28 Mar Define Your Data Architecture: The Path to Truly Personalized Experiences
Under pressure from younger customers with higher expectations and new digital standards being set in retail, finance companies must do more to build true omnichannel experiences that live up to the full potential of the idea – to provide personalized, contextually relevant customer and prospect experiences across device where, when, and how consumers want to interact.
Key to this concept is harnessing all the rich data that is generated from online and offline interactions. As more and more is written about omnichannel in finance, we must start to get to the heart of how best to plan it. Taking practices born out of “multichannel” planning – where the focus was establishing options to engage with nothing smart or personal – is the mold that must be broken.
The fire is fanned by the idea that consumer demands for retail experiences are on the rise. Finance brands must understand that there is data beyond the transaction that can lead to a better overall customer experience.
As Andrew Joss from Informatica writes in his post, “Omnichannel Banking and the Need for Data”:
Being driven by a younger generation of Banking customers, with high expectations of service provision due to their experiences with some market leading retailers, there is now an increasing demand for channel choice based upon individual preferences allied to continuous availability of the service as well as the ability to switch to an alternative channel if desired.
He goes on:
This same experience has created an impression that all key service providers (i.e. Banks) have huge amounts of customer data and that this data is being used to drive the engagement process. Banks have traditionally managed their businesses as operational or functional siloes as there was little need to do anything else – that time is coming to an end.
The key being data intelligence. The parallel is drawn to consumer expectations based on other retail experiences outside banking which is generally understood to be a better experience. But it is also assumed that banks are rich in data therefore are able to craft experiences based on preference, behavioral patterns, and sentiment which lead to better, higher value marketing and utility to the customer. This is all driven by smarter data management and optimization.
The big promise of omnichannel is the idea of seamlessness across devices. Within that concept, is the notion of personalized and contextual experiences. And this is where most finance companies fall short.
In The Mobile Mind Shift; Engineer Your Business to Win in the Mobile Moment (Groundswell Press, 2014) we learned that “within the next 1-2 years, 50% of consumers will have the expectation of anywhere, anything, anytime on their mobile device and yet in reality a mere 4% of companies actually have what it takes to provide it.”
One of the key challenges is in the strategic planning approach. By definition, omnichannel is to provide users with a seamless experience no matter the transaction point – desktop to mobile to phone to physical space. This needs to be more than just enabling interaction or offering the touch point option such as what multichannel planning provides.
Omnichannel is not achieved through thinking merely about channels and touch points first. It’s achieved by understanding how data flows – the inputs and outputs that map the true view of your complete customer experience – then backfilled by touch point activation and engagement based on what the data is telling us.
The right place to start is not with the touchpoint or technology first but rather with planning the data architecture. How does data get captured? How can it be passed from point to point? How can it inform smarter interactions? Then build the experience on top of that architecture.
Here are some steps to get there:
- Begin by mapping out the data that can and should be captured: behavioral, engagement, demographics, technographics, segmentation, transaction patterns, product purchase, preference, sentiment, etc.
- From the data set, understand which data allows you to build smarter experiences: decide what will allow you to build towards more contextual and personal experiences, and play out these scenarios in prototypes through the lens of the consumer.
- Push the limits of how data can flow: start with an architecture of touch points and technologies modeled after the scenarios to create a version of how data needs to flow from point to point to inform better interactions and enable the experience vision.
- Identify the barriers and gaps that exist in capturing and passing data from point to point. This will inform data requirements that should inform technology platform and configuration decisions, as well as the DMP (data management platform) housing the data and where it should “live.”
- Come back to the experience layer and focus on engagement value (content, utility, social, etc.): knowing your data architecture means nothing until it is manifested in smarter, more personal, and more contextual interactions which is a creative exercise.
Trade has executed this work in the finance space and can guide you. It’s not a one size fits all approach but once you understand the required outputs to get to the right answer, it’s just about doing the work. Interested in learning how to get started? Please contact Jeff Blackman