1. Delight, don’t just deflect

Businesses should implement self-service with the strategic intent of not only deflecting phone calls but also delivering delightful self-service experiences for customers and building the brand. Poor self-service, purely focused on reducing live customer contact, can turn off customers. Here are some ways to delight customers through self-service:

• Provide multiple self-service options whether it is the interaction channel that a customer might prefer or the way they might want to use self-service to find answers—some might like FAQs, others might prefer natural language conversations, and some might prefer step-by-step guidance, for example
• Make sure to allow customers to escalate from self-service to human-assisted service without losing self-service context. Who wants to repeat the mother’s maiden name ad nauseum?!

2. Teach them to “fish”

You know the proverb “give a man a fish, you feed him for a day; teach a man to fish, you feed him for a lifetime.” Use technologies such as cobrowse to teach customers how to use self-service so they can help themselves when they come back for service the next time—this is like providing training wheels for kids when they learn how to ride a bike.

3. Self-service everywhere

Customers should literally “trip” on self-service. Make it easy to find and ubiquitous.

• Make it available at touchpoints where your customers “live”—web, app, IVR, and more
• When customers are on hold for human-assisted service, organizations can send a contextual self-service link to the customer, where they might find the answer, while assuring them they won’t lose their place on the call (or live chat) queue if they decide to try out self-service. Since most calls today originate from smart phones, this approach is an effective way to gently nudge customers to digital self-service.

The caveat: Your self-service better be contextual, correct, and consumable for this approach to work.

4. Don’t force it

While this is not a complete list, here are some common self-service blunders:

• Trapping customers in a self-service cul de sac or dead-end without allowing them to escalate to a human when they want to
• Trying to handle complex or high-stakes customer queries through self-service without assessing the organization’s self-service capabilities and maturity
• Continuing to push self-service when customer sentiment is going south in a self-service conversation
• Not preserving self-service context when the conversation is escalated to human-assisted service

Businesses should triage customer queries based on customer sentiment, nature of the query, value of the customer, and other factors. For instance, high-stakes queries on life-and-death matters or large and complex financial transactions could be routed to human experts whereas routine low-stakes queries can be routed to self-service first. As part of a unified omnichannel Knowledge Hub, AI can be used to triage customer queries at scale.

5. Trust or bust for AI

Gartner warns that 100% of AI projects for CX will fail without integration with a modern knowledge management system! 61% of contact center leaders and consumers are concerned about erroneous or inconsistent answers from AI, according to a recent KMWorld State of AI survey. Trust ignites user adoption, which, in turn, ignites business value. The opposite is true when answers cannot be trusted. Consumers and frontline employees associate “trusted” with correct, consumable, compliant, and contextual. A surefire way to deliver trusted answers is to implement a central hub for AI Knowledge, where conversational, generative, and agentic AI are backed by rich content management, pre-built connectors to trusted enterprise data and content, and analytics.

6. Leverage the right technology and technology partner

The building blocks of a modern self-service system include:

• Rich interaction capabilities unified in a conversation hub, supporting a comprehensive set of interaction channels—SMS, social app messaging, live chat, cobrowse, email and more, including support of channel-specific features (e.g., support of Apple Pay when a customer is interacting through Apple’s Messages for Business)
• AI Knowledge Hub (explained earlier). Moreover, an AI knowledge hub built on a composable, BYO architecture provides you with the flexibility to leverage your own bots, LLMs, and other sourcing and consumption points
• Analytics Hub to get insights on contact center operations and AI knowledge scope and performance (e.g., agent performance by individual or queue or the effectiveness and adoption of GenAI prompts)
• Generative and agentic AI capabilities to speed up the knowledge management lifecycle and further automate service—from delivering trusted answers to also driving trusted actions on behalf of the customer, all backed by the AI knowledge hub
• Technology partner with a proven AI knowledge implementation method and domain expertise to generate quick business value

eGain client success stories

• Specialized Bicycles, a pioneering leader in e-bikes, replaced their hard-to-use, obsolete knowledge management system for self-service with the eGain AI Knowledge Hub. The eGain hub now serves as the single source of truth, i.e., trusted content and process knowhow—across 21 languages! Self-service search success has improved 85% and the use of conversational AI for diagnostics has soared 18X since the deployment of eGain!
• Hypergrowth retailer was struggling to meet the soaring demand for customer service. They tried out eGain’s Virtual Assistant through the eGain Innovation in 30 Days™ program, a risk-free production pilot. Delighted with the experience, the retailer deployed eGain chatbots for multiple brands. The bots are resolving a wide range of shopper queries, deflecting customer contacts by up to 90%. Where needed, they escalate the conversation to agents, who can see the full self-service context in the eGain Advisor Desktop™, to seamlessly move the conversation forward.
• Leading omnichannel retailer is automating and augmenting customer service with eGain, leveraging digital self-service, messaging, chat, and IVR deflection to digital service, all backed by the eGain AI Knowledge Hub™, to handle over 9 million customer contacts per year. They are deflecting 45% of phone contacts and 30% of IVR contacts with digital self-service and chat messaging, delivering joined-up omnichannel service with context-aware escalation to agent-assisted service.
• Large federal government agency achieved groundbreaking results after adopting the eGain AI Knowledge Hub. They were able to divert up to 70% of incoming calls to AI virtual assistants, cut case handling time by 25%, and streamline online form-filling with AI knowledge assistance. These impactful improvements boosted agent engagement to 92%, well above the industry average of 67%.

Conclusion

Done right with the backing of a trusted AI knowledge hub, self-service can help slash customer service cost dramatically while scaling service cost-effectively and elevating the brand.

Gartner Critical Capabilities Reports offer deep insights into vendor offerings. Per Gartner, “Critical Capabilities Reports offer comparative product and service research based on rigorous analysis and are backed by highly structured methodologies.”

Here are our scintillating scores:

Knowledge management: 4.8/5.0
Digital engagement: 4.7/5.0
Automation of engagements: 4.5/5.0
Composability: 4.2/5.0

Are you looking to pick the best AI knowledge platform for 2025 and beyond? Gartner has already done the heavy lifting for you! Talk to us now to learn about our easy acquisition and adoption models:

eGain SafeSwitch™: Stuck with legacy KM? SafeSwitch is a unique escape plan. Learn more!

eGain Innovation in 30 Days™: Guided innovation pilot for our solution. No-cost, easy, risk-free. Learn more!

Gen AI helps KM

Gen AI accelerates each step of the KM lifecycle on a modern knowledge platform.

• Discover: Identifying likely questions is the first, often-ignored, step to an effective knowledge base. Gen AI can extract questions from interaction history using best-practice, contextualized prompts in the knowledge platform.
• Create/curate: Gen AI can draft knowledge content, using long-form, complex documents, and other enterprise sources. It can adjust content for brand voice, interaction channel, and consumer persona. Finally, it can propose knowledge taxonomy based on question patterns and user profile.
• Deliver: Enterprises have boatloads of documents with “correct” content, but they are not “consumable” by users. With an irate customer on the line, no agent wants to read a tome! Gen AI generates consumable answers, referencing multiple knowledge articles and documents.
• Optimize: Knowledge must be measured and managed for business impact. Gen AI identifies knowledge gaps in accuracy and ease-of-use, suggesting alternatives for improvement.

KM helps Gen AI

Gen AI can transform business, but it also poses significant risks. Among them are hallucination, loss of trust in answers and in the technology itself, knowledge fragmentation, and compliance risks. A modern KM system implemented as a hub—like the eGain Knowledge Hub—helps safely operationalize gen AI by offering:

• Trusted content: KM can ensure that correct data and content are used to feed and train gen AI. Without this foundation, the initiative is likely to fail with disastrous consequences for the brand.
• Controls and governance: A modern knowledge hub comes with controls to determine which queries to process with gen AI and which ones not to. The knowledge manager can control its “creativity,” as the situation warrants and configure additional accuracy checks.
• Closed-loop analytics: KM provides insights and actionable recommendations on the use, effectiveness, and improvement of gen AI actions.
• Process orchestration: Gen AI is an exciting building block of KM, but not the only one. A knowledge hub also includes other AI technologies like reasoning and machine learning, plus critical components such as content management and conversational guidance. The hub orchestrates these capabilities to deliver effective journeys to agents, business, and customers.

Conclusion

The symbiosis between Gen AI and KM is powerful. Without robust KM, Gen AI remains a prototype. Without Gen AI, KM struggles with building and maintaining knowledge in a fast-changing operation.

Related links

1. Knowledge Management and Generative AI Transformation
2. Webinar | ChatGPT and Generative AI for CX
3. The Best Use Cases for Generative AI in Digital Customer Service
4. Harnessing generative AI for customer service: KMWorld webinar
5. Generative AI For Customer Service: Best Practices For Success
6. Blog: 10 Use-Cases for Leveraging Generative AI for Better CX and AX (Agent Experience)
7. Try generative AI for customer service
8. eGain Knowledge Hub
9. eGain AssistGPT

Premier IT analyst firm Gartner has the answer. According to them, knowledge management (KM) is the #1 technology that simultaneously improves operational performance, customer experience, and employee experience, and we agree. Here are a few examples from our blue-chip clientele of how KM helps slash costs. They are all focused on the customer service function but if also leveraged by other business functions, KM can help reduce costs across the entire enterprise!

1. Call deflection

Self-service helps deflect incoming calls and requests for human-assisted digital customer service (e.g., via messaging, chat, social networks). But without accurate knowledge that is consolidated in a unified hub and optimized with analytics, self-service will do nothing but trigger rage by the time the customer gets to a human contact center agent. With knowledge technologies such as AI reasoning, you can do much more. Reasoning enables a conversational approach and helps solve more complex customer issues with self-service. Knowledge-backed self-service is a powerful tool to scale customer service quality while containing costs.

• Media and legal services giant deflected 70% of requests for email and chat customer service with knowledge-powered self-service
• Hypergrowth digital-only retailer deflected up to 90% of their incoming requests for human-assisted digital service with knowledge-guided digital self-service, including virtual assistance, across multiple brands

2. First-Contact Resolution and Average Handle Time

Consistent knowledge, delivered in the flow of customer conversations from a centralized omnichannel hub of trusted knowledge and knowhow, reduces repeat calls and improves the seemingly conflicting metrics of First-Contact Resolution (FCR) and Average Handle Time (AHT). The increased effectiveness and efficiency reduces the need to hire new agents, helping control or even reduce service costs while improving CX.

• A leading global bank increased FCR by 36% and reduced AHT by 67% by arming frontline agents with agent assist technology, powered by the eGain Knowledge and AI hub, in their B2B contact center
• A leading telco improved FCR by 37% across more than 10,000 agents and associates in more than 600 retail stores, while boosting NPS (Net Promoter Score) by 30 points, with AI-backed agent assist technology. They also used the flexible guidance capabilities of eGain’s agent assist AI technology by providing conversational paths personalized to the agent’s experience level and past performance. For example, expert agents would be allowed to take short cuts to the answer, taking efficiency and cost control to the next level without compromising service quality

3. Training cost reduction

Training new employees in customer service or any other function in an organization is not cheap, with U.S. companies alone spending as much as $92.3 billion in 2021, according to Training Magazine’s spending report. Moreover, it is a big challenge to train millennials and Gen Z, who constitute the majority of the contact center agent workforce. They have very short attention spans (12 and 8 seconds respectively, according to cultural intelligence firm sparks & honey) and they hate traditional classroom training. Moreover, humans forget 75% of new information they learn just after two days, according to the forgetting curve theory of German psychologist Hermann Ebbinghaus. In fact, research by the University of Waterloo found that number to be whopping 97% after just 30 days. Clearly, the solution to this challenge is knowledge and AI-enabled guidance delivered contextually to agents (and other employees) in the flow of their work.

• Leading telco client reduced training time by 50% across multiple thousands of agents with knowledge and AI guidance
• Leading health insurance client reduced agent training time by 33% and sustained agent performance even when their 2000+ agents had to go remote overnight when COVID hit.
• Global banking client reduced agent training time from 10 weeks to 4 weeks while being compliant with regulations.
• Miscellaneous professional services client reduced new employee training time for frontline services from 7 weeks to 1 week!

4. Hiring and onboarding costs

According to industry experts, the true cost of hiring new employees that includes not only the hard costs of placing ads and/or referral fees but also the cost of scanning resumes, interviewing, and selecting candidates, can be three to four times the position’s salary (Source: SHRM). The average salary of a contact center agent in the US is $33,000, per Glassdoor. That means the true cost of hiring an agent is close to $100,000!  When agents are armed with knowledge and AI guidance, they are happier and more confident, resulting in lower churn and lower hiring and onboarding costs.

• With the eGain Knowledge and AI hub, a hypergrowth B2B software company was able to speed up time to answer by 67% and boost agent confidence by 60%
• Mammoth government agency improved agent engagement to 92% versus their industry benchmark of 67% with eGain Knowledge

5. Product returns and exchanges

No-charge product returns or exchanges has become standard policy in many branded manufacturing firms, retailers, and telecoms due to customer expectations and competitive pressures. Called No Fault Found (NFF), many of these returns and exchanges are unwarranted where the products were not defective, but the contact center could not solve the customers’ problems. NFF costs organizations tens millions of dollars each year, but KM and AI guidance in the contact center can address this issue head on.

• Leading telco reduced unwarranted ‘No Fault Found’ handset exchanges and returns by 38% through knowledge-guided problem resolution in the contact center

6. Field visit reduction

Field service is common in industries such as utilities, manufacturing, and CSPs (communications service providers). Depending on the industry, each truck roll or engineer callout for field service can cost organizations from a couple of hundred to a few thousand dollars. Oftentimes, these field visits can be avoided through more effective problem resolution by contact center agents with knowledge guidance.

• With the eGain Knowledge and AI hub deployed in the contact center and on the website, a water utilities client saved ~$5M per year by reducing unnecessary engineer callouts, while improving FCR by 30%
• Premier manufacturer of household appliances saved $50M a year by reducing unwarranted truck rolls through knowledge-enabled problem resolution in the contact center

7. Search costs

The knowledge deficit extends beyond the contact center. For example, did you know that workers across the enterprise spend 19%-35% of their time looking for information, knowledge, insights, or expertise during their workday (Source: McKinsey and APQC.) This is not even counting the time they spend in recreating knowledge that they are unable to find or the time it takes them to chase experts and get answers from them. Imagine cutting even a fraction of that wasted time and money across your entire workforce with a knowledge tool that helps them find answers quickly or guides them through an unfamiliar process or helps them with decisions.  The search costs saved and the business value generated will be nothing short of transformational!

Conclusion

The benefits of AI-infused and analytics-optimized omnichannel knowledge, delivered from a central, trusted hub, are clear and proven. When it is time to cut costs, it is time to deploy a modern, AI-infused knowledge hub!