We are living in the “age of the customer”. Customers really are calling the shots, controlling the relationship with the bank (instead of the other way around), demanding a hyper-personalized experience and the bank to already have anticipated (mind you, correctly) her financial needs.
Not to mention, this naturally stems from the current environs that customers increasingly find themselves in where they have more information on their fingertips than ever before, more touchpoints where they experience relationship with the bank, burgeoning amounts data/ information generated by customer engagements that enterprises are expected to leverage, where such customers have been the shown the way by digital natives such as amazon, google and Spotify. And so, customer expectations on how B2C enterprises including (and increasingly) banks need to engage with them is now at a new unprecedented and unforgiving normal.
Note that this has far reaching implications for how banks look at engaging with the customer, how they plan marketing dollars, how banks plan the next round of investments in digital and marketing technology and how they build strategy and roadmap for the future, as well as investment in analytics powered decisioning mechanisms and by extension (and foundation) in data and technology
Why use data to maximize relevance
The ever-increasing amounts of customer related data that banks are finding at their disposal is varied in granularity, sources, harness-ability (structured or unstructured), and constantly expanding.
In not using all of this information that the customer is generating about herself (through transactions and engagements at touchpoints), bank marketers end up sending the same canned messages to all customers. And the customer is essentially burdened to sift through the spam to figure out what is relevant for her versus not. When it comes to financial needs, financial services providers that mine all information for a sharper understanding of the customer, and personalize propositions accordingly, stand to gain. Personalized propositions (messages or offers) find relevance with customer’s situation and resonate with the customer. Inevitably, the customer finds it in her interest to not waste precious time scanning through multitude of irrelevant messages from banks that have not embraced personalization.
This is also the age of short attention span and timeliness of action. Any marketing proposition does not only need to align with customer’s profile and past behaviour but necessarily needs to find relevance in her immediate context. This translates into using real time interaction data and geo-location data, the ability to understand the customer’s current context and make a proposition to her that aligns with her situation.
The technology quandary
The fact that we need intelligent real time decision systems and so a set of new sophisticated recommendation algorithms is a no brainer. But most banks freeze in their tracks when they try to fathom the installations or investments that have to be made in the areas of data and technology – two foundational pillars that will make the algorithms come alive and personalized decisioning real.
Talking of technology, the number of different technologies needed and the options within each can be mind-numbingly high. And even though bank marketers have invested in various different systems and technologies over a period of time, banks still struggle with some foundational capabilities such as having a true 360-degree view of the customer which means getting cross product view (slightly easier) as well as integrated view of digital and offline customer interactions. Each system has its own set of data and these end up further demarcating the silos of channel and touchpoints.
How do we get started?
Personalization at scale needs a programmatic approach. As a first step, we paint the desired end state view as well as list down use cases. We also assess the current state so as to be able to chart the way ahead – taking stock of current sources of data and their usability, current channels and their integrability, investments already made in marketing tech stack and view of future investments. It pays to start small, leveraging the existing ecosystem of data and technology, and setting up recommendation algorithms by choosing to solve for use cases that are quick wins.
Test and learn is a critical part of the game to enhance the effectiveness of personalization (improvement in conversations). Incremental build in waves, enhances sophistication of both algorithms as well as delivery mechanisms (read channels and touchpoints). Such an iterative approach to building personalization capability ensures that impact can be delivered along the way. Such demonstration of benefits from the program also makes for an easier, than otherwise, buy in from stakeholders and sponsors of the program.
Product Head- BRIDGEpersona
Financial Services & Insurance
BRIDGEi2i Analytics Solution
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