According to a recent Forrester report, conversational interfaces are expected to have a large impact on wealth management practices in the next decade.
Notably, no other emerging technology fell into the “large impact” category: anticipatory experiences, immersive experiences, digital identity, extended reality, and sensors were defined as having medium impact, while unstructured data’s impact is expected to be small. The findings were similar for banking, with only slight variation: anticipatory experiences and digital identity were rated as having medium- to-large impact, sensors having a small impact and extended reality as well as unstructured data having no expected impact.
As conversational interfaces pervade our personal lives via Siri, Amazon Alexa, Facebook Messenger and others, it’s no surprise people are warming up to the idea of sophisticated chatbots helping them navigate finances. What may be surprising is that the warming-up process is taking longer in financial services than for other industries, despite emerging use case successes: Today, 59% of US online adults are not interested in using robo-advisors for wealth management.
So, why hasn’t public opinion shown greater support? While this figure isn’t unusual for a new technology that affects customer and employee routines (we are creatures of habit, after all), it does leave something to be desired.
When the largest-impact technological opportunity is also met with customer ambivalence, it’s up to companies to build something that a customer may be reluctant to admit they need or would benefit from now, so they can be pleasantly surprised (dare I say, delighted?) by the experience when it’s available to them and embedded into typical activity.
Whether considering the advantages of expediting a fielded customer service issue that would have taken hours on a hotline to resolve or receiving automated alerts that provide instant insights into stock market trends and pricing, account balances and more, information that can be available in a snap – and, using natural language processing, done without even realizing a robot is behind the scenes – can make a time-constrained individual more financially literate and equipped for action amidst the ebb and flow of wealth management.
When it comes time to make decisions, allocate budget, and get to work creating conversational interface chatbots, the potential (and expected) impact should be sufficient motivation. Elevate your efforts from good to great with the following key points of focus.
Conversational Interface Advances and Challenges
- Don’t cut corners in order to be faster to market. My biggest caution for wealth management firms is not to cut corners when it comes time to build: A single negative interaction with a conversing interface can be costly and off putting to customers, further reinforcing the sentiment that they are a setback to customer service – lowering standards and cheapening exchanges of information – rather than an asset for bottom-line financial viability, smart talent resource utilization and customer experience (the need to have something when, where and how you want it). Alternatives must be readily available as part of a rollout or transition period, as well as a variety of checkpoints to offer additional assistance as the machine learning accrues.
- Human interaction is still critical. Relationships continue to be at the heart of wealth management (and banking, though to a lesser extent); introducing a new technology will not dissolve the association between a knowledgeable advisor and the reason(s) customers choose to invest with a brand. Assuring customers that chatbots and other AI-backed automation doesn’t impede but rather enhances their levels of service and options for engagement is a marketing and public opinion hurdle in and of itself, but with repetition and positive experience, the numbers will gradually tell a different story.
This type of offering – paired with human intelligence (logical, adaptive, and emotional), and communicated as such – will catch on with favor: In 2018, 26% of US online adults told Forrester they would be more interested in using conversational interfaces if there were human oversight. Organizations must use and develop tools to provide a better CX, more useful touchpoints, and transparency of choice to show customers that what’s driving a chatbot is the cumulative learnings of their business and foundational personal information needed to give users answers, fast. This boils down to good design across many layers of platform decisioning: moments of triage where an automated conversation can move smoothly to a human interaction to supplement transactional inquiries and ensure customer stewardship – especially for top accounts – the highest priority. Layers of human judgment, empathy and discernment enable relationships to be top of mind while freeing up broker time and attention to focus on more multifaceted client needs instead of FAQs or other service-related interactions.
- Context matters. When the backbone of a machine-only chatbot or a machine-augmented conversation thread is natural language, words (and other cues) matter more than ever. Moving requests into actions, finding relevant information, and understanding tone or an informally stated ask (aka slang) requires much machine learning. Building in the capability to understand written and spoken words adds greater complexity but more options for future application, as well, whether through a call center or through initiating 1:1 conversations before passing off to a human expert on the other line. Preparing scenarios and integrating with other customer systems to provide background on a customer as well as the history of inbound requests helps to formulate a dossier of context that AI can consume well before it begins to learn live through a chatbot or other customer-facing or employee-enabling platform.
There’s more to this conversation(al) interfaces, especially for financial services organizations. Let’s chat chatbots — and anything else on your mind. Reach out, and we’ll keep the momentum going.