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Top 5 Industry Use Cases of Predictive Analytics

Top 5 Industry Use Cases of Predictive Analytics

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.

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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 fromxmpro.com/10-predictive-analytics-use-cases-by-industry

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Predictive Analytics: The Key to Enhance the Process of Debt Collection

Predictive Analytics: The Key to Enhance the Process of Debt Collection

A wide array of industries has already engaged in some kind of predictive analytics – numerical analysis of debt collection is relatively a recent addition. Financial analysts are now found harnessing the power of predictive analytics to cull better results out for their clients, and measure the effectiveness of their strategies and collections.

Let’s see how predictive analytics is used in debt collection process:

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Understanding Client Scoring (Risk Assessment)

Since the late 1980’s, FICO score is regarded as the golden standard for determining creditworthiness and loan application. But, however, machine learning, particularly predictive analytics can replace it, and develop an encompassing portrait of a client, taking into effect more than his mere credit history and present debts. It can also include his social media feeds and spending trajectory.

Evaluating Payment Patterns

The survival models evaluate each client’s probability of becoming a potential loss. If the account shows a continuous downward trend, then it should be regarded soon as a potential risk. Predictive analytics can help identify spending patterns, indicating the struggles of each client. A system can be developed which self-triggers whenever any unwanted pattern transpires. It could ask the client if they need any help or if they are going through a financial distress, so that it can help before the situation turns beyond repairs.

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Cash Flow Predictions

Businesses are keen to know about future cash flows – what they can expect! Financial institutions are no different. Predictive analytics helps in making more appropriate predictions, especially when it comes to receivables.

Debt collector’s business models are subject to the ability to forecast the success of collection operations, and ascertaining results at the end of each month, before the billing cycle initiates. As a result, the workforce of the company is able to shift their focus from the potential payers to those who would not be able to meet their obligations. This shift in focus helps!

Better Client Relationship

Predictive analytics weave wonders; not only it has the ability to point which clients are the highest risks for your company, but also predict the best time to contact them to reap maximum results. What you need to do is just visit the logs of past conversations.

Challenges

Last, but not the least, all big data models face a common challenge – data cleaning. As it’s a process of wastage in and out, before starting with prediction, company should deal with this problem at first to construct a pipeline, for feeding in the data, clean it and use it for neural network training.

In a concluding statement, predictive analytics is the best bet for debt and revenue collection – it boosts conversion rates at the right time with the right people. If you want to study more about predictive analytics, and its varying uses in different segments of industry, enroll in R Predictive Modelling Certification training at DexLab Analytics. They provide superior knowledge-intensive training to interested individuals with added benefit of placement assistance. For more, visit their website.

 

The blog has been sourced fromdataconomy.com/2018/09/improving-debt-collection-with-predictive-models

 

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