Banks must move from a product-based paradigm to one that puts the customer in the driver’s seat. Banks today are grappling with an overly simplistic understanding of their customers combined with a vastly complex product set with only very subtle differences, frequently unappreciated by customers. All of this comes at a significant cost in terms …
Banks must move from a product-based paradigm to one that puts the customer in the driver’s seat.
Banks today are grappling with an overly simplistic understanding of their customers combined with a vastly complex product set with only very subtle differences, frequently unappreciated by customers. All of this comes at a significant cost in terms of operations, technology, and service.
What’s more, most banks have never created a close relationship with their retail customers and understand little of their actual needs. This is happening at a time when consumers are redefining their expectations, taking cues from other industries that offer multichannel access, product simplicity, seamless integration and ‘me’ targeting. They want convenience, personalisation, accessibility and ease of use. They want to feel like their bank is anticipating their needs, not bombarding them with irrelevant product offerings.
Conditioned by service experiences with digital leaders in other industries, today’s retail banking customers expect personalised solutions for how, when and where they want to bank. They expect more convenience and faster service based on their experiences in a fast-paced mobile world. Banks must therefore be able to not only personalise their services and tailor them to the customer, but also learn to deliver them instantly and at exactly the right moment.
In fact, the entire retail banking business model needs to change from a model that is built largely around product silos to one that revolves around translating deep customer insights into tailor-made products and services. To achieve this transformation, banks must focus on two things:
1. Understanding customers better
Data-driven micro-segmentation is the only way to provide the tailored experience today’s customers expect. The goal should always be to drill down to an increasingly granular level. Financial marketers have the opportunity to know their customers better than most other industries and machine learning technology allows them to do so more deeply than ever before.
Sophisticated segmentation can be achieved by obtaining insights about customer demographics, behaviours, geo-location, purchase/transaction history, online behaviors, search history, requests for information, customer service interactions, and social media interactions. Leveraging this information empowers financial marketers to offer a much more relevant online experience, and also to make cross-sell or upsell recommendations based on each consumer’s unique segment.
2. Building stronger, insight-based customer relationships
Recent reports show that up to 78% of customers consider their interaction with the bank “transactional”, which barely amounts to a relationship. In addition most customers merely interact once or twice a week or even less with their bank. Increasing the frequency and more importantly quality of interaction is paramount to rebuild trust, the key factor in the relationship building process. In order to be able to unfold a valuable relationship it is imperative that the digital bank can show a deep understanding of the customer’s financial situation as well as an ability to support and guide the customer wherever needed. Money management is an essential part of establishing that connection, sense of engagement and trust.
These two elements operate in a virtuous circle: the better you get know your customer, the easier is to establish a relationship that in turn increases your knowledge of that customer.
But how to establish that virtuous circle in the first place?
That requires the acquisition, integration and analysis of multiple sources of internal and external data; create complex models to be able to understand their customers’ needs; and build a delivery mechanism across all channels to be present with a relevant solution at the time of need. Sounds complex, right?
Enter Strands Discovery
Strands Discovery does precisely that. By reading from various enterprise data sources and utilising machine learning (ML) algorithms, Discovery is capable of drawing a detailed and holistic picture of individual customers’ financial (i.e. life stage), attitudinal (i.e. life style) and behavioural (i.e. spending behavior, loyalty) profiles.
But more importantly, it can detect opportunities: transitions (for instance a change of life stage or lifestyle), needs or events (a need for a car or a trip coming up) clustering customers in groups and allowing marketeers to dig deeper in the audience of those to be able to personalise the bank financial offering to the individuals within that group.
Once that is done, Strands Discovery, uses Precision Targeting to deliver personalised messages in real time and in the appropriate context, which is the key to producing relevant customer interactions.
Finally, Strands Discovery makes all customer profiles and opportunities visible to the whole enterprise via customer dashboard, which helps in breaking down product, organisational and channel silos and putting the bank on the right path toward a customer-centric business model.
What about ROI?
By walking down this path banks will inevitably enrich the customer experience leading to higher customer satisfaction and retention.
The deeper customer understanding and relationship will allow banks to calculate and focus in long term value, considering the value of not only the expected immediate gain that current loan products will generate, but also the potential value of future business opportunities that could arise from the existing relationship.
But these deeper understandings and relationships will also boost sales.
Finally, in the search for profitable income growth, two distinct business models are competing: the high-volume, low-cost model and the service differentiated model. Most financial institutions are choosing the latter, differentiating their service for customer segments, even individuals.