in cross-selling or upselling opportunity with your customers, what if your company could predict almost with certainty what each existing customer wants and you could device plans to address those needs. Sounds exciting?
Most businesses are customer-centric in nature, directly or indirectly, and this kind of information could be of colossal advantage for the growth and sustenance of most organizations. Currently, we have entered the most established phase of the empowered customer, where to exist in the long run, most organizations are ferociously finding ways to connect with the customer. Organizations acknowledge that to sustain and grow they need to have more evolved interactions with their customers, and that is easy when they have a strong knowledge base with the customers. This is Predictive Analytics for you.
Predictive analytics is viewed by most people as a branch of big data, although it is partially true, it is vast enough to be considered as a separate phenomenon.
Predictive analysis allows a more proactive approach, with foresight on anticipating and predicting outcomes and behaviors, based upon data and not only assumptions. What’s exciting is that it not only predicts, but also suggests actions derived from the findings, and provide decision options.
Over a period of time, after cleaning the volume and variety of data, a project is designed and Data Mining for Predictive Analytics prepares data from various sources on which Reporting Analysis is done, which gives insights on what happened and why did it happen.
The next stage is Monitoring, getting a real-time understanding of what is happening now.
Predictive Analytics, where Statistical Analysis is performed in getting insights into the future, keeping in line with the hypothesis.
Predictive Modelling Deployment provides possibilities of what could happen derived from historical facts and behaviors. Taking into account an action plan is suggested to get the desired output.
Model Monitoring ensures that the models are managed and monitored to review the performance and guarantee that it provides decisions as expected.
Predictive Analytics and its application have a great scope across a variety of industries
Customer-Centric Businesses apply predictive analytics to achieve objectives in Marketing campaigns, sales, and across the customer lifecycle, from acquisition to retention.
Health Care applies predictive analysis to gauge the risk factors of patients before they develop certain conditions.
Fraud Detection applies predictive analysis to predict inaccurate credit applications.
Marketing applies predictive analytics to identify the most effective combination of product versions, marketing material, communication channel and timing that can be used to identify and target a given customer.
There is a lot more to the world of predictive analytics, it is no longer in the nascent stage and is fast growing. To acquire a basic understanding of the functionalities of predictive analysis would be a very wise thing to do.
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