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Data Science: The Key To Streamlining Customer Engagement For Financial Institutions


Oct 2019
2817 Views 4 Minute Read
data sciencedata science: the key to streamlining customer engagement for financial institutions
In today’s digitally-connected world, every company strives to gain the attention of their customers. As a result, customers have become more demanding and less loyal. If any company fails to cater to what customers want, customers waste no time switching to an alternative that can meet their expectations.
Therefore, predicting what your customers want is the key to customer satisfaction and higher retention rates. This is especially true for financial institutions (i.e., banks) since customer retention is an essential part of their business model. The only way you can retain customers is by understanding their behavior, and that is where data science comes into the picture.
Data science is a vast field where experts extract useful insights with the help of data. Although it is a multidisciplinary field and has various applications, financial institutions can apply data science to predict what customers want and deduce ways to increase engagement.
In this article, we discuss how financial institutions can leverage data science for streamlining their customer engagement levels.
Meet Expectations and Collect Feedback
EY’s Global Consumer Banking Survey in 2014  makes it clear how much customers value experience. According to the survey, as much as 40% of banking customers are willing to pay more if the bank can provide them with simpler experiences and interactions.
With alternatives such as non-bank providers and digital banks, financial institutions need to focus on how to improve customer experience. However, before making any changes to customer experience, they must identify what needs to be changed, which is not a simple thing to do.
Financial institutions cannot rely on surveys alone to find weaknesses in their customer experience. One way they can do this effectively is with the help of machine learning algorithms in data science.
Mitigate Potential Challenges
Besides retaining customers, banking entities can leverage data science to mitigate challenges they face on a daily basis. They can apply machine learning and data analytics on historical data to predict operational demands.
Moreover, they can use the predictive quality of data science and analytics to take preventive action for future events. For instance, they can find out what amount of cash a specific ATM location requires on a certain time. Alternatively, they can predict spike and rush hours to optimize their internal processes.
Make Customers Feel Safe by Preventing Fraud
One of the most beneficial aspects of data science is its ability to produce precise and accurate analytical models. By utilizing predictive models, banks can easily identify and prevent people engaged in fraud.
These models can trace transaction anomalies, suspicious activities, and make real-time suggestions for mitigating fraud. Similarly, they can prevent credit defaults with specialized data collection strategies and borrowers’ segmentation.
To make the internal process even more secure, these analytical models include factors such as bank capital adequacy, stress testing, and market liquidity risks. Using real-time data, these models calculate and predict these risks, based on existing standards.
Recruit and Retain the Best Talent
To streamline customer engagement, banks must train their human resources and monitor their interactions with customers. However, in most cases, this might not be enough to satisfy your customers.
Still, you can use data science to gauge the performance of your employees and predict the optimal employee count for a branch. Some banks even use models to quantify employee retention and then work on ways to keep the best talent pool at their side.
Build Trust and Loyalty
Using data science to streamline customer experiences is vital for the success of any bank. It can go a long way in developing effective marketing and digital strategies for the financial institution.
Delivering the right customer experience can make you stand out from the rest of your competition. It gives customers the impression that the bank understands their needs and expectations. Such concern for customers encourages loyalty and trust amongst customers.
Target Niche Clients
Like other business, many clients are difficult to categorize in financial businesses. Some of these clients have a particular set of expectations from their providers, and they can be equally beneficial and destructive for the business’ image.
However, by leveraging data science, you can offer these customers personalized experiences. This can help financial businesses target their customers through tailored promotions and help them deliver the right messages at the right time. For instance, a company can filter which customers they need to target for investment schemes.
Slowly, but surely, businesses around the world are leveraging data science to offer personalized services and optimize their workflow. Financial institutions have a lot to gain if they utilize data science for greater customer satisfaction and higher retention rates.
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Harnil Oza is the CEO & Founder of Hyperlink InfoSystem. With a passion for technology and an immaculate drive for entrepreneurship, Harnil has propelled Hyperlink InfoSystem to become a global pioneer in the world of innovative IT solutions. His exceptional leadership has inspired a multiverse of tech enthusiasts and also enabled thriving business expansion. His vision has helped the company achieve widespread respect for its remarkable track record of delivering beautifully constructed mobile apps, websites, and other products using every emerging technology. Outside his duties at Hyperlink InfoSystem, Harnil has earned a reputation for his conceptual leadership and initiatives in the tech industry. He is driven to impart expertise and insights to the forthcoming cohort of tech innovators. Harnil continues to champion growth, quality, and client satisfaction by fostering innovation and collaboration.

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