How Data Science Drives Growth and Improves Customer Experiences in FinServ and Retail

Businesses love data science. They use it along with analytics to understand customer behavior and aid real-time decision-making. This achieves better, more goal-oriented results. 

Businesses may also use data science to reverse negative trends. For example, retailers and financial-services companies can use data science when dealing with bankruptcy, layoffs, or imminent closures. The data may suggest the best courses of action.

Data sciences work so well, in part, because it reduces human error. Many modern businesses collaborate across time zones, with high numbers of employees, and multi-faceted operations. Those complicated structures can lead to wasted time and innumerable errors. 

Businesses that commonly face such challenges are in retail and financial services sectors—and, the complexities they face are on the rise. So is the competition. It’s all forcing organizations to use every available channel—from physical locations to online to mobile—to increase brand exposure and optimize customer experiences.

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How Businesses are Harnessing Data Science 

There are several ways businesses are putting data science and analytics into action.

1. Offering Personalized Services

A small business owner needs more customers and to keep the ones he has. He uses data science to gather information about his customers. This includes demographics, purchasing history, and behavior. Using that data, the owner provides customers with personalized promotions. The customers react well to the deals, and the owner better understands his customers’ needs. The ability to give customers exactly what they want ultimately boosts loyalty and retention.  

2. Providing Product Recommendations

 An ecommerce business needs to drive sales. They use data science to gather information about what their customers are looking for. They then place customers in different segments depending on the customers’ needs. The insights they gather lets them offer customized recommendations, which increases up-sell and cross-sell opportunities, resulting in growth.

3. Leveraging Robo-advisors.

A small business owner wants to help customers make wiser buying decisions, but she doesn’t have the time or money to build a customer service team. So she deploys a robo-advisor that utilizes algorithms based on a customer’s investment history, risk profile, and buying patterns. The owner then uses the robo-advisor to help customers make decisions by suggesting products or presenting peer-to-peer comparisons.  

4. Streamlining Customer Accounts

The marketing team inside a startup company needs to streamline the process of working with customer accounts. The director of the team deploys data science to do this. It automates customer-account tasks and identifies business opportunities. The data comes from customers’ spending and savings habits, risk profiles, and available funds. Data-driven insights into customer accounts help businesses analyze trends so that they can gain a holistic understanding.  

5. Implementing Intelligent Chatbots

A leading tech company wants a more efficient method of responding to customers’ queries. They use data science to develop AI-powered chatbots. This not only addresses customer needs but generates quality leads. Over time, the chatbot becomes more intelligent, the same way we get smarter as we gather more information. The chatbot collects data on customer behavior, and it develops more relevant replies to users’ questions. It also walks customers through processes and provides valuable purchasing insights. 

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How Data Science Enables Businesses to Manage Risks and Optimize Business Outcomes

Let’s dig into some of the scenarios in which data science enables better business outcomes.

1. Early-warning Prediction

Business worries about problems after the launch of a product. They use data science and analytics to perform a liability analysis, which tells them where and when they may encounter problems. The company then shifts its strategy to reduce risks to ensure that the product launches with minimal issues.  

2. Loan Delinquency Prediction

A financial-services company has trouble identifying and placing delinquent borrowers into their categories. They use data science to do it. The analysis helps them improve their collection policies and increase on-time payments.   

3. Identify at-risk customers

A financial company needs to spot customers who might default on debt obligations quickly. The company uses data science to develop new strategies to institute pattern-recognition methods that mitigate risk. The company then spots risky customers with greater frequency and reduces delinquencies. 

4. Recognizing Financial Crimes

A financial services company wants to limit fraud, financing of terrorist activities, and money laundering. They use geospatial, transactional, and black-list data to detect and act on suspicious transactions. In the end, they are better equipped to catch fraud, terrorism, and laundering endeavors.

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Using Data Science to Automate Business Processes

1. Automated Trading

A stockbroker wants to automate some of his work. He utilizes data science to initiate algorithmic trading. The stockbroker uses this algorithmic trading, something founded on the principles of deep learning, geographic positioning, and high-efficiency computing. It reduces the workload for the broker and lets him move faster than the competition.  

2. Instant Risk Evaluation

An account manager wants to evaluate a customer’s credit risk in real-time. She uses data science to do this. It automates decision making by using customer data, such as transaction records, employment experiences, addresses, prior communications, credit history, and age.  

3. Streamlined Complaint Management

A customer service representative wants to determine the most common complaints she receives. She employs data science, which enables data analysis from various channels. The analysis identifies the cause of the problem and lets her respond to customers faster. The number of dissatisfied customers decreases considerably. 

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What This Means for Careers in Data Science

The demand for data science professionals is rising sharply because global financial services and retail companies want to leverage it to increase efficiency, lower costs, and stay ahead of the competition. A report published by Indeed.comshows a year-over-year upsurge of 29 percent in demand for data scientists. It also shows that demand has increased 344 percent since 2013

The same report also reveals that, while the demand for data-science professionals is booming, job seekers with data science skills are only growing at 14 percent.

Experts agree that upskilling employees is the key to overcome talent shortages. The problem? Many of the available upskilling courses lack the flexibility to accommodate the schedules of busy professionals. 

That’s why Simplilearn has co-developed our Data Science Courses. It enables students to upskill from anywhere, at any time, and their own pace. 

Simplilearn’s Artificial Intelligence and Data Science Course also provides best-in-class training that makes upskilling fun and easy. It covers top programming and data visualization frameworks, such as Spark Hadoop, Python, R, and Tableau. 

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