Predictive analytics is an effective in-hand tool crafted for data scientists. Thanks to its quick computing and on-point forecasting abilities! Not only data scientists, but also insurance claim analysts, retail managers and healthcare professionals enjoy the perks of predictive analytics modeling – want to know how?
Below, we’ve enumerated a few real-life use cases, existing across industries, threaded with the power of data science and predictive analytics. Ask us, if you have any queries for your next data science project! Our data science courses in Delhi might be of some help.
Customer Retention
Losing customers is awful. For businesses. They have to gain new customers to make up for the loss in revenue. But, it can cost more, winning new customers is usually hailed more costly than retaining older ones.
Predictive analytics is the answer. It can prevent reduction in the customer base. How? By foretelling you the signs of customer dissatisfaction and identifying the customers that are most likely to leave. In this way, you would know how to keep your customers satisfied and content, and control revenue slip offs.
Customer Lifetime Value
Marketing a product is the crux of the matter. Identifying customers willing to spend a large part of their money, consistently for a long period of time is difficult to find. But once cracked, it helps companies optimize their marketing efforts and enhance their customer lifetime value.
Quality Control
Quality Control is significant. Over time, shoddy quality control measures will affect customer satisfaction ratio, purchasing behavior, thus impacting revenue generation and market share.
Further, low quality control results in more customer support expenses, repairs and warranty challenges and less systematic manufacturing. Predictive analytics help provide insights on potential quality issues, before they turn into crucial company growth hindrances.
Risk Modeling
Risk can originate from a plethora of source, and it can be any form. Predictive analytics can address critical aspects of risk – it collects a huge number of data points from many organizations and sort through them to determine the potential areas of concern.
What’s more, the trends in the data hint towards unfavorable circumstances that might impact businesses and bottom line in an adverse way. A concoction of these analytics and a sound risk management approach is what companies truly need to quantify the risk challenges and devise a perfect course of action that’s indeed the need of the hour.
Sentiment Analysis
It’s impossible to be everywhere, especially when being online. Similarly, it’s very difficult to oversee everything that’s said about your company.
Nevertheless, if you amalgamate web search and a few crawling tools with customer feedback and posts, you’d be able to develop analytics that’d present you an overview of the organization’s reputation along with its key market demographics and more. Recommendation system helps!
All hail Predictive Analytics! Now, maneuver beyond fuss-free reactive operations and let predictive analytics help you plan for a successful future, evaluating newer areas of business scopes and capabilities.
Interested in data science certification? Look up to the experts at DexLab Analytics.
The blog has been sourced from ― xmpro.com/10-predictive-analytics-use-cases-by-industry
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