How Customer Analytics in Banking Can Help Banks to Improve Customer Acquisition Rate?

Author: Associate Vice President, Analytics and Data Strategy, Quantzig.

The shift from product-centricity to customer-centricity in the banking sector is almost tangible, however, the challenge still lies in engaging with the customer effectively and efficiently right when they sign up or even before they decide to. The focus in the banking industry for the last few years has been client life-cycle management.

When it comes to having actionable insights into their customers, banks are ahead of the curve as compared with any other industries. The issue lies in the fact that these actionable insights are often not shared across the organization. Customer analytics in banking helps banks gain granular insights on current and upcoming requirements of their customer base. The client, an American financial services provider company, approached Quantzig to leverage its expertise of customer analytics in banking. Quantzig’s customer service analytics experts delivered on the client’s expectations and helped them improve their customer lifecycle management.

Case Study: Leveraging Customer Analytics to Combat Fraud and Enhance Customer Acquisition in the Banking Sector

Case Study Summary

Tabs	Details
Client	A major financial and banking services provider headquartered in California with branches across the United States.
Challenges Faced	Fraud Detection: Faced fraud activities close to a million USD in 2018. Customer Segregation: Needed to segment customers by age, gender, behavior, and interest. Customer Acquisition: Faced low acquisition rates over the past year.
Solution Offered	Quantzig deployed a customer analytics framework to identify threats and created an analytics-driven dashboard to detect patterns indicating potential fraud.
Impact Delivered	Customer Acquisition Rate: Improved by 17%. Customer Retention Rates: Increased by 54%. Financial Frauds: Reduced, saving USD 2.5 million. Customer Interaction: Enhanced personalization and cross-selling opportunities.

About the Client

The client is a prominent financial and banking services provider headquartered in California, with branches spread across the United States. It is one of the largest banks globally by market capitalization and total assets.

Business Challenge

This industry giant faced significant predicaments with fraud detection. In 2018, the client experienced severe fraud incidents, leading them to seek Quantzig’s expertise in customer analytics in banking to comprehend and analyze the spending patterns and financial history of their customers. The main challenges included:

Problem Statement 1: Fraud Detection

In 2018, the client faced fraud activities amounting to nearly a million US dollars. They approached Quantzig to leverage its customer analytics solutions tailored for the banking industry to identify and mitigate fraud activities.

Problem Statement 2: Customer Segregation

The client aimed to segregate their customers by age, gender, behavior, and interest. This segregation would allow the client to analyze individual customer spending patterns and offer personalized services.

Problem Statement 3: Customer Acquisition

Customer analytics in the banking sector helps banks and financial institutions identify high-value customers likely to respond to services. The client had faced low acquisition rates over the past year and sought to deploy a customer service analytics solution to enhance their customer acquisition rates.

Solution Offered

The primary objective was to identify fraudulent activities and customers posing such threats. Massive digitization globally has heightened the need for banks to have sophisticated systems to detect and handle fraud. By utilizing customer service analytics, banks can readily identify fraudulent activities. Our experts deployed a framework for the client to identify threats and designed an analytics-driven dashboard to detect patterns indicating potential threats before they arise.

Business Outcome

To help this American banking giant maximize the value of their customer base, Quantzig’s customer analytics experts captured and analyzed the complete potential of their customer base using customer profiling and segmentation solutions. This approach enabled the client to deliver a perfect mix of solutions to their customers. The key business outcomes were:

• Improved customer acquisition rate by 17%
• Enhanced customer retention rates by 54%
• Reduced financial frauds, saving US$2.5 million
• Personalized customer interactions and enhanced cross-selling opportunities

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Best Practices for Using Customer Analytics in Banking

Customer analytics can revolutionize banking if implemented correctly. Here are some best practices for your customer analytics strategy:

Integrate Data Sources

Combine data from various sources to get a comprehensive view of your customers. In banking, common sources of data include transaction history, helpdesk transcripts, surveys, banking app or website usage data, and customer surveys.

Focus on a 360-Degree View

It’s essential to collect and analyze comprehensive customer data. For accurate and detailed insights, gather data from the full range of customer experiences and demographics, including:

• New customers to long-time customers
• Customers who purchase different products
• Quantitative customer data (transactions, login history)
• Qualitative customer data (chat transcripts, emails, chatbot conversations, surveys)
• Customers from different income brackets

Leverage Predictive Analytics

Predictive analytics can help anticipate future behavior and market trends based on historical data. Use machine learning to analyze your current data and trends to help you:

• Categorize your customers to predict customer lifetime value
• Improve upsells
• Predict the likelihood of future product or service purchases for individual customers and your customer base as a whole

One bank used predictive analytics to forecast customer loyalty, achieving a 70% accuracy rating in correct predictions for individual customers.

Create Personalized Experiences

Did you know that 66% of customers expect you to know what they need and want? This means banks need to truly understand individual customers to personalize the banking experience. This can be done through data-backed, targeted upsells or customized ads and content based on their preferences and history.

According to reports, banks are struggling with personalization. Only 28% of banks use well-rounded and consistent customer data in their AI models, and only 8% effectively utilize the resulting customer insight within the various customer segments.

With the right data and AI models, you can create more personalized experiences and test hypotheses in real-time. For example, you may hypothesize that customers aged 30-49 using your mobile banking app desire one feature, while those aged 18-29 desire another. Before investing, test this hypothesis with your current voice of the customer data to get segmented insights on what different user demographic groups want or have issues with.

Invest in Big Data Analytics Tools

Banks should invest in advanced, big data analytic tools, train their teams, and establish clear goals for their customer analytics initiatives. Effective, AI-supported customer data analysis can lead to improved ROI and an optimized customer experience.

One Italian bank reported using analytics to automate their client evaluation process, cutting evaluation time by 60%, even after analyzing more than 100 features and data points to determine their clients’ creditworthiness.

Act on Machine Learning Insights

Collecting and analyzing customer data is crucial, but utilizing the insights is where the real value lies. To leverage the power behind customer experience analytics, dedicate yourself to understanding the insights and taking action to improve them.

Risk analysis and business intelligence tools should also be leveraged to ensure that customer behavior and market trends are closely monitored, providing real-time data reports to aid in making informed decisions based on both present data and historical data.

Utilizing Customer Analytics in Banking

Financial institutions leverage customer data analytics to gain deeper insights into customers’ behavior, identifying potential risks and growth opportunities. By analyzing customer data such as credit, transaction, and investment history, banks can assess the risk associated with each lending proposal.

Determining which analytics provide the most valuable insights and generating them accurately can be challenging and prone to misinterpretation for some lenders. When properly harnessed, customer data analytics aids in creating personalized marketing strategies, enhancing product offerings, and streamlining banking operations. Utilizing artificial intelligence and machine learning can significantly improve the customer experience by analyzing customer data to predict interest rates and tailor services to individual needs.

Leading banks use machine learning and artificial intelligence to predict future customer patterns and habits. This approach results in hyper-personalized experiences based on actual customer data and advanced machine learning predictive models, rather than on generalizations or averages, thereby improving the overall customer experience and optimizing interest rates.

The Future of Customer Analytics in Banking

The future of customer analytics in banking is promising and exciting, with endless opportunities for growth and innovation.

AI and machine learning are key drivers of customer analytics in the future of banking. They enable predictive analytics, allowing banks to anticipate customer needs, offer personalized services, and strengthen their business processes and systems.

Real-time data processing will also be crucial, providing instant insights into customer behavior and helping banks make quick decisions on products or services. Despite AI being seen as the future in many industries, market reports only 8% of banks are using AI-informed insights to inform their campaigns. The same source also reports that only 16% of banks have standard protocols for creating AI tools.

Digital banking platforms will continue to evolve, integrating more seamlessly with other financial tools like budgeting apps or investment platforms for an all-around financial management experience. For example, customer analytics service providers like Quantzig is already seeing 61% of customers interacting with their bank’s digital channels weekly, and 32% prefer mobile banking services over visiting an in-person branch. Banks who leverage customer analytics are seeing tangible results: Today’s banks are already generating 5-15% more revenue from campaigns, and launching campaigns two to four times faster, using insights from customer analytics.

Integrating real-time data reports, historical data, and present data enables comprehensive customer insights and risk analysis. Business intelligence tools will further enhance the ability to understand market trends and customer segments, facilitating better-targeted strategies and improved customer experiences.

Quantzig’s deployment of customer analytics solutions provided the client with a strategic advantage in mitigating fraud, personalizing customer experiences, and improving acquisition and retention rates. This case highlights the critical role of data analytics in empowering banks and financial institutions to enhance their revenue while maintaining stringent safety measures and adhering to industry regulations.

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