Banking expectations among consumers are changing—quickly. Siloed multichannel experiences, once perceived as powerful and innovative, have given way to deeply personalized omnichannel options. In retail, that happened some time ago as consumers moved from either-in-person-or-online shopping to another model: viewing merchandise in stores and purchasing (or returning) it online and vice versa. Moreover that applies today no matter how they contact the retailer, including by phone or mobile. Companies must support this channel traversing while keeping sight of who the customer is beyond a unique ID—and more importantly their preferences, history and more.
Where conversational artificial intelligence comes in
Banks that implement conversational AI to power virtual assistants and chatbots are likely aware of this omnichannel expectation. Regardless, it’s a paramount to consider how this serves two complementary goals: 1) It saves time and money through efficiencies; and 2) It drives the design of customer experiences that increase brand loyalty through consistent, personalized engagements.
Meanwhile, conversational AI will continue to gain critical mass among financial institutions in 2019. While that’s good news, it also means many banks will struggle to get their arms around it. Here are five capabilities your conversational AI platform must support to create the omnichannel experience consumers expect:
Architecture that expands and scales
Two years ago, few if any foresaw that consumers would bank by voice with a speaker in their kitchen as a banking channel. As a bank it’s critical that you choose a conversational AI platform that not only supports your current website, mobile app and any third-party channels your customers want to use but can also expand to other channels not in use yet—such as banking in self-driving cars.
As your virtual assistant works within a blend of channels and third-party services, you also need a platform that’s “opti-channel,” meaning your conversational AI optimizes customer experiences. For example, Alexa, should not read aloud my last 15 transactions. Instead, she’ll send them to the virtual assistant in my mobile bank app.
Central data repository
Though user experience varies across channels, the underlying data is often similar for natural language processing and dialog management. As a conversation management system (CMS) and AI training tools manage data, they can leverage it to improve the experience across existing channels as well as bootstrap new ones. Data siloed in one channel cannot be used in others, which leads to missed opportunities in efficiency and experience. That’s a major shortcoming of multichannel implementation and only platforms that manage data centrally will apply data and learnings across channels. Sometimes, banks experiment with an emerging channel such as Facebook Messenger, Amazon Alexa or Google Home, and use its tools to create their virtual assistant. But when that happens, the learnings get trapped in the channel—meaning that any investment in data collection and training cannot be utilized website or mobile app.
Consistent customer experiences
Customers expect not only expect fluid channel movement but also consistent answers and experiences across different touch points. A conversational AI platform allows you to offer a consistent user experience at both the individual customer level and channel/cohort segments. A personalized experience for all “platinum” customers in the mobile app, for example, should align those in the website, messaging platforms and voice-enabled devices.
Problem-free customer journeys
A single, omnichannel platform makes possible sophisticated customer journeys that include cross-channel handoffs. For example, a customer who asks for a tax payment reminder can set the alert on Alexa, but get that message and make the payment on their bank’s app. As more consumers manage finances on the go, this ensures smooth experiences that serve them at key moments—and will drive brand loyalty in big ways.
Your platform must meet your risk, compliance and regulatory standards and procedures. A single platform simplifies approval processes since your bank can save on the time and resources to validate and approve conversational experiences. And once the platform is approved for one channel, it can easily get used on another without additional approvals.
Putting it all together: Leading through experiences
Though every bank already offers multichannel customer experiences, the smartest ones are readying even more sophisticated omnichannel ones that take customer experience to new levels. Make sure your conversational AI platform offers tools and application program interfaces that leverage your investment, let you deploy across channels, and manage content and data in a centralized, extensible way.
The resulting system will deliver huge operational efficiencies for your business, consistent friction-free customer experiences and growth potential over time. All it takes is channeling your intelligence—both the high-tech and high-minded kind.
Jeannette Kescenovitz, who leads development of banking-as-a-service at Finastra, joins us on the BAI Banking Strategies podcast to share her views on how BaaS might grow its presence at U.S. banks and credit unions this year.
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