01/30/2023 | Press release | Distributed by Public on 01/30/2023 01:50
Gone are the days of regular visits to local bank branches for customer service needs to be met. While most BFSI institutions have moved on to dynamic websites, mobile applications, self-serve models and conversational chatbots for multilayered customer engagement, it is pertinent to note that this is only the tip of the iceberg.
The evolution in customer engagement has multiple drivers but can be bracketed into a few that can be considered key driving factors. A primary reason would be a change in expectations of the customers themselves. Today's customers are influenced by the exposure and experience they receive from OTT, eCommerce and other consumer technologies. With access to immense consumer data sources and advanced analytics capabilities, these organisations can offer hyper-personalisation and instant gratification to customers. For instance, we have seen these tech giants entering the fintechspace and disrupting the BFSI sector.
The introduction of neo-banks, fintech startups and micro-finance organisations have also contributed to the disruption and changing consumer expectations. These organisations are creating the perfect cocktail of technology, data-driven product development and hyper-personalised targeted moments-based interactions to elevate customer engagement to the next level. The ones who succeeded work on the age-old model of time=money. i.e., customer time spent engaging has a higher chance of eventually converting to revenue, such as gamification of services.
Digital Adoption, Customer Dynamics, and New Products
To remain competitive and relevant in a space that now includes agile, highly data-driven digital entrants- legacy banks and financial institutions are forced to adapt and delve deeper into customer behaviour by integrating data from multiple channels to be able to provide real-time insights and actions. Banks need to shift to highly customisable and personalised marketing experiences across all channels by pivoting to a customer-centric approach instead of a product-centric one.
The advent of new technologies and evolving customer expectations and behaviour have pushed BFSI institutions, including traditional banks, to find new ways to cater to digitally-savvy customers. But just offering web-based or mobile-friendly interactions is not enough. The self-serve models customised and personalised product recommendations, and moments-based real-time interactions are still distant for bank customers.
This would require leveraging real-time behavioural and transactional data to create a potent customer knowledge base. A rich customer knowledge base with the appropriate platform can predict when to reach out to customers and which product or service they would be interested in. Essentially, when the appropriate martech ecosystem is adopted, the bank or financial system can predict an individual customer's needs and reach out at the right time with the right product or service. A customer who has a saving account and a credit card with XYZ bank but has a loan from another bank can be approached with lower interest rates based on their credit score/history.
With ever-growing amounts of data entering the system and the use of emerging technologies such as artificial intelligence combined with intuitive marketing technology platforms, BFSI organisations can have highly customised touchpoints with their customers.
For example, loyalty programs enhance customer experience by instantly gratifying customers through rewards, offers and discounts. This is especially important for legacy BFSI institutions as trustworthiness and personal relationships are two of their strengths, with new marketing technologies now providing the option to continue maintaining their advantages while going digital.
Artificial intelligence in BFSI has massive potential for growth, and the institutions that are quickest to draw will likely gain a huge competitive advantage. These organisations will be able to improve operations, drive customer engagement, optimise experiences, and automate insights to act on, in addition to providing services like conversational chatbots, portfolio optimisation, enhancing credit decisions, and strengthening cybersecurity.
AI & ML can segment customers based on dynamic parameters, increase product or service adoption by using micro-segmentation and providing context-based offers on a real-time basis and enhance cross-selling or upselling.
An AI/ML-enabled martech solution can allow BFSI institutions to:
Additionally, A well-thought and structured approach can take advantage of having a single source of data across the disparate functional areas of the business (for example, the loan department). This would allow the martech tool to analyse individual customer profiles holistically, enabling the AI engine to recommend offers with a higher chance of acceptance.
Banks and other non-banking financial institutions are aware of the risk of losing customers to more customer-oriented, digitalised and data-driven institutions. This primarily concerns the competitor's ability to engage, retain and activate their customers.
The solution lies in advanced analytics. The BFSI sector needs to be able to use data to predict sales trends, identify and act on customer sentiment, identify and act on risks in advance, and segment customers in a manner that allows them to target high-value customers for greater returns specifically.
As per this article, advanced analytics works best based on five principles - segmenting customers, automating forecasts, predicting loyalty, understanding the causes of churn, and a test-and-learn approach.
Most institutions probably do so based on demographics (age, gender, income, etc.), which is useful but ignores the nuances of the individual. Demographic-based segmentation of customers inherently includes the assumption that the group is homogenous in nature.
The right martech ecosystem that provides advanced analytics will be able to provide the reasons behind a customer's behaviour. Why did customer X choose a product from a competitor? Similarly, advanced analytics of data can help a bank predict whether a customer will remain loyal or not (and why).
As discussed above, the future of marketing in BFSI looks interesting with the advent of advanced martech solutions that offer the best of capabilities. But is that the only correct approach for all BFSI marketing problems?
It's not that easy! The significance is to recognise that a martech solution is ever-evolving, and organisations should look to ensure that their customer's profiles and personas are continuously updated. Sources like first-party cookies allow banks to upgrade their customers' personal data, thereby improving future journeys. The right technology platform will then allow the bank to enhance the user's experience and amplify engagement in real-time.
The use of AI & ML, when paired with the right technology, can ensure that institutions can improve targeting accuracy, provide much greater value to customers and, in turn, increase revenues. For example, AI and ML use cases may include financial monitoring, investment predictions, process automation, secure transactions, risk management, algorithmic trading, financial advisory, etc.
Another value-added service that banks can offer to their customers is by using customer behavioural and transactional data to provide real-time contextual promotions. For instance, a bank could partner with retailers and access geospatial data to push promotions to customers who have visited the store (physically or digitally) and are currently in the vicinity. This provides added value to the customer, which in turn, enhances customer loyalty.
It is salient to remember that competitiveness in the BFSI sector is increasing, reflected by the ever-rising share of marketing budgets invested in the digital domain. Financial institutions must find ways to be unique, with the best performers being those who prioritise providing value to customers over product sales. (Need link to "the outcome driven personalization" blog to link)
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