When customers walk into their bank, they normally expect to talk to a personal banker or customer manager to discuss their need or issue. With every customer tending to demand personalized relationship with their banks, scalability issues creep in for a
branch level personalization for each and every customer. However, retail banking has gone digital and therefore, the personalization that customers used to get in their banks is getting missed out as more and more people are going online for their banking
needs. In such situations, customers demand that banks create products and solutions to cater to their needs and not the other way around. Consequently, the focus of banks has turned to cultivating customer engagement through personalization adopting the latest
tools and technologies in AI. According to Oracle's The Era I Enterprise: Ready for Anything
report, banking institutions that are able to successfully offer customers a highly personalized experience were expected to earn an additional 14% in annual revenues.
Even though personalization can take on many meanings, from a banking point of view, it most notably refers to
- Relevant products and offerings based on their needs and channel preferences
- Pricing of products based on risk and customer value
- Customer servicing as per their preferences
Personalizing the right offers to customers – Need of the hour
Lately, there has been some level of success for banks in launching innovative products by creating bundled solutions to cater to the needs of the customer. Nonetheless, with the wide array of product choices available, offering the right product to the
right customer and at the right time remains a challenge. Many a banks' marketing/sales teams continue to depend on cold-calling to gain acumen into a customer's needs, but with a low success rate. Also, the constant intrusion might deter the customer from
engaging with the bank. Most of the predictive analytics employed currently by marketing has been used after the customer needs are identified.
The need is therefore to effectively and efficiently develop a group of products and service offerings that could interest a customer and personalize them to correspond to the customer's true needs. Predictive analytics can be utilized for creating models
that could algorithmically pinpoint product offerings that a customer scouts for, by tracking their life events and different triggers on social media.
Need identification based on customer life events
Almost every critical event in a customer's life triggers a multitude of changes in spending patterns and financial priorities. It is a proven theory that there is a definite correlation between life's events and the financial decisions that a person takes.
Currently, many banks utilize information provided by their customers to offer appropriate products and services. One example would be a customer adding his/her newly born baby to an existing insurance policy, which the bank would use to offer the customer
with a new kids savings product. Banks generally collect retrospective details when a customer wants to update his/her personal information. However, such information related to customer life events will not be completely effective due to the fact that up-
and cross-selling opportunities arise both before and after such events take place. Just like that, the bank misses out on up- /cross-selling opportunities waiting for the customer to retrospectively register such events with the bank. Therefore the ideal
analytics model should be able to predict a customer's imminent life triggers.
The advancements made in social media analytics empower deciphering social media data to forecast impending life events. A Stanford University
study proposed a model to effectively extract and decode life events using Natural Language Processing (NLP), based on published tweets by the users on Twitter. This model can be effectively used to establish events like job change, graduation, marriage,
divorce, location change, childbirth, retirement, etc. The ability to discern such events in advance can certainly help retail banks in targeting customers – current and future, with personalized products and offerings that are most relevant to them.
With the continued growth of social media platforms and the increasing sophistication of the analytics tools, banks will have true opportunity to get to know their customers and better cater to them. Other than the trigger and event data, there are also
other valuable sources of information on social media like consumer feedback, discussions around products and brands, requests for new services and brand recommendations. Mining all of these data and highlighting patterns, will also help in understanding and
classifying which customer segments are well positioned to respond to the bank's marketing campaigns in the future. In this era of digital banking and smart devices, enormous amount of data foraged from social networks, search engines and smart devices can
be utilized for better understanding of the customer behaviour and for more meaningful ways of customer engagement with distinct product and service offerings.
Personalization in every step leading to customer engagement
While needs identification is the stepping stone towards personalization of products or offers, in order to realize exhaustive benefits of analytics and personalization, each step after the analysis of the identified needs, needs to be personalized before
executing the same over an omni-channel environment.
Products and services designed to the customer needs: Products and services should be tailored exactly to the needs of the customer. The bank has to determine and establish the appropriate mix of their products, partner solutions, and services
to build an exceptional, unbeatable offering. For example, when a young customer accesses the mobile app, the bank can offer him a customized offer that gives him two free movie tickets every month if a particular dollar amount is spent on his credit card
per month as well as he subscribes to insurances for his gadgets, as opposed to offering a life insurance policy or estate planning services.
Personalizing product pricing: The effectiveness of marketing offers lies in pricing products and services, attractively so as to make the offer irresistible. For example, when a bank understands that a customer is looking for an auto loan,
bundling the loan product offer with partner deals and appropriate discounts could add immense value to the offer. While innovations in technology creates a possibility of perfect price discrimination, consumer perceptions of fairness with regard to the personalized
pricing is going to be important for successful customer engagement specifically in an environment where information is freely available
Personalizing content and communication: Subsequent to customizing an offering, the bank will have to then evaluate how best to communicate with the customer adopting a multichannel approach. It has to be designed to such a degree that it
enhances the 'feel good' factor, rather than sounding obtrusive or interfering. Artificial Intelligence and NLP has now made it possible for banks to design and develop personalized, automated and genuinely personal communication through the channels most
preferred by the individual customer.
The key to personalize in a large scale for the business resides in the bank's ability to unify all of their data (including social media data) for personalizing with the help AI (while including data science algorithms that can cope with increasing complexities)
and eventually engage with the customer in an omni-channel environment – which means to be available where the customer is, rather than where you would want them to be. Also key to sustain continued success in personalization is to create a feedback loop system
that would enhance the bank's ability to learn from customer feedback and sharpen the marketing elements – which is at the core of personalizing the banking experience thereby delivering more value to the customer.
The effective utilization of social media analytics can not only enhance customer experience, but it can also propel enhanced business growth through acquisition and increased share of the customer wallet. The crucial element is to design and develop a unified
analytics platform that will necessitate the banks to have technologies, processes and systems that are compatible with the evolving needs. Having said that, banks are currently falling short in personalizing the product offerings, as the data required for
the same is confined across multitude of legacy systems.
Evolving retail banking practices will reshape the manner in which customer services and interactions are carried out. With the evolution of technology, the banking customer experience will become more fulfilling and personal in nature. What we are currently
witnessing is just beginning of the dawn in banking personalization. The future will propel phenomenal innovation in accomplishing customer-centricity through personalization driven by digital transformation.