Amid the growing demand for more flexible and efficient customer support offerings, self-service is emerging as a cost-effective approach to delivering on-demand resolutions at scale. Companies are already investing heavily in self-service support tools, with the market projected to surpass $57 billion by 2030. However, many support leaders are observing results that don't live up to the hype. One recent report found that only 14% of customer issues are fully resolved by self-service.
These results emphasize an important reality of any new software implementation: software, on its own, is rarely transformative. To make a true impact on the customer experience—and deliver tangible business value—it must be supported by clear goals and a data-driven strategy. This can be challenging in the context of customer service. The diversity of customer preferences, and the various nuances and complexities of their support requests, make it difficult to implement a single strategy, or a "one-size-fits-all" approach, to support.
Organizations that attempt to fully replace their existing support infrastructure with self-service are likely to find that the approach is neither broadly effective, nor sustainable in the long-term. Instead, self-service must be viewed as one tool within a larger ecosystem of support channels and strategies, expanding, rather than limiting, the available paths to resolution. If companies hope to increase its appeal over alternative offerings, they'll need to take key strategic and operational factors into account, such as:
Common barriers to self-service support
Many customers turn to self-service for a more streamlined support experience, but even among younger generations, live phone support still holds strong appeal. One financial services company found that its phone support offerings were almost equally popular among Gen Z and Baby Boomers, and strongly preferred by premium customers across age groups. Comfort with the familiar may be influencing these preferences, but solution relevance is likely a more significant contributor. Lack of relevance is cited as the most common reason self-service support fails, suggesting automated systems may struggle with contextualization, personalization, and other skills that come easily to seasoned support agents.
Sometimes, however, support channel selections are influenced more by the customer's level of technological proficiency. Less tech-savy customers may struggle to navigate a company's self-service portal, or to articulate their challenges in language AI chatbots can understand. They may also have difficulty troubleshooting independently or identifying the best solutions from a list of AI-generated suggestions.
In designing self-service support journeys, companies must prioritize simple, intuitive UI that feels accessible to users at all different skill levels. Achieving this often requires continuous testing and refinement of everything from chatbot conversation flows to the clarity of knowledge base content. It’s also important to recognize that, for a variety of reasons, some customers may not trust or feel comfortable relying solely on self-service solutions. For these users, providing a clear and easy path to escalation, and a visible option to connect with live agents on the channel of their choice, is essential.
The role of live agents
While there are many factors that contribute to "good support," companies that demonstrate empathy, authenticity, and responsiveness have an opportunity to differentiate themselves from competitors. This can be difficult to pull off when a company relies heavily on automation, leading to homogenized support experiences across brands and missed opportunities for engagement, deep personalization, and the type of above-and-beyond service that drives customer loyalty.
At minimum, live agents should be available for escalations and exception handling, and prepared to deliver tailored services for the most complex and sensitive cases. However, involving agents throughout the support journey can significantly improve the efficiency and effectiveness of issue resolutions. Human agents that regularly update knowledge bases, flag outdated suggestions, and provide feedback on chat flows, for example, help minimize friction for self-service customers. They can also do this by recommending improvements to automated services based on observed trends, such as emerging challenges or workarounds with a high success rate.
To maximize their impact, agents should be encouraged to monitor support portals for opportunities to intervene in anticipation of customer needs. This can be particularly valuable if they recognize a request from a high-priority customer or a customer dealing with a notably complex issue. Proactively offering customers the option for live interaction and more personalized support is a powerful way to augment AI services with a human touch, resulting in a more memorable and valuable customer experience.
Self-service vendor relationships
By 2027, support executives are anticipating a 53% increase in their AI usage for self-service purposes. However, most recognize that they will be relying heavily on external vendors to meet their technology needs, with 86% planning to outsource digital assistance for field service and 81% planning to outsource customer communication.
As part of this approach to AI in support, careful vendor vetting strategies should be implemented to preserve the cost savings and productivity gains self-service is intended to deliver. Decision makers should be wary of contracts that lock their teams into long-term subscriptions, particularly if those subscriptions are accompanied by annual price increases. They should also take the time to investigate potential integration challenges. If AI-powered self-service tools don't interact well with the company's knowledge base, analytics platform, and other critical support applications, users could be forced to contend with inaccurate or outdated information, while employees work to configure manual workarounds.
From a security perspective, robust data privacy protocols are especially crucial in the context of customer support. Hoping to improve the relevance and accuracy of AI-generated solutions, customers may be inclined to provide sensitive or personal information in self-service portals—or offer up more data than necessary. This is why it's essential for companies to assess their AI vendor's policies for storage, usage, and ownership of the data shared with its models. Doing so will enable more secure interactions between customers and self-service systems, and prevent incidents that could undermine trust at an often-vulnerable point in the customer journey.
What sets self-service apart
Before implementing any new system, it’s essential to establish KPIs and other measurements of the technology’s success. For customer support operations, this will typically include standard metrics, like CSAT score and time to resolution. However, companies can benefit from expanding the scope of their metrics, and sharpening their focus on the novel ways self-service enhances existing capabilities.
For instance, with self-service available 24/7 across regions, organizations might track increases in global resolution rates. They may also choose to examine the cost per resolution, reduction in support ticket volume, and repeat usage of self-service channels by customers. These types of metrics can help companies identify the gaps self-service is filling in their existing operation—and the factors that differentiate it from other support offerings. Equipped with these insights, companies can better position and promote their self-service offerings, while also identifying opportunities for continuous improvement, enhancing the long-term value of self-service for customers and support teams alike.
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