Global digitalization makes financial institutions (FIs) face severe competition, giving their clients plenty of opportunities at the same time. The clients, though, don’t seem to be satisfied with what they get from the majority of FIs.
An image of a digitally-connected client is now more demanding but less loyal: If FIs fail to cater to what customers demand, they will lose clients to competitors who will meet their demands.
The key to higher satisfaction and retention rates lies in predicting customer needs and delivering accordingly.
Data science and predictive analytics help improve customer experience and create new revenue streams for financial companies.
Some digitally-minded companies, such as banks, have already succeeded through driving customer engagement in financial services. For example, Discover, an American financial services company, uses predictive analytics to reduce attrition and increase the rate of successful payments.
Financial institutions can better engage with customers by working with valuable data such as customer profile information, transactional data, and customer feedback.
8 Ways Predictive Analytics Can Improve the Customer Experience in Financial Software
- Meet Customers’ Expectations and Collect Feedback
- Mitigate Potential Challenges
- Help Customers Feel Safe by Preventing Fraud
- Recruit and Keep the Best Talent
- Ensure Data Traceability
- Gain a Competitive Advantage
- Build Trust and Loyalty
- Target Niche Clients
Data Management Provides a Solid Foundation for a Better Customer Experience
Transforming the customer experience in banking and other financial services is now an inevitable part of development strategies – Or, at least, it should be, if companies want to receive a bigger market share and drive customer engagement in financial services.
Data management includes enterprise data such as customer, market, and transaction data as well as big data such as that from social media, email, images, and web logs.
In the machine learning phase, developers capture feedback, prepare data, and then train and apply the model to applications and processes.
Collecting, managing, analyzing, and applying data to processes can help businesses improve their overall customer experience.
Predictive analytics, on the other hand, has more to do with the application of the data managed.
According to Aberdeen, predictive analytics can boost your sales through:
- Statistical model
- Consumer behavior
- Machine learning
Predictive analytics in finance can help businesses engage customers, streamline internal processes, better manage risk, provide better training for HR recruiters, and ensure data transparency.
1. Meet Customers’ Expectations and Collect Feedback
Customers expect more businesses to cater to their needs and time.
Nearly half of banking customers (40%) are willing to pay more for simpler experiences and interactions, according to EY Global Consumer Banking Survey.
In fact, most customers prefer non-bank providers because they offer a better customer experience than traditional market players.
Additionally, the number of clients planning to turn their heads and wallets towards digital-only banks increases daily.
Including machine learning algorithms and predictive analytics techniques while shaping the tactics on how to improve customer engagement in banking is an investment for FIs to consider.
2. Mitigate Potential Challenges
Banking entities can more easily preform daily tasks through machine learning and predictive modeling.
Predicting operational demand based on historical data and future events can help predict:
- Demand spikes and rush hours
- The amount of cash required for specific ATM locations
- The effectiveness of chatbots versus call center operators
Being able to predict situations that may cause productivity to slow can help FIs prepare in advance and make internal processes smoother.
3. Help Customers Feel Safe by Preventing Fraud
Through building predictive analytical models, banks can identify and prevent exposure states.
Stress testing, bank capital adequacy, and market liquidity risk of banks are calculated and predicted against existing standards.
With data insights based on predictive modeling, banks can prevent credit defaults through tailored collection strategies and borrowers’ segmentation. The same is with the risks of fraud.
Detecting and preventing fraudulent activity are the main tasks for data management. Tracing transaction anomalies and suspicious activities and real-time responding to threats help financial institutions fight with this pressing issue.
4. Recruit and Keep the Best Talent
In terms of human resources (HR), banks should consider how to:
- Optimize of the employee count for effective operation
- Motivate personnel
- Reflect the company’s image in a positive light
Data management and financial data analytics can help HR departments measure the effectiveness of incentives and training performance.
It can define how many personnel each branch requires and create models for salary optimization.
5. Ensure Data Traceability
The word “regulations” often appears next to the word “financial” – and it’s no wonder this industry is exposed to a growing number of regulatory and legislative initiatives.
Banks tap on data management to ensure data traceability.
With the timeliness of reporting, data management and predictive analytics also provides adequate stress management and credit management models.
6. Gain a Competitive Advantage
In finance and banking, service or product differentiation is quite narrow. And that’s one of the reasons why the battle for customers is so severe.
Streamlining customer engagement through the financial software products can turn the battle in favor of the more tech-savvy side.
7. Build Trust and Loyalty
Streamlining customer experience is a must-to-include item while developing both marketing and digital strategies.
Delivering the right customer experience means that a business understands its clients with their needs. Only through appreciation on the client's side, brands can build up loyalty and trust.
8. Target Niche Clients
In financial business, similar to the other domains, some clients are difficult to categorize. These people are often influencers and have a very particular mindset and social standing.
Businesses are often afraid of such people since they can be equally beneficial and destructive for the business’ image.
However, ensuring them with 5-star customer experiences can be a solution. Think of targeting them through one-of-the kind personalized propositions or customer-tailored promotions with the right messages at the right time and device.
These are the most typical areas of customer-related concerns, where predictive analytics can come into play:
- Transparency in communication and fee collection
- Smooth omnichannel experience
- Smooth operations between digital banking channels
- Personalized offers and advice on preferable financial services
- Customers segmentation
- Claim management
If you are thinking of achieving similar goals, it’s high time to start considering predictive analytics for FinTech and banking.
Predictive Insights Help Win Users' Hearts
FinTech startups are rising as competitors challenging traditional banks, while financial titans are actively embracing digital transformation. Big data is a game-changer here. Only those capable of harnessing and managing it will get ahead and meet the growing expectations of clients.
Embracing predictive analytics as a part of technological strategy helps get precious customer insights, enhance cross-selling and speed up customer acquisition.
Add exceptional fraud preventing capabilities and the ability to streamline operations, and you’ll get a powerful tool in your inventory.
Even though digital clients are picky, they are predictable. Don’t take too long to learn what will make them stay with your company instead of moving to one of your competitors.