The results of a new poll released last week by the deVere Group show that more than half of the world’s millennials are happy to switch to, or already have, a digital-only bank.
While that may be welcome news for financial players in the online ecosystem, it does not augur well for real-world banks, as most of them, along with other financial institutions such as insurance companies, credit card companies, and payment providers, remain mired in the traditional way of doing business.
Even those financial institutions that have tried to make the shift find themselves straddling the two worlds, still trying to optimize customer service across channel silos, struggling with call centers and mobile apps at the same time.
In a sense, the story unfolding here is comparable to the battle between traditional retailers and e-commerce firms.
It’s a struggle which, from the looks of it, the traditionalists may well lose if they do not take some action.
Only those who adapt to the ways of the digital world will eventually survive, and the same is true for the finance sector.
As global financial institutions continue to cope with pressure on the top and bottom lines, it is becoming increasingly clear that, like in other businesses, here too, customer service is driving sales and profits.
Please speak to our experts to find out how data analytics can enhance your customer experience
Finance Businesses Need to be Proactive, Not Reactive, in Customer Service
Even for financial enterprises that have made the switch, customer service can no longer be reactive. Proactivity is required to keep customers happy with the brand, the product, or the service.
Proactivity should not start and end with providing personalized customer service; financial institutions need to rethink how they interact with prospects and clients.
To achieve a measurable degree of proactivity, data analytics techniques such as predictive analytics and customer value analysis (CVA) need to be deployed.
Improving the customer experience through advanced data analytics will keep customers satisfied and eventually drive up revenue. Analytics can help identify the next-best action or product offering.
CVA is the interaction between technology, data, statistics, and business processes. It uses data science to monitor consumer buying patterns, scrutinize customer responses, and other activities, and then applies these insights to customer service.
A straw poll, however, shows that many banks and other payment providers have started implementing customer service transformation to varying degrees but have not yet fully leveraged it.
How Data Science Can Play A Role
Delivering a high-quality omnichannel experience throughout the customer journey requires integrating customer interactions across digital and traditional channels.
As we said earlier, customer service is shifting from branch and call-centric models to a seamless omnichannel dynamic interaction between the enterprise and the customer, albeit slowly.
Financial institutions need to develop customer service strategies that are not only data-driven but also incorporate data-driven innovations, such as machine learning and predictive analytics.
They must not fall into the classic trap of operating data silos, but the strategy must envision the connection of data across the entire technology landscape. The plan must simplify the landscape while reducing data redundancies.
Writing in an article in MIT Sloan Management Review, Barbara H. Wixom, a principal research scientist at the MIT Center for Information Systems Research in Cambridge, Massachusetts, and Gabriele Piccoli, the Edward G. Schlieder Chair of Information Sciences at Louisiana State University, had this to say about “data paradox”:
…managers continue to find it challenging to use (this) data to create new value for customers. We blame this data paradox on the classic data-insight-action framework. The familiar process lulls managers into thinking that producing “valuable” data or surfacing insights for their customers is enough. This encourages passivity, and companies become much too trusting that customers will actually use and act upon the information provided. As a result, companies deploy analytics-based features and experiences that customers don’t use. And because there is no action, there is no value creation and, indeed, no value capture by the company.
Research by the duo showed that analytics-based experiences and customer product features paid off only when combined with aggressive tactics. Those companies that learned to do this relied less on a sequential data-insight-action process and instead interwove insights and action activities to deliver actioned analytics to their customers.
Invest in Technology Like AI
Thus, investments in technology like artificial intelligence and robotics are critical to transforming the customer experience.
For example, machine learning can help in the following ways:
- Deliver personalized customer services
- Recommend more products and services
- Automate personal finance
All of this and more can be achieved by deploying AI. Machine learning can use customer profiles, purchase history, preferences, demographics, and other data to tailor services and deliver highly targeted offers that improve customer satisfaction.
For example, with ML-driven data analytics, banks can increase customer retention due to customer profiling.
Please speak to our experts to find out how data analytics can enhance your customer experience
The use of analytics means banks no longer have a generic view of their customers, but a more complete picture of each customer, which will help them track their online banking behaviors and tailor their services to their preferences.
Before signing off, a report by Deloitte Digital’s Digital Banking Maturity 2020 found that 60 percent of banks had either shut or shortened their opening hours and fast-tracked new digital features, such as automated account creation, remote identification and verification, and contactless payments.
Conclusion
It is of utmost importance for financial sector services to measure the customer experience and identify ways to improve it. For this, financial services enterprises must increase their adoption of data analytics and AI to capitalize on the data from new, digitally driven channels.
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