Top 5 Problems with Service Today

We’ve all been the victims of bad service. We have to repeat ourselves to agents; we get different answers depending on whether we use email or we call a service rep; or worse, we don’t even receive a response. We have a tendency to blame the agents.

But it’s not their fault. They want to do a good job. They just don’t have the tools they need to meet customers’ expectations of personalized, consistent, accurate and fast service. They don’t have the tools because their technology isn’t working.

Thus, I trace bad service to 5 root causes:

- IT organizations have not solved the integration problem. Agents need dozens or often hundreds of un-integrated tools and applications to perform their jobs. Agents must toggle through many applications in the span of a service call, resulting in long hold times.

- IT organizations have not solved the change problem. Agent tools are typically hardwired together. When procedures change in a company, IT organizations cannot quickly respond to the changes in order to give the agents the new tools that they need.

- Knowledge management vendors have not solved the knowledge problem. Corporate knowledge exists on an island; it does not fit into the context in which agents are searching for content. This means that agents need to wade through many solutions in order to find the one that is right for a particular customer.

- Case management vendors have not solved the business process management problem. Today, business or call center leaders can’t drive agents through clear processes. This means that they put the responsibility of following the right resolution processes in the hands of agents, which are not all equally competent.

- Organizations have not solved the agent training and turnover problem. Businesses know how they want service delivered, but they can’t have their best, most highly trained agents handle every interaction.

How do we start solving these problems? We start with technology. We implement service-oriented architectures (SOAs) to begin integrating systems in a flexible way so that information is no longer siloed. This means that when processes change, IT systems can be rewired easily, without a tremendous amount of overhead. We ensure knowledge is it’s shared and applied appropriately. We present in a way that makes sense to agents, and we use it to inform our decisions about what’s working and what’s not working in our service processes. We provide agents with stepped procedures for each and every service interaction that allows them to provide the consistent service that we desire. If we are able to achieve these goals, and provide agents with the tools they need to succeed, then we’re solved our final dilemma. Because every agent becomes as good as our best, most highly trained agent.

Thoughts on Choosing Customer Service Software

The most important thing to remember when selecting Customer Service software is to choose an offering that supports your company’s brand perception. It is not the goal for all companies to offer the most ideal customer experience that vendors try to sell you. For example, the expectations of users of a discount airline is not “over-the-top” service, but service that is streamlined, and heavily reliant on web self-service and email. On the other hand, a luxury retailer’s service offering is very different – It should be white-gloved service, reliant on personal contact that is tailored for a particular customer.

In evaluating Customer Service software you must also understand your customer’s demographics, and their communication channel requirements as baby boomers for example, have very different communication needs that the more tech savvy Generation Xers and Y’s. Finally, you need to understand the communication channel requirements that your company can afford as channel costs differ widely. Web-self service and email costs are typically a fraction of those for the “live assist” channels of chat and phone. Irrespective of the communication channels that you can support now, you must architect your offering so that channels are not siloed, that corporate knowledge is shared and that alternate channels can be added at a later time as the need arises.

Once your Customer Service system is implemented, you need to measure its efficacy and tune it using a balanced scorecard approach. You need to measure key performance indicators – for example, the cost of delivering customer service against the satisfaction of your customer base, the compliance of agents to company policy and generated revenue. These measures then need to be mapped to the overall customer perception of your company to ensure that the chosen Customer Service systems are supporting and not eroding your brand image. And finally, you need to be agile enough to change your service offering based on customer feedback and the gathered metrics.

KANA: Change Redux

Earlier this year, in a previous post, I noted that change is the one constant in business and in life. Change is good as long as it ultimately carries a person and a business forward with new insight, knowledge and skills. Without change, a person can stagnate and an organization can lose its edge.

Look at the Yankees. Over the past 15 years, the team has changed managers, stadiums, and nearly every position player on the field. These changes might have been too much for another team. But the DNA of champions is deeply embedded within the Yankees organization. Ultimately, it was a combination of their winning DNA and the changes made during rebuilding years that lead the team back to the top – after nearly a decade without a Series ring.

Like the Yankees, KANA’s got winning DNA. The changes we’ve made at KANA to date this year are intended to have the same end game: getting us back on top. While these changes haven’t been easy and much hard work remains ahead of us, we continue making steady progress on all fronts.

Just last month, on the business side, we announced Accel-KKR’s plans to acquire our company and take it private. KANA has been a strong leader in customer service for more than a decade. This is attributed to the depth of our customer base, the talent of our employees, and the investment in innovation technology, like KANA 10. Accel-KKR recognized these winning attributes, and they played a significant role in bringing about this corporate shift.

Accel-KKR’s financial strength and expertise make it the ideal partner to help us fortify our operation – from providing strong, responsive customer support to ongoing product enhancements to adding to the innovation of our new service experience management (SEM) solution, to which we just integrated new social capabilities.

This month, we’re making yet another change, on the communications front. We are folding KANA’s executive management blog, Speak Out, into the Company’s Evolved Thinking blog, which was established years ago by our service division and has built a strong following. KANA’s Speak Out contributors (myself included) will now post on Evolved Thinking.

As many of you may know, Evolved Thinking is a place for KANA’s thought leaders to share their perspectives on trends in customer service and support, self-service, knowledge management, social CRM and other topics central to improving the service experience. This focus won’t change. Rather, we’ll be posting more often and offering a broader spectrum of contributor voices to sound off on these pertinent topics, share best practices and, of course, to continue providing insight into changes in our industry and at KANA.

Hype or Hyper-Measurement?

Recently, Seth Godin posted on how we are moving into an era of “hyper-measurement.” As he explains, a journalist being fired because his or her web content isn’t drawing a lot of traffic is not going to be a newsroom novelty for much longer. In a world where, more and more, success can be measured in quantifiable metrics, why wouldn’t a publisher base business decisions on newly available evidence? It just makes sense.

The customer service industry is no different. As my fellow blogger, Kate Leggett, points out, to truly maximize the value of their contact centers, customer service mangers must take an experimental approach towards service processes.

For every end-to-end service process they design and deploy, managers must define a testing period during which they play around with different aspects of it and measure results against business objectives. These metrics can then be used to determine the relative effectiveness of the process – what worked and what didn’t – and update the service process, or eliminate it entirely. To ensuring ongoing optimization, no process should ever be viewed as final. Rather, managers must consider each and every service process as a prototype and resign themselves to always retesting and tweaking them.

Delivering “evidence-based service” in this manner will help consumer-facing companies break bad habits, discover new tricks and cope with the constant shifts going on in their marketplace.

For instance, a business introduces a new tool that captures and analyzes customer interactions in various community forums – essentially, pooling the collective input of the crowd at given time. The customer service manager applies that insight in real time to change, reprioritize and improve information presented to a customer on the self-help portion of a website or educate agents on a shift in incoming questions (depending on the level of service the company provides). Over time, they begin to see patterns in terms of what information is just chatter and what type of information is important to meeting their business objectives and their customers’ brand expectations. And, the way they use the tool – and incorporate the customer voice – becomes a highly sophisticated and beneficial to their service processes.

The “hyper-measurement” trend, as Mr. Godin puts it, shouldn’t be ignored. It should be embraced. It offers a way for service departments, as well as publishers, to be more precise and pragmatic with their decision-making – to remain in control and one step ahead of the customer.<

Cast Your VOTE Today:  Search!

This week’s elections got me thinking about a voting process most of us do many times a day but hardly think of that way:  searching on the web.  If you really break it down, a search isn’t some deep, technical thing we do – it’s more of a guess, an approximation, a forecast, a VOTE about what words we think exist in content we think we want to view.

This process works quickly, magically and consistently most of the time so we rarely give it much thought.  We enter a few words, as unique and meaningful as we can make them upon 2 seconds reflection, and if we get what we want no more thought is given to it.  But many times we don’t get great results, or they’re confusing, or what we want appears way down on the list.  At these times we often blame the search tool, the content, the website we’re on.  But what about our own responsibility in this game?  Who made us the experts in the domain of information we’re looking through?  I don’t know about you but I have little interest in the details of my cellular providers’ terminology, content types and taxonomy, except as it applies to my immediate issue.  I have to admit upon reflection I’m not really much of an expert.  My queries are based on guesswork, what I assume to be common sense.  But often times they’re just my view of some other organizations’ reality – with good or bad results based on how well I’ve accurately guessed what that is!

In order for us to create great search and browse experiences for customers in support and service we should acknowledge the realities of our searching audience.  We need to think about how our tools and content can respond when we get queries of various types.

Many people query in greatly oversimplified fashion, ESPECIALLY when they’re not sure what to look for, since they don’t know what other words to use.  These folks are usually the least satisfied with the search experience, for obvious reasons.  We need to set up content and knowledge base results mechanisms that guide these folks to either the most popular documents we know they’re likely looking for, and/or give them further guidance on where or how to look for something in the topic area suggested.

For example, I had a client once who discovered upon analysis that 4% of their queries were the word “vista”.  It wasn’t Microsoft, it was a company whose customers wanted to know how the new Vista OS impacted their company’s products.  This one word query isn’t really very descriptive, and it wasn’t about their stuff, so they had not done anything to respond to it.  But 4% of web traffic’s a pretty decent amount, so all they had to do was put some general documents up pertaining to Vista requirements and settings and they were able to satisfy it.

Other people query in overly complicated fashion, using really specific terms or longer queries in an effort to get something really specific.  The same tactic applies as with general queries: being sure content exists to overview a topic, or to help people understand how the information is organized to find the specific item.  The mechanisms may be different in terms of how the tool responds to such queries, but the principle is the same.  These folks need to learn what will truly drive the specific results they seek.

Finally, it’s often the case that a hot topic or issue comes up that people query on in lots of different ways, using a wider variety of terms and combinations than one would expect, or using terminology that has nothing to do with how the company thinks of the problem.  I recall a company that had a cash-back program that had a fancy internal marketing name that every document referred to, but no information or support built in for queries where people just asked obvious questions like “where’s my check?”, “how do I mail in the return form?” or “how much cash am I eligible for?”  In this instance adding synonyms to the search capability, generalizing the titles to these types of issues and making the wording in the content a bit more customer-friendly all helped assure people assure they got something that seemed to match an obvious common question.

Searching is a funny sort of discipline – it’s something we’ve all evolved culturally without any rules, guidance or training.  Nobody says how to do it well, websites don’t give away any help about what’s going to work, and we are really left to our own resources to decide and cast our vote about what’s going to be in the content we’re looking for.  As long as we don’t forget that customers are indeed just GUESSING at what they want, and build bridges to meet them as they enter our world, we’ll stand a better than even chance of providing them with the best info, at least for the common questions that have the most impact both ways.

So get out there and VOTE today – for content!

John Chmaj
“The Knowledge Advocate”

It’s no fun to state things in negatives, but sometimes it’s important to identify common patterns and issues, so we don’t keep doing the wrong things over and over.  It’s also quite easy to enter into a comfortable state of denial, where things are because “that’s always they way they’ve been”.

In an effort to help identify areas that might actually be hurting you that you’re not aware of, or not aware you can CHANGE, I present to you my KM ‘Letterman List’ for November.  KM DOESN’T WORK BECAUSE:

10. The knowledge management tool just sort of sits there – nobody’s really minding it, it’s kind of like bad plumbing that everybody puts up with.

9. Nobody really measures how well content is meeting the needs of the internal and/or external users.There’s no top-line outcome of success from either the internal KB or external self-help that guides activity.

8. Nobody asked the CSR’s how THEY search, what content THEY use, and how THEY want their tool organized.

7. You dumped all your stuff into your new knowledge base, and it’s no easier to find than before.

6. The toolset doesn’t work – it’s too slow, inconsistent, up and down, behaves oddly, integrates poorly, and/or doesn’t bring back decent results.

5.  The content being used is too long, complex, jargon-filled or inaccurate:  it doesn’t provide quick, easy access to the best information the user requests.

4.  The organization isn’t really staffed around KM – activity is sporatic and hit/miss towards keeping the knowledge base current and relevant.

3.  There’s nobody holding the products, engineering, marketing and/or sales groups responsible for keeping their information accurate, up to date and focused on support-relevant topics. People across the organization don’t buy into the need for content and tagging standards – they write what they want, how they want.

2.  Nobody’s evangelizing, coaching, educating the organization about the value of KM, how to best use the tools at hand, what needs improvement, and helping drive effective continuous adoption across centers, locales and lines of business.

1.  There’s no objective, capability or outcome from doing KM that resides on a key executive’s top ‘to do list’, as a key enabler for the business as a whole.  Such objectives would spark and demand leadership, action and accountability across the organization to stay focused on achieving better knowledge development and delivery in the midst of all other priorities, initiatives and crises.

Does #1 sound simple?  Ok – quickly – name the top outcome from KM your executive expects, and what objectives they are monitoring and driving to achieve it…

If that’s clear and easy to identify, the chances are you’re getting help addressing the other 9 issues.  If you’re still waiting, make a few up and go have a chat with them!

If you answered yes to any more than 2 of these you probably need to ask – WHO is driving your KM bus?

Happy November,

John Chmaj
“The Knowledge Advocate”

Email Done Right

Customers don’t trust email as a reliable communication channel with a service organization. How many times have you sent in an email to a company, and received no response? Or received only a partial answer after waiting for days?

Poor performance of email tools can usually be traced to their implementation history – email systems were typically deployed years ago with little tuning to maximize their productivity.

Even with history working against you, here are some basic steps to follow in setting up your customer service email.

• Make email part of your multichannel strategy – Don’t think of email as a siloed channel. Provide seamless escalation between your web self-service offering and email, and be sure to have a single source of knowledge that is used across all your communication channels.
• Keep your customers in the loop from the time they send an email into you, to the time that they get an answer to their questions. Always send them an auto-acknowledgement letting them know you got their email. Tell them how long it will take to answer their email. And provide them with alternate contact channels if the SLA you have communicated to them sounds too long to them.
• Manage your email flow so that you can meet your SLAs. Set up your rules and queues to ensure that emails get sent to the right skillset of agents. And staffing each email queue with the appropriate number of email agents to ensure that your SLAs are met
• Use automation tools – like auto-responses, auto-suggestions to take the load off your agents. Use text matching algorithms to read the intent of incoming emails in order to route them to the right email queue.
• Teach your agents to properly answer email – like answering all the questions that are contained in an incoming email, and answering all questions that are asked, and ones that are implied.
• Monitor, measure and optimize your email performance. And be flexible enough to change if you find yourself falling behind in your SLAs or quality of customer care.

The Ants Go Marching

If ants have to choose between two unequal length paths from a source of food back to their colony, they ultimately always choose the shortest, easiest one. Here’s how it works:

1. Ants run around the colony, more or less randomly, looking for food.

2. If an ant finds food, it returns back to the colony, and leaves on the ground a scent trail.

3. This scent trail attract nearby ants, which will follow this path and strengthen the scent of the trail, attracting more ants, strengthening the trail…

Ant colony optimization can be analogous to customer service. Think of how customer service agents answer customers’ questions. They each have their own style of interacting with the information at hand. Typically, they hunt and peck through disparate, unintegrated data, knowledge and back-end systems in a way that is unique to each of them. This often results in inconsistent, inefficient or, worse, incorrect service.

To avoid such consequences, companies must leverage technology to create process flows that guide agents through the same method of discovery. Think back to how ants find food – no single ant finds the best route back to the colony alone, yet a collection of ants are successful in doing so. And positive (stronger scent trail) and negative (weaker scent trail) feedback throughout this quest is used to guide this optimization.

Imagine if you had the ability to design not only a single customer service process that led agents to the correct answer to a customer question, but several variations of this process. Take, for example, varying the step in a service process in which the identity of a customer is verified, or when a specific knowledge article is presented to the agent.

Ideally, companies measure KPIs at each leg of this process. These steps are akin to the resistance that ants experience at each leg of their journey. By uncovering the strongest segments of each process, companies essentially rely on basic ant colony optimization techniques to determine the best customer service processes.

FINDING Depends on How you SEEK -

One of the most important things to think about in making knowledge delivery systems work is HOW users will find what they’re looking for.  At first glance it may seem simple:  type in a search, find your stuff.  But is it really that straightforward?  Do you really know what you’re looking for?  Do you really know what the right query is?  Do you even know what content exists in the system you’re using?

One of the main reasons we all engage in building and managing support-focused search tools and portals is because we are trying to create a series of efficient, intuitive user experiences that lead people to the right answers to their questions.  However, my experience is that all questions – and all users – are not created equal.  Sometimes we know exactly what we need and how to ask, other times we haven’t a clue.  The same person, who may be expert on one issue, may be totally lost just looking for a slightly different issue in their own KB.

In this context always think of one Christmas morning spent huddled over my sister’s computer, trying desperately to get the Disney Aladdin DVD to install and run properly, my little 4 yr old daughter wailing and pleading with me as I, the great computer expert in the family, struggled to figure out what query to enter in the Disney knowledge base to address the failure of this $5 DVD to load!  One is only as smart as the next problem, really –

One of the most critical yet unrecognized features of a knowledge base is its ability to teach users about the terms, taxonomy, and content that is available to solve their question.  Most searches aren’t a one-shot deal – the user progressively browses to learn about an issue, scope the content, tries different queries to see what’s in the KB, and finally decides when to browse through titles and content for the best fit.  I find it useful to boil the types of interactions one needs to have based on how much one knows about an issue, and what one needs in terms of guidance from the knowledge base to get to the answer. A simple way to think about it is as follows.

Three types of navigation

1. I Know What I Know – Lookup

This is the stereotypical “Google-esque” query – I know the data I want, I know the terms that should bring it back, I type in “error 99″ and voila!  There’s my stuff.  Support experts usually expect this type of behavior, and in fact the weaker a KB is the more they memorize special terms, document ID’s and keywords that will give them what they want.  But in the end they’re not really searching as much as looking up known content.  This is an important capability that must work well, but it’s far from the only interaction people have, ESPECIALLY in self-help systems.

2. I Know What I Don’t Know – Guided Search

In this scenario users know the TYPE of information they need, perhaps also the topic or potential terms, but don’t exactly know what content is out there.  They may browse to scope the issue, then type in a query or two based on what appear to be the common topics.  KM systems assist here by providing potential additional browse and filtering options that are relevant to the current query scope.  The user can go back and forth, examining the options provided by the system, to find the right fit of topic and query detail.  These systems work well when the user has some idea of what the right solution is and just needs assistance getting to the right area of the knowledge base.  But that’s not the final area, nor perhaps the most important…there’s still:

3. I Don’t Know What I Don’t Know – Browse and Filter

Many users, especially those new to a particular domain, don’t really know how to think about the information that’s in it, what terms or topics are relevant, maybe not even what they’re supposed to ask.  In my Disney DVD problem, I had some smart ideas but none turned out to be relevant.  Was it the display driver?  The computer RAM?  OS version?  Plug-in requirements for video?  And how should I query a simple Disney DVD for this kind of stuff?  These scenarios are where a good browse experience can shine.  Not only can it help users figure out what the key components of a product are, the topics and known problems, but should also quickly point up common questions the user is likely to have.  By seeing common issues and how they are organized users can get insight into what their questions are, or likely queries should browsing and filtering not bring back the answer immediately.

At the end of the day we’re all just looking for ANSWERS, aren’t we?  If we think through what the process of acquiring knowledge entails, we can trace these patterns of inquiry and do our best to model them for particular audiences, users and issues.  When this is well done for all three query types, users experience the ‘magic’ of knowledge bases.  They DO indeed seem to ‘know exactly what you want’, yet you may not even be aware of how seamlessly the system allows you to move between these modes of inquiry and still get the scent of the answer you need.  As KM system builders, we just have to give a little THOUGHT to how people THINK….!

John Chmaj
“The Knowledge Advocate”

New Contributors Joining Our Blog

KANA historically has had two blogs: this one, and Speak Out .

Evolved Thinking’s charter  has been to provide a forum for Practice Leaders to share their perspectives on customer service and support trends, self-service best practices, Web 2.0, knowledge management and other topics central to improving customer service delivery.

Our other blog, Speak Out, provided KANA’s executive management with an outlet for sharing their insights regarding the emerging technology category of Service Experience Management.

Next week we will be folding the Speak Out blog into this one. This means that you will see more posts and a broader variety of contributor voices, like those of KANA’s CEO Mike Fields and CTO Mark Angel. The focus of this blog will remain unchanged, and we encourage you to keep reading and commenting.

What topics are you interested in hearing our point of view? Please let us know and we will consider them as our charter evolves.