5 Applications of Predictive Analytics in Business Intelligence

Posted By : Priyansha Sinha | 29-Aug-2018

Predictive analytics in business intelligence

Predictive Analytics draws out relevant information from the data sets with an aim to decode complex relationships, forecast upcoming trends, discover unknown patterns, derive associations and so much more. This enables enterprises and government organizations to foresee future risks and take the right decisions. Altogether, the applications and usability of predictive analytics in the domain of business intelligence are uncountable and encompasses infinite potential. So here are a few applications that would basically give a brief and closer insight to the prowess of predictive analytics.


Customer Segmentation


Customer segmentation, as the name suggests, is a practice of classifying a customer base into a group of users that are nearly similar in various ways pertaining to the marketing relevance such as gender, age, spending habits, and area of interests. It allows the companies and organizations to precisely target marketing messages to the users who are most likely to buy any of their service products. Furthermore, it has also been proven that predictive analytics has a much better capability of identifying potential customers as compared to the traditional methods.


Risk Assessment


Risk assessment enables users to scrutinize and figure out the possible hurdles linked to any given business. The sole objective of data mining in this scenario is to devise decision support systems that can meticulously foretell the most profitable operations for a company and blocklist all the actions that are of no good.


Different data sets are prepared in order to determine the segment of users for the proper handling of any future products and it is then figured out if they would be taken in successfully by the masses or simply fall under the high-risk category.


Forecasting Sales


The cornerstone of an enterprise’s planning is to accurately examine market-moving events, seasonality, a brief look into the users’ history etc, which results in a realistic prediction of the actual sales. Further to this, data mining can help businesses to anticipate the users’ response and varying standpoints by analyzing every possible factor associated with it. Sales forecasting can be linked to small, medium, or long-term predictions.


The thorough evaluation and assessment of this study enhance the accuracy of a forecast, which signifies better information to select what is the best plan of action.


Churn Prevention


Churn Prevention assists the businesses in the literal prediction of customers to end the relationship with, when to do so, and why exactly. This entire procedure can be very expensive since the overheads of retaining the existing customers is much less as compared to acquiring the new ones.


The companies can, however, leverage the Big customer data sets to create predictive models that facilitate proactive intervention before it gets too late.


Also Read Unlock the true potential of big data with machine learning

Financial Modelling


Generating an abstract representation of the real world financial situation is termed as financial modeling. This is basically a mathematical model built to portray a simplified version of the performance of any business project, the portfolio of companies, financial assets, or any other investment. Financial modeling means different things for different users and has been gaining acceptance all over these years.


What are your thoughts on implementing Predictive Analytics for businesses? Let us know by writing down in the comment section below.


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Priyansha Sinha

Priyansha is a Content Developer and Writer with almost 2 years of experience. Besides, she is a trained vocalist and pianist with an enormous love for photography.

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