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Customer Success 3.0?
What could be the next iteration? McKinsey established that there is consensus on the importance of customer success (CS). Moreover, many companies treat it as a direct sales channel, however both in a B2B as in a B2C context, especially in a rapid scale-up scenario, traditional approaches to customer success management will not suffice to meet customer expectations. Growing the internal CS department as fast as the client base would mandate it is often unrealistic and overly costly. The solution is to turn to modern tools.
Introducing Customer Success 3.0 : hyper-personalization and predictive customer success management.
Next-generation customer success management, and moreover, organization-wide prioritization of a memorable customer journey is a significant opportunity and the most powerful way to build lasting brand fidelity. This being said, meeting customer expectations is increasingly challenging. The majority of millennials expect real-time customer service, and three-quarters of all customers demand a consistent experience across engagement channels, of which there is an increasing number of, as brands seek to maintain a presence across the ever increasing number of social networks, for instance.
What must customer service leaders consider when evaluating use cases and legacy systems? Evaluating AI use cases, integrating new technology with legacy systems and finding the right talent or re-organizing corporate structures to fit new operating models is only a few of the relevant aspects to consider. COVID-19 accentuated the move to digital channels as a first point of contact and with the progress in chatbot technology in recent years, customers have more confidence in these self service channels. Their confidence in these channels, and their relative efficiency in solving relatively straight forward issues is leading them to expect similar workflows for more complicated requests. This suggests the need to more rapidly adopt conversational AI, proactive customer servicing and predictive logic to uplift the CS experience.
How does a mature AI-driven CS organization look like?
When looking at, for purposes of entering or simply evaluating customer service organizations, specifically gauging AI usage maturity, McKinsey differentiate five levels. While there are numerous nuances in-between one might think of, this is a useful guiding metric on where digital transformation focus areas could lie, when modernizing such organizations. The full article is quite insightful, enclosed below is the grading list:
Level 1: Manual and high-touch, based on paper forms and offered largely via assisted channels.
Reactive service, with the majority of interactions on human-assisted channels
Paper use is still prevalent
Level 2: Partly automated and basic digital channels, with digitization and automation of servicing in assisted channels.
Reactive service, with limited self-servicing opportunities
Lower adoption of available self-service channels
Lower availability of digital or straight-through-processing (STP)
Level 3: Accessible and speedy service via digital channels, with self-servicing on select channels and a focus on enabling end-to-end resolution.
Somewhat proactive, but limited engagement
Self-service channels such as mobile apps, interactive voice response (IVR) systems, and internet sites handle half of all interactions, and can support STP.
Level 4: Proactive and efficient engagement deploying AI-enabled tech, with self-servicing enabled by proactive customer interactions and conversational user experience (UX).
Proactive, with high customer engagement on digital channels
Self-service channels such as mobile apps, IVR systems, and internet sites handle 70-80 percent of interactions and can support most requests and transactions
Level 5: Personalized, digitally enabled engagement, bringing back the human touch via predictive intent recognition.
Engagement via service interactions that are personalized and proactive at the individual customer level
Digital touchpoints drive service-based engagement, for example via enhanced cross-selling and upselling
More than 95 percent of service interactions and requests can be solved via digital and STP channels
What would a mature organization's handling of an inbound customer service engagement look like? It starts even before customers reach out,. An AI-powered system can predict their likely needs and provide prompts for the agent. For instance, the system could alert the agent that the customer's credit card bill is unusually high, highlight minimum-balance requirements, and suggest payment-plan options. When the customer calls, the agent can not only address the immediate issue but also offer proactive support, strengthening the relationship and potentially preventing the need for future calls.
Also from McKinsey, a component view of this reimagined customer engagement would be:
Overall, while the AI-hype can be thought of in different ways, what should be taken away from this particular topic, keeping in line with next-gen customer requirements is this:
CS can profoundly impact a firm's business model, perception and ultimately is a core strategy element. In order to be successful, CS needs to be an organization-wide priority and needs alignment behind it.
Every customer touchpoint needs to be considered, whether digital or analogue, for improvement and automation
Every customer interaction needs to be approached in a maximalistic fashion. This is to deepen customer relationships, build loyalty and most importantly deliver value over the client's contracted lifetime.
Leverage AI end-to-end, in the firm's entire technology stack with the ultimate goal of moving the vast majority of customer interactions into self-service mode.
See one example on how Autodesk used IBM's AI offering below.
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