- Customer self-service support channels are designed to automate low-variability, high frequency cases. Research by IBM shows up to 80 percent of routine customer service questions or problems can be resolved with self-service tools.
- Call centers move high volume, low complexity cases to self-service channels to increase support options and drive down call center costs.
- Despite broad adoption, analyst research shows that most customer self-service tools fail most of the time. However, there are mitigating steps to prevent failure and achieve success that delivers very high payback in the forms of increased customer satisfaction and lower cost to serve.
Customer self-service is any support performed by the customer and without the assistance of company staff.
Customers increasingly seek self-service as the first source to solve their questions.
According to Forrester, there has been a 12 percent rise in web-based self-service, a 24 percent increase in chat and a 25 percent surge in community forums in the past three years. Forrester also advises that 67% of customers report that they regularly use online self-service support.
The Benefits are Compelling
The three overarching benefits of online self-service are to empower customers to help themselves, lower contact center cost and elevate the agent experience.
- Accommodating customer preferences is essential to grow customer lifetime value (CLV) and retention. Most customers prefer self-service options. A Nuance Enterprise research survey found that 67 percent of customers prefer to use self-service options instead of speaking with an agent. Failing to deliver a customer service wanted by more than half your customers is more than a missed opportunity, it's a disregard for customers.
- Reducing contact center costs is a continuous journey, and one made easier with online self-service technologies. According to a Harvard Business Review (HBR) post, when customers can resolve their own questions and problems, it costs orders of magnitude less than having a support staff do it. The research advised that "Self-service offers companies a tantalizing opportunity to reduce spending, often drastically", and that while the cost of customer self-service transactions is measured in pennies, the average cost for live service cases is more than $7.00 for B2C companies and more than $13.00 for a B2B companies. Similar research from analyst firm Gartner and their Customer Service and Support Leader Poll found that live support channels such as telephone, webchat and email cost an average of $8.01 per contact, while customer self-service support channels on the company website or from a mobile app cost an average $0.10 per contact.
- Improving the agent experience is a prerequisite to improving the customer experience. Customer support representatives grow tired of repetitive, monotonous cases. Fortunately, these are the perfect types of cases for customer self service support. Using customer self-service technologies to complete the high volume of low complexity cases elevates agents to assume escalations, exceptions and tackle the more complex incidents.
Why Customer Self-service Fails
The benefits are clear. The path to success less so.
According to Rick DeLisi, VP of Gartner's Customer Service and Support group, only 9 percent of customers report successfully resolving their cases with self-service technologies. He shares using these technologies, "to offer more choice in their service experience sounds like a great idea, but in fact, it has unintentionally made things worse for customers."
These failures happen when call centers overemphasize the cost savings goal at the expense of customer satisfaction and case outcomes. They take an approach to implement more channels rather than fewer more effective channels, and implement channels as standalone 'bolt-ons' rather than a coordinated universal platform. It's important to recognize customers frequently switch channels and a disconnected experience is a failed experience.
Another very common cause of self-service failure is outdated content. Products evolve with new versions or models. Sources of problems incur root cause diagnosis and get fixed. When support content doesn't keep up and customers are presented outdated articles or answers, or the correct answers are buried in a sea of obsolete information, the self service fails.
Customer support content must be periodically reviewed and potentially refined or removed. It's also important to measure content usage. If customers ignore certain content, or content receives low customer satisfaction (CSAT) scores, the content should be upgraded or retired.
Technologies to Help your Customers Help Themselves
Achieving success starts by applying the right tool for the job. The four primary self-service technologies are FAQs, knowledgebases, chatbots and communities. Each has its advantages and disadvantages.
Frequently Asked Questions (FAQ) are the most widely adopted online support channel. They are most often a list of commonly asked questions and answers published on the company website. They are easy to create, and if well designed, are easy for customers to consume. Customers that have the need for speed appreciate curated answers to common questions.
This self-service tool is best used by small businesses or call centers deploying their first online customer support channel. It's also a good tool when the bulk of customer questions can be easily answered with a small number of responses.
FAQ success factors include coverage for the most frequently asked questions, logical groupings with thematic headings, a search function that indexes files and matches keywords, and mobile support. Some FAQ pages offer Natural Language Processing (NLP) for voice searches. But FAQs are not meant to become voluminous, so NLP is generally a better fit for tools such as chatbots.
It's also important to recognize that customer questions evolve, so FAQs must be verified and updated periodically.
Knowledge management systems create, publish and distribute curated information in a variety of forms. They store and manage a collection of structured and unstructured knowledge artifacts, such as articles, manuals, technical documents, images and videos. They leverage search tools to find answers and retrieve content.
Knowledgebase capabilities support the creation, management and retrieval of support content. That includes things like content authoring and version control, content taxonomies and tagging, metadata management, workflow processing, localization and federated search. Many use AI to deliver more accurate and personalized search results.
They are designed to be used by customers for self-service or by internal staff such as customer support representatives to aid their job performance. When made available to broader audiences they can harvest more information from more people.
Popular CRM systems such as Microsoft Dynamics 365 and Salesforce have knowledgebases built into their customer service clouds. When the CRM software has knowledge management, it's integrated with the case management functionality and advanced tools such as AI. It also results in contact centers having to manage one less point solution and avoid yet more system integration.
The single biggest challenge with knowledgebases is maintenance and keeping the content current. Over time they collect vast amounts of content. If irrelevant or obsolete content is not removed, it degrades the search functionality and clouds search results. If users can't quickly find accurate information because its buried among out of date content, the knowledgebase fails.
A chatbot is a software application that uses online text or speech to mimic human conversations and resolve customer questions and problems. They use natural language processing (NLP) technology to converse (the 'chat') and process automation to fulfill requests or complete tasks (the 'bot'.)
Some basic bots simply apply keyword searches or deterministic dialogues to deliver basic responses. Other more intelligent chatbots are trained on customer support transcripts and built on NLP and AI to engage in more sophisticated dialogues and deliver more accurate, contextual and personalized responses.
Unlike knowledgebase searches, chatbots generally offer more intelligent and contextual searching. They leverage complete sentences instead of just keywords, ignore poor punctuation and grammar, and can pick up on customer sentiment. And they deliver a single best answer rather than pages of possible answers. Or if they cannot find an answer or become confused, they escalate to a live agent.
Millennials show a preference for chatbots and virtual agents. Bots are also well suited for contact centers supporting sophisticated products and customers. They can better accommodate more complex queries and deliver more precise answers than FAQs or knowledgebases.
Peer-based online communities or forums are most often informal groups of company customers that help each other. Participation and peer-to-peer user generated content generally revolve around common interests such as products, problems or solutions. Participants collaborate and learn from each other.
Community curated and generated content extends the reach and depth of company sponsored content. Some online communities exist on social networks. Others may reside on a company provided website. This later approach is ideal for companies that want to harvest customer content and insights or integrate communities with other support channels.
One More Thing
Artificial intelligence is one of the 5 most important contact center technologies and it may be applied to any of the customer self-service options for improved results. In fact, CRM software leader Salesforce published in its Salesforce's State of Service report that "Three-quarters of AI pioneers are automating routine service tickets completely, thereby giving agents the bandwidth to focus their attention on customers' unique, complex issues and redefining their roles as strategic client advocates."
The company's research found that more than half (51 percent) of agents at contact centers without AI spent most of their time on monotonous tasks. That figure fell to about one-third (34 percent) for contact centers that use AI.
Other similar technologies can also deliver improved results. Machine learning can create customer support predictive analytics to deliver next-best-action recommendations to agents or customers. Augmented Reality (AR) technology can overlay digital images onto 3D workspaces to guide customers or field service staff with visual instruction in a hands-free setting. Call center speech and text analytics can leverage structured and unstructured customer data to detect and change responses based on customer sentiment.