“How can this be possible”. It’s the third time I have heard this phrase in the past couple of months.
Which got me thinking. What makes business leaders of large Fortune 500 Banks so disbelieving when confronting with the possibility of real real-time marketing personalization. And yet be in silent admiration of new-age fintechs nipping at their heels.
To get a few things off the table, we’ll always find ways to attribute this to legacy, frolicking pachyderms or just plain inertia. I truly believe these and similar pithy maxims drum up a false sense of the unsurmountable. So we’ll leave them and get to first principles.
I think the tenets of being customer centric in engagement are apparent:
- Get a deep understanding of customer’s history, preferences and aspirations
- Create messaging and communication that is relevant
- Get the message to the right person at the right time through the right mix of channels
Pause. Double take. Get the message to the right person at the right time through the right mix of channels? Ok. Now we’ve started hitting the roadblocks.
The idea of cross-channel synchronization of messaging is non-trivial. It requires a few things to come together. One – a central engine (or brain, if you will), that is determining which message is relevant for which customer. Two – recognising a customer when he is present on a particular channel. Three – understanding the context with which the customer is engaging on that channel.
This is where the opportunity to beat that fintech at its own game comes alive.
Because your customer knows you. Has engaged with you for years. Has shared his needs and wants with you. You understand the channels through which the customer best likes to engage with you.
Which means – you have the date, the channels, the portfolio and the messaging.
What we need to do is bring all this knowledge into that central engine, the ‘brain’. Build the ability to understand the ideal next best action for engagement. Recognise the context of engagement on a channel to alter communication in real time. He is an ideal customer for a mortgage loan. That is what our recommendation engine tells us. But we observe him spending a lot of time researching travel insurance on our website. The context guides us to inject the personal loan and travel insurance to him on the web banner. And hold off on that home loan offer for another day.
The prevalence of AI-ML algorithmic capability, coupled with cloud technologies allows this to happen seamlessly. That is – the ability to compute or recompute product-offer-message recommendations in real time based on changes detected in customer context; and the ability for different execution channels – websites, mail gateways, mobile apps, teller consoles – to consume these recommendations as a service. It does not require fundamental re-architecting of your CRM systems, your execution gateways or your transactional workflows. We need to devise ways for each of these sources and destinations to talk to the central engine. Which means – while the integration and interoperability requirements are non-trivial, they are not “impossible” either.
VP, Strategy & Products
BRIDGEi2i Analytics Solution
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