Data analytics: Driving super personalised customer experience for banks

FIs have been at the centre of technology adoption and are pioneers in using new age technologies such as AI, ML, Advanced Analytics and more. The way they use these technologies evolve, as the customers of today who are extremely sophisticated and adopt rapid digitisation in all aspects of life.
In the early 1990s, Bill Gates had said that “Banking is necessary, Banks are not”, and we are now realising what he meant then. The new-age customer wants ubiquitous banking services from anywhere, at any time and from any device! It is an era of customer-centric banking and product-centric banking has become passe. Omnichannel is gaining popularity that it’s no longer a privilege but a necessity, the failure to adopt is suicidal for banks. FIs are continuously using technologies to analyse, understand and build strategies that help them better service their customers, in line with their current needs and preferences.

Data analytics: At the core of customer experience 

Banks have moved from being store of value to store of trust to store of critical information.

Valued at $4.93 billion in 2021, the data analytics in banking market is estimated to be growing at a CAGR of 19.4% to reach a value of $28.11 billion by 2031. Data-driven-banking is no longer a fancy new concept and has amassed tremendous importance gradually. Data translates to the trust factor that exists between the banks and their customers, entrusting the former with sensitive information. It is this trusted data that fuels the gamut of banking.  

Banks and FIs have begun to realise the invisible curtain that clouds what they think the map of the customer journey should look like from what the customer really wants to experience.  This reality can be easily deciphered from the digital trail that a customer leaves behind across the multiple touch points such as websites, mobiles apps, social networks, digital transactions and much more. It contains rich data that can easily help banks chart out a customer’s journey and predict the engagement of the customer in terms of the when, where and how. Banking is now more about providing super personalised customer experience. Some of the key areas where data analytics can be leveraged in a customer’s banking journey are:

1. Acquiring new customers with enhanced prospecting capabilities.
2. Predict the needs of a customer much ahead of requirements realisation by the customer.
3. Analyse potential areas where the customer would require support.
4. Risk assessments on the go using extremely large data sets to prevent bad debts without being extremely risk-free and under serving the needy.
5. Optimise pricing for relevant products/services through a segment of one approach.
6. Retention strategies to change behaviours and negate potential customer attrition.

That data analytics can not only transform the banking business for good, but also can build robust customer relationships based on transparency and trust.  This is a fact universtally accepted across the banking and financial services industry, be it global players or regional and small sized super local FIs. Several players have begun to utilise decision intelligence platforms to provide contextual services and solutions, laced with empathy, to humanise the experience in this tech-driven era. One such example is the recent collaboration of US Bank with Adobe to provide a more personalised experience for its consumer banking customers, in-branch and online. With the Adobe Experience Platform, US Bank can provide more personalised experiences that evolve with customer expectations, having a single view of the customer while combining both offline and online data.

Challenges of using data analytics in banking  

Data quality and integrity:  Advanced analytics and use of ML and AI algorithms are premised on on high-quality data.  Needless to say, poor-quality data can lead to inaccurate insights and decisions. When the petabytes of data are not validated, profiled, standardised and purified it can result in conflicting, outdated or inaccurate information, impacting data integrity. Imagine the outcome when such low-quality data is fed into the machine learning algorithms! The lack of a single source of truth can prove very costly for banks and perhaps lead to loss of opportunities to deliver superior customer experience.   Many banks end up doing an expensive catchup game or worse still give-up being data enabled  

Without accurate data, digital banking s is myth

Siloed data sources and assets with legacy infra: New-age customers not only use multiple touch points for their baking needs but multiple digital assets (many through external partners). This translates to data residing across disparate systems.  Challenged with legacy infrastructure and lack of capabilities to communicate with each other, this results in sub-optimal analytics and inefficient inferences from elegant algorithms.

Data integration is another area often overlooked and results in lower ROI

Digital transformation fatigue: Even before the onset of the pandemic, digital transformation became a necessity to stay customer centric and ahead of competition. This digital transformation journey now seems to have become a never-ending race. There are several banks that have been shepherding their digitisation journey on a piece-meal basis and many more that are losing momentum with their transformation, unable to keep up with the pace of innovation along with the demand for newer services and solutions that are also personalised. Many banks are increasing their costs while trying to optimise budgets, by not investing in the requisite processes and data infrastructure.

Banking is more digital and less banking.  This is irreversible and need to be respected.

 Conclusion

Data analytics is powering banks to steer away from being product-oriented and bring back the customer perspective at the core. As the new-age banking customers seek more contextual and super-personalised experiences, data analytics is a must-have for banks. Data analytics and related technology that can facilitate large-scale personalisation, compliant with the industry rules and regulations, are the need of the hour for the banking industry to thrive in the future. They can easily provide the banks with a 360-degreee view of the customer and make omnichannel services much more effective. Leveraging data analytics banks can deliver super personalised customer experiences, establish long haul customer relationships, and utilise customer analytics to give them the competitive edge.

Muraleedhar Ramapai, Executive Director, Maveric Systems Ltd.