As customers increasingly experience more consistent, personalised treatment from companies across a wide variety of industries, it is natural for them to expect—and require—the same of financial services providers.
This is an extract from ‘Personalisation-as-a-Service: Harnessing Data in the Banking and Payments Industry,’ a Finextra Research impact study in association with FICO, part of the AWS Cloud Series.
Consumers do not want disjointed experiences across auto loans, credit cards, and HELOCs any more than they want them when shopping for different categories within the same online retailer.
Aside from consistency, 84% of customers revealed that being treated like a person, not a number, is important when winning and retaining business. That’s according to Accenture Global Consumer Pulse Research, which also found that 73% of consumers expect specialised treatment for loyalty and anticipate rewards for past interactions, as well as for sharing their preferences or personal information.
However, only 22% believe that customer experiences are tailored effectively by organisations, and 50% fewer consumers perceive their bank as a trusted partner today than in 2018. Thus, there is a significant opportunity for savvy financial services companies who can meet consumer expectations.
At the same time, financial institutions that fail to satisfy those standards are in jeopardy, as customers are re-evaluating their choice of financial providers given an increasingly diverse and non-traditional range of alternatives.
Ultimately, consumers want financial providers to offer personalised services and integrated offers that address their most relevant needs at the right time, such as when they are buying a car, getting married, purchasing a home, continuing their education, etc.
The financial institutions that can anticipate customer needs and deliver service that is personalised and consistent across channels will be well positioned to thrive in the digital age.
Overcoming the data paradox
Many banks have vast stores of department-specific data, but they often fail to leverage their information for a deeper, enterprise-wide understanding of their customers. This paradox stems from siloed structures that are usually based on products or lines of business, rather than customer segments or individual clients.
However, cloud-based data decisioning platforms can unify these silos, to make their contents interoperable to build hyper-detailed customer profiles, and enable financial services to offer Personalisation-as-a-Service. The engine of this evolution in service is machine learning, which encompasses a range of algorithmic approaches that derive from statistical methods such as regressions and neural networks. Machine learning allows for complex, holistic, and predictive analyses of customer behaviour.
The computational power and analytic versatility required to implement ML-powered advanced analytics—from massive computing clusters to storage—has been unlocked by the cloud, which has significantly reduced the resources necessary for purchase and maintenance.
The result is that more banks have the technical and financial means to deploy decision platforms that visualise each customer not as a collection of disassociated data points but as a mosaic, made up of different characteristics that merge to provide a comprehensive or ‘360-degree’ view. Infused with this sophisticated customer intelligence, banks can provide consistent, tailored experiences.
Click here to read ‘Personalisation-as-a-Service: Harnessing Data in the Banking and Payments Industry’.