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THE

ESSENTIAL GUIDE TOAUTOMATING CUSTOMER SERVICE

CONTENTSIntroduction

The Benefits of Automation

The Automation Landscape

How Automation Increases Value at Each Support Touchpoint

Integration of Machine Learning

Are You Ready for Automation?

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INTRODUCTIONWe live in a world where one click can get you anything you want: an answer, an app, or the album you just heard on the radio. This always-on, get-my-information-from-anywhere environment is raising the bar higher and higher for customer experiences across the board, and support is no exception. Getting an answer to their question is the primary customer goal.

Customers want their answers now and will not accept being passed around, no matter what your escalation strategy dictates. In fact, studies show a full 32% of customers switch companies because they are fed up with speaking to multiple agents. They want the right person, with access to the right answers, at the moment they need help.

Customers also expect to be heard and acknowledged, to be treated with the utmost care and personalization, and to receive responses promptly. And why shouldn’t they? So, for a customer service team to stand out, the question then becomes: how do we do this faster and better than anyone else? Customer service teams are expected not only to react to requests and questions coming their way, but to also proactively anticipate customer needs. In this era where support teams are being asked to do more with less, smart automated processes can enable support agents to deliver great customer support even more efficiently. In fact, it’s quite likely that agents are already employing simple automation techniques, such as using spam detection and business rules in their routing workflows or by referring to knowledge base content and using response templates in their interactions with customers. These agent-created automated shortcuts help make them more productive, but that is only the tip of the iceberg for how automation can enhance your agents’ and customers’ experiences.

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5 KEY BENEFITS OF AUTOMATION

1. ELIMINATE REDUNDANT, REPETITIVE WORK

2. IMPROVE CONSISTENCY

Automation is about using technology to allow processes to run seamlessly, without manual intervention. Contrary to the assumption that business automation is intended to replace human resources, automation simply assumes responsibility for administrative and repetitive tasks, allowing employees to focus more intently on the areas where human intuition and human touch are truly indispensable. Automation is especially valuable in organizations where resources have been capped and headcount is a highly-guarded commodity. Here are five ways support teams can reap the benefits of automation:

Support teams are often bogged down with repetitive requests. Rather than spending valuable human energy answering the same question over and over, automation in the form of a knowledge base can enable customers to find answers to commonly asked questions without tapping into human resources. Macros that help agents effortlessly respond to customers and intelligent systems which route tickets to the most appropriate agent also eliminate redundant tasks, freeing up time and energy for support agents to focus on more high-level priorities.

For lack of time or direction, it’s common for reps to build their own support “cheat-sheets” (either individually or shared among an entire support team). This cobbled-together patchwork of information becomes increasingly un-useful—and potentially detrimental—to the customer support experience as resolution processes evolve and change, seasoned support representatives leave, and customer service teams expand. Automation allows tribal knowledge to be properly recorded, added to, optimized and easily referenced across an organization.

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3. SET A STRONG FOUNDATION FOR FUTURE SCALING

4. INCREASE SUPPORT TEAM HAPPINESS

5. IMPROVE CUSTOMER HAPPINESS (REDUCING CHURN AND INCREASING ROI).

Many support teams start out small—leveraging a few key documents and their combined intelligence to resolve issues, educate customers and inform the rest of the organization of their progress. Then, as the customer base and support ticket queue grows, time-intensive, manual processes deliver less-than-perfect results. Automation is critical for companies who want to safeguard their hard-earned knowledge against employee attrition and build a customer service team able to scale as the company grows.

The life of a customer service representative can be tedious and stressful. Empowering support teams through automated knowledge bases and personalized issue resolution guidance increases agent satisfaction. Automatically routing tickets to agents with certain skillsets, personality traits and performance indicators shows that you value their individuality and are equipped with the proper data sources to ensure their success. In short, customer support automation recognizes individuality and empowers teams with the resources to be proactive rather than reactive.

What makes customers unhappy? Customers crave a comprehensive, intuitive and engaging experience. They know you have their data and they are judging your organization on how well you use this data to create a personalized customer experience. Chat agents that respond out of context with their questions, online agents that don’t seem fully educated on the company, its issues and operations, inconsistent service, and being passed from one representative to the next will quickly lose your customers’ goodwill and business. In fact, according to Forrester, 92% of companies surveyed reported a decline in customer satisfaction from consumers most disappointed by inconsistent service.

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Automation in ticket routing and aided response can increase an agent’s ability to provide a personalized, engaging customer experience. According to Harvard Business Review, assessing the personality type of a customer at the beginning of a call can reduce repeat calls by 40%. When a company can identify with a customer’s unique needs, it illustrates a high value to both customers and employees.

SIMPLE METRICS CAN’T DEFINE WORLD-CLASS SUPPORT“Some of the things we don’t really care about or don’t talk about with the team... are traditional call center metrics like average handle time [which is] how long someone is on the phone…it doesn’t speak to the quality of the experience, it doesn’t speak to if the customer is satisfied.”

Eventbrite VP of Customer Experience, Dana Kilian

THE AUTOMATION LANDSCAPEAs organizations begin to explore new options for customer service efficiency, a primary consideration is exactly how automated they want to be -- both from an internal process standpoint and customer-facing perspective. Balancing risk and reward is critical when deciding what level of automation is appropriate for your customer service approach. Just as no manual process is perfect, no automated process is perfect either -- even those that learn over time. However, as Figure 1 describes, by assessing your customer service processes in terms of the complexity of the work and the pain associated with a wrong action, a picture can quickly emerge for where automation will help the most.

Within the automation landscape, there are three distinctive and increasingly sophisticated levels of analytical complexity -- descriptive, predictive, and prescriptive analytics. Each of these can be used across the risk-reward balance to help customer service teams achieve better outcomes.

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Descriptive AnalyticsDescriptive analytics summarizes data from past events to tell you what happened. The most common automated descriptive analytics approach is do-it-yourself business intelligence, in which a customer support manager can automate the analysis and presentation of data for internal company usage. These kinds of automated reports frequently focus on a range of operational and customer-facing metrics, such as average response time, ticket volume, and customer satisfaction ratings.

In this do-it-yourself analytics space, the analysis and presentation are automated, but decisions and process are not. As a result, this type of analytics and automation tend to be very low-risk, as the output is typically used only internally and not shared with customers.

Predictive AnalyticsThe next tier of automation is based on predictive analytics. Predictive analytics builds models from existing data to predict what is likely to happen based on past behaviors. While a human may still be involved, predictive models are setup to look at patterns in various data inputs and provide insight into the data.

This can be as simple as a routing rule that sends tickets to a designated queue based on a particular keyword. Or, it can be as complex as analyzing all of the interaction and demographic data about a customer, along with performance data for each support agent, in order to determine the best match for that particular customer, at that particular point in time.

FIGURE 1: DECISION MATRIX FOR AUTOMATING CUSTOMER SERVICE

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At this level, data helps inform processes. But, the sheer amount of data absorbed every day by a support team—and an organization as a whole—makes it a challenge to synthesize and use it to make better, more informed decisions. From looking up templates based on text similarity, to predicting how to route a ticket, or even how a customer is going to react, automation through predictive analytics can be helpful to reduce or eliminate manual processes in lower risk interactions -- both customer-facing and internal..

Prescriptive AnalyticsThe top tier of automation helps support teams make decisions from data. It leans on prescriptive analytics, which goes beyond descriptive and predictive models to not only collect data and predict outcomes, but actually recommend one or more courses of action. While descriptive analytics can help create a report, prescriptive analytics suggests what to do next from the data.

This advanced level of automation help agents personalize service by telling them what response they should give to drive action to a customer (i.e. why are they about to churn, what product is the right one to recommend). It can be used to route tickets to the right agent, suggest the best response, and deflect tickets to the best knowledge base article.Channel specific approaches like virtual chat agents embrace this level of automation by responding to a customer based on scripts developed from historical data.

While prescriptive analytics is nearly completely customer facing, it can be tuned to align with the needs and value of automation for the organization. At its simplest level, it can recommend a correct response and still allow a human decision. Or, it can be tuned to completely automate the customer engagement. Even then, support organizations do not have to depend on a black box answer. Recent advances give even more insight into why a particular course of action is being suggested, which helps with adoption and usefulness of predicted actions.

HOW AUTOMATION INCREASESVALUE AT EACH SUPPORT TOUCHPOINT

Forward-looking organizations know that each support touchpoint represents an opportunity to create value. The advantages of an automated support process can be recognized by both customers and companies from before a request for support is initiated to issue resolution and beyond.

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AUTOMATION AT WORK: BEFORE A TICKET IS SUBMITTED

Emma Age 57 From California Is an established mobile customer

Emma has a problem with her new mobile phone. She’s having trouble activating it. Her granddaughter suggested she look at the company’s website for self-help options. When she arrives at the support page she is provided with a field in which she can type her question “how do I activate my phone?” The results include:

• An article titled: 3 Steps to First Time Phone Activation • A video title: Learn to Activate Your Phone

What Emma thinks: This company gets me! And I don’t have to waste time talking to an agent.

Benefits to the company: Commonly asked questions and easily answered issues are resolved at the self-help level, while the more difficult issues and/or delicate customers are provided one-to-one care. An automated self-help model:

• Optimizes care center resources • Builds customer loyalty and brand satisfaction • Increases ROI in terms of support center costs

Before the customer submits a ticket.At this phase, a customer is having an issue and wants a solution. If your team is like many support organizations, you’ve accumulated a repository of questions and answers that comprise the vast majority of the support issues your agents face and you’ve put all that information in a knowledge base for your customers. Here, automation can proactively walk the customer through the solution (e.g., WalkMe), or it can recommend a good knowledge base article as the customer starts to request help.

But considering that many customers, particularly those online, will simply abandon the effort when they face a problem, proactive support is critical and necessary. Automation can identify which customers are likely having an issue—based on usage, transactional or other engagement and demographic data—and can better allow the support team to reach out to the customer in real time, and in the channel in which they’re engaging. This not only improves self-service by customers, but also positions your support department as highly responsive, while still giving your team extra time to follow up if necessary.

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AUTOMATION AT WORK: WHEN A TICKET IS SUBMITTED

Tom Age 68 From Idaho Customer who has used the service for 10+ years

Tom has a problem with his new phone. The battery keeps dying within an hour. He isn’t even sure that he wants this fancy new phone and is thinking about returning it. He emails the support center and answers a few questions describing his technical issue. Data points such as age, demographics, and Tom’s long history with the company establish him as a VIP customer and lead to a prioritization of his case. Tom’s email is automatically routed to Roy, a customer service agent who’s proven particularly adept at handling battery issue calls from customers like Tom. Roy is able to immediately review Tom’s issues and respond back immediately.

What Tom thinks: That customer service was lightning fast.

Company Benefits: Customers care about speed of response and accuracy of resolution. By reducing the time it takes to get the support case to an agent and ensuring that the right agent receives it, customer satisfaction, retention and revenue start to climb.

When the customer submits a ticketNot everyone is into self-help. There will always be customers who prefer to get assistance through email-based support, rather than spend time on the phone. Automation can smartly ask the customer questions or trigger actions based on the ticket to reduce response time and improve routing once the ticket comes in.

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Situation:A service focused website helps millions of people tackle their to-do lists everyday by connecting them with qualified professionals. As their business grew, so did their number of support tickets from both individuals and professional providers. To meet the support demand, they built up a large international team to provide the round-the-clock service that their customers came to expect. Part of that international team consisted of 10 agents whose entire responsibility was to triage incoming tickets. However, because of a range of issues, including language, weather, and category confusion, as well as a constantly growing volume of tickets, that triage was frequently a bottleneck. It often took up to 8 hours to triage a ticket, leaving customers or merchants waiting before the right agent could even begin to respond.

Solution:Wise.io implemented a machine learning application to gather insight from hundreds of thousands of tickets and seamlessly integrate into their Salesforce Service Cloud® instance to introduce an automated triage solution within a matter of weeks.

Results:Triage times went from the previous 8 hours to less than 5 minutes, enabling the company to reallocate those triage resources into other parts of their organization where they could better serve customers.

CASE STUDY: AUTOMATION REDUCES TRIAGE TIMES FROM 8 HOURS TO 5 MINUTES

FIVE MINUTES IS THE LONGEST THAT 43% OF US CONSUMERS WILL WAIT ON HOLD WHEN ENGAGING WITH A BRAND. HANG UP

5:00

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When an agent responds to a ticket.At this touchpoint, automation can recommend the right template or knowledge base article. It can enable auto-response to certain types of requests by matching tickets to macros that agents actually used in the past and then recommending the best response template. This level of automation replaces manually entered and/or hand-selected information to improve accuracy. Automated templates also hasten response times dramatically, improving customer satisfaction and allowing companies to get even more mileage from their finite support resources.

AUTOMATION AT WORK: DURING A SUPPORT CALL

SaraAge 32From New York Is an established mobile customer

Sara has a problem with her new phone. Her texts are deleting on their own after two days. She’s the “I need it now” type and wants a quick resolution without having to wait on hold to talk to a support agent. She goes to the website, clicks the “Need help?” button and provides some basic information into a chat panel. An automated program recognizes patterns in her message and matches the content to a specific support template that has all the answers she needs to solve her problem. Because the match is so spot on, an agent isn’t even involved in resolving her issue. A response is automatically sent to Sara, who is thrilled to have an answer in record time.

What Sara thinks: This company values my time.

Company Benefits: Support resources can be spent on tickets that require more hands-on, human resolution.

DID YOU KNOW? PREFERENCE FOR SERVICE AGENTS’ DEMEANOR VARIES BY REGION: 49% OF MIDWESTERN CALLER PREFER “CASUAL,”

WHILE 36% OF NORTHEASTERN CALLERS PREFER “FORMAL.” – SOFTWARE ADVICE

49%CASUAL

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Situation:With nearly half a million support cases and over 900,000 emails coming in over a seven month period, a leader in the gaming industry needed to improve the efficiency of their customer support process. The support team relied heavily on response templates, but there were more than 8,500 response templates available to reps, and no effective auto response or template selection process to help them quickly choose the best one. Although their support software recorded how often each response template was used, it didn’t provide information about which template was used on which cases, leaving support technicians starting from scratch every time a ticket came in.

Solution:Wise.io worked hand-in-hand with this company to help make their template library much more usable and useful. By applying sophisticated natural language processing (NLP) techniques, Wise.io was able to identify duplicate templates that could be removed from the library. Wise.io then developed an application that recommended the most appropriate templates based on responses to similar cases in the past. Recommended responses were shown directly within the agent view so that customer service representatives were able to take advantage of the suggested templates without learning a new workflow.

Results:Using this automated identification approach, this gaming company was able to reduce their library of templates by over 90%, making agent training and usability much easier to manage. In addition, by presenting their customer service representatives with recommended responses, median resolution time decreased by over 50%, meaning that agents could resolve twice as many tickets in the same amount of time or could engage more deeply on the customer issues that really needed more attention.

CASE STUDY: AUTOMATION REDUCES RESOLUTION TIME BY 50%

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Post-Resolution

Every issue a support team resolves becomes fuel for your analytics engines. Here, automation can send a follow-up satisfaction survey, auto classify tickets for reporting and even push customer service activity to other parts of the organization to help improve their processes and outcomes

Support teams then and now. In contrast to early telephone-only customer interactions, today’s contact centers find themselves at the center of omnichannel feedback and a wild proliferation of options for customer interaction, from social media to virtual agents. Automation can help enhance the flow and functionality of channels ranging from customer emails and text messaging to mobile business and service applications, in addition to streamlining and simplifying traditional voice calls.

AUTOMATION AT WORK: POST-RESOLUTIONRemember Tom from Idaho? Automation can be set up to send him a customer satisfaction survey after the call. Based on the inputs from his feedback and his age demographics, prescriptive analytics engines help determine that he is an ideal customer to be included in a marketing campaign aimed at long-term clients over the age of 55. A week after his support interaction, he gets a postcard in the mail offering a 10% discount on his next month of service for every person he refers to the company. Decision-based automation helps turn his information and responses into assets, creating better outcomes for marketing.

What Tom thinks: They value my opinion and loyalty.

How does a company benefit from constantly crunching this stream of data? Streaming, cross-channel customer support data provides decision-makers across the organization with real-time metrics to inform the actions required to increase value, improve revenue and raise customer/agent satisfaction levels.

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While reducing response time is one of the many advantages of adding automation to your customer support practices, real value comes in improving team productivity and customer satisfaction, so that you are able to scale with rising customer demand and expectations, while keeping engagements as high quality as possible.

Machine learning is a predictive (and increasingly prescriptive) analytics approach that teaches computers to think and solve problems like a human, continuously adapting to new information. Automation enabled by machine learning is able to analyze vast amounts of data and adapt to changes that happen over time. With machine learning, you can monitor the entire customer experience to not only gain new perspective but actual guidance on the best next steps to take.

Put simply, machine learning is much like an analyst on the floor of an exchange or a line coach in the middle of a big game. Specialists at what they do, each is fed a constant stream of data to which they compare past experiences to make predictions and advise on next steps like what to buy or sell, which defensive play to call or what agent will produce the best outcome.

It’s the sum of all of those interactions, trades and plays that sets one analyst, coach or machine learning application apart from another. Machine learning is able to scan all applicable data to optimize a particular outcome for that particular engagement (be it faster response, the most relevant answer or the highest satisfaction.) As a result, machine learning can help optimize the health of a company, including its:

• Operational performance• Scalability• Customer and support member satisfaction• Optimized ROI across all departments Machine learning begins with the data a company already has on tickets they’ve closed across all support channels as well as data clues that define their support teams’ expertise individually and in groups. By sifting through or “mining” ticket history data, machine learning organizes and finds relationships between all of these seemingly disparate pieces of data—relationships that predictive analytics engines can then use to intelligently optimize their existing workflows.

INTEGRATION OF MACHINE LEARNINGCustomers put a high value on immediacy and intuitiveness, evaluating every service-related experience in their day against the one before. However, delivering good customer service is no longer a means to keeping your support queue clear or outperforming others in client courtesy rankings. Traditional customer support metrics like ‘average handle time’ are only a small part of what makes up a successful customer experience.

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TOP USES FOR MACHINE LEARNING WITHIN A CUSTOMER SERVICE DEPARTMENT

• Triage support cases without human involvement. Machine learning identifies how past tickets have been classified and routed, allowing it to mimic that logic on each new ticket. Furthermore, as processes change -- perhaps because of a new product release or staff turnover -- machine learning observes the new behavior and updates its triage approach.

• Identify the appropriate template to use. The application reads the content of each new ticket and presents the agent with the top recommended templates that can be applied immediately. Over time, as individual agents apply templates, the application continues to learn and improve its recommendations for all agents, driving consistency and efficiency across the support team.

• Reply automatically to customers. For many customer service situations, speed of response has the biggest impact on customer satisfaction and the answers provided are relatively straightforward. For those scenarios, machine learning can fully automate the response, quickly getting customers the answer they need and allowing agents to focus on more complex cases. Even if the automated reply is simply a recommended list of relevant knowledge base articles (which machine learning can also find), that frequently gives customers everything they need to answer their question while still maintaining an open line to the Support team if their issue is more complicated.

• Push relevant information in context across channels. Customer support’s role on the frontlines of your organization’s dealings with the public means their interactions produce a treasure trove of valuable data, with critical importance to other departments. Machine learning can help support teams make their data actionable for other parts of the organization. Detailed information about customer service engagements can help improve business incomes in marketing, sales, product development and other departments

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Not all support channels are created equal.

“Many “digital” channels have historically suffered from poor discoverability, problem coverage and knowledge management processes. Quite simply, customers just can’t find the answer to their problems. What’s more, many customers find self-service digital channels impersonal; they miss the two-way nature of a conversation.”

Support teams currently integrating machine learning capabilities and automation with support processes will be better equipped to achieve a consistency in support across all channel and to establish an industry-leading omni-channel presence.

“A new generation of virtual agents will emerge this year; virtual agents that can learn from terabytes of data to provide contextual, relevant responses instantly and with the same accuracy as a live agent in a call center.” - WDS

Automation can play a valuable role in a team’s early stages and as the organization matures. From consistent messaging and improved user experience to taking productivity further than you ever thought possible, automation works to improve efficiencies. Automation involves highly advanced data science, but it does not mean that only advanced, well-established support teams can benefit from automation or machine learning. No matter what stage of the journey your support team is in, automation can help move you to the next.

Is your team ready to implement automated processes or decisions? How many of these scenarios resonate with you?

• Basic ticket routing is set up, but it will be hard to scale.

• Our team spends a ton of time searching through templates. There must be a better way.

• Agents are ‘cherry-picking’ tickets based on preference or what they consider to be high priority, which is not always the most important issue.

• We have a ton of great self-help information, but customers aren’t using it.

ARE YOU READY FOR AUTOMATION?

If any of these scenarios line up with your current support operations, it might be time to implement automated processes into your organization.

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HOW TO GET STARTED WITH AUTOMATION

1. Consider an agile approach - It’s easy to fall into the trap of trying to solve everything at once, looking at every possible interaction with every customer on every channel. Keep in mind that automated processes can always be enhanced (remember the Descriptive, Predictive, and Prescriptive models) so you don’t have to solve every analytics challenge right out of the gate. Initially, you can automate your biggest pain points and then gradually automate more more over time.

2. Choose the right business process - Segment your support processes to identify where it makes sense to layer in automation and machine learning technology. Start with a process that is relatively mature and stable, and where you see your agents performing repetitive tasks, like choosing what template to use for a ticket.

3. Understand your data - Take inventory to understand what data is available, where it lives and how it’s being used to make decisions. But, don’t fret if your data is scattered, or if you feel like you “don’t have enough.” Machine learning can work with your existing support systems and any data types, including text —  to setup automated processes so there’s no need to “organize your data.”

Here are five ideas to consider to get your support team and data ready for automation and machine learning.

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4. Make your outcomes crisp - Balancing risk and reward is critical when deciding what level of automation is appropriate for your customer service approach. The more clear you are on what outcome you want, the better the value. By understanding past outcomes — good, bad, or ugly — automation can help you scale in the best way for your business.

5. Look at risk and benefit - Think about which decisions to automate and which to augment by assessing the “cost of being wrong,” or the cost of an incorrect action. For example, choosing which support agent should receive a ticket can be automated; the customer likely wouldn’t notice if he engaged with the “wrong” agent. However, saving an at-risk customer may be better left to an augmented approach.

Automation is not a black box for decision making, but a supplement to human action. Machine learning can help determine which customers are most at risk to churn and which template is likely to work best, but the support agent ultimately controls how to handle the interaction. Automation is meant to help support teams work smarter, not harder by improving consistency and processes to make for happier customers and support staff.