When I started my career in banking in the 1980's, banking was often referred to as the 3-6-3 business in the US, as bankers would borrow funds at 3% from the depositor, lend the money at 6% and were off to the golf course by 3 o'clock. Today, technology has revolutionised banking, enabling customers to manage their dealings with banks, i.e., what was once Customer Relationship Management (CRM) has now become Customer Managed Relationship (CMR).
Banking is therefore a 24-7-3 business, which basically means that as a banker, 24X7 you are focussing on just three things - how to anticipate your customer's fast changing needs, how to carry out contextual conversations with your customers through all touch points, and how to create customer delight. Collectively, these have now become major components of the new customer engagement model being followed by banks.
Emergence of new digital technologies has resulted in increased customer expectations from banks, now that they have more choices than ever. In the current competitive landscape service, differentiation has assumed utmost importance for banks to attract customers, grow wallet share and minimise attrition.
Knowing what the customer wantsBanks, by virtue of their business model, have access to significant data about customers' needs and preferences. This has now presented banks with an opportunity to passively listen to customer interactions and gather volumes of data about their needs and preferences over a period of time.
This is a welcome change from the past, when banks lacked quality data and had little idea about effective utilisation of the data they had access to. While, in the past, banks were organised in silos, they are much better integrated now, yielding more comprehensive data in abundance.
However, access to data will not solve any purpose without a system to capture and synthesise it.
Therefore, data analytics as a technological tool can be useful for banks to deep-dive into data collected during the course of customer interactions. This way, experiential heuristics will give way to structured and quantitative models that can provide valuable insights into customer preferences.
Now is the time for banks to move away from 'gut-driven' decisions to a regime of data-driven decisions. Indian banks are slowly warming up to the idea of analytics as a key decision tool and incorporating it in their businesses integrally. At the same time, specialised data analytics firms have emerged that are helping clients, including banks, understand the benefit of adopting analytics.
New-age companies will lead the charge when it comes to adoption of analytics as a core business toolEngaging at customer touch pointsWhile digital interactions and responses can be captured and fed back into the data and analytical framework of banks, it is important that physical channels are transformed to 'digical', i.e., physical channels should also interact with customers with the help of technology and capture all customer interactions.
A sound analytical framework can also present banks with an opportunity to engage with customers more meaningfully, enabling them to carry out micro-targeting. For example, when integrated with CRM, it would help banks to target the right customers with the right products or services and also optimise customer communications.
In a similar fashion, when the digital touch points of a bank such as ATMs, mobile, internet and IVR channels are integrated with this framework, these channels can become more intelligent as the interfaces can now adapt to different situations and can have interactive conversations with the customers.
Big Data backed analytical models can thus help in offering personalised and yet consistent customer experiences.
Predicting customer aspirationsFinally, a good customer engagement model also needs to anticipate customer needs or spot the right opportunity to interact with customers. Banks need to fuse socially collected data with historical transaction data and be able to predict the 'next best action'.
These predictive models need to be continuously refined and improved, which will only be possible, when the success from the action plan thus created is also measured and fed back into the analytical framework. This will not only help banks to develop a new paradigm in customer experience but also help them realign the digital banking strategy to better engage with customers.
Adoption of analytics a culture issueBanks have started to understand the significance of these advantages and have been making significant investments in Social-Mobile-Analytics-Cloud technologies. However, we have miles to go before we can start reaping the benefits of a framework that leverages Big Data and advanced data analytics. Adoption of analytics as a key business tool is more of a culture issue among Indian banks â€“ sometimes, there is far too much resistance from within the organisation. For Indian banks to shake off institutional lethargy and adopt analytics as a key decision aid, the top management has to play a central role. They need to act as ambassadors, showcase benefits and put in place all systems that may be required for internal 'buy-in'.
The new economy in India is seeing the emergence of many such new-age companies which are far more open to free exchange of thoughts and ideas. Many of them are entrepreneurial ventures â€“ being managed by a sole proprietor or a small team.
I believe that these new-age companies will lead the charge when it comes to adoption of analytics as a core business tool and pave the way for more scientific decision-making in the banking sector.
The author is managing director and CEO, Yes Bank and chairman, Yes Institute.