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.

Customer Support Myths…are They Really Myths?

Managing a call center is more of an art than a science. Some service managers use a set of pigeon-holed metrics to manage their business to – like average hold times, number of emails processed per agent, and in some cases customer satisfaction ratings on their service. Others apply established best practices to their organizations, without thought of what works for their company size, their product or service set and their customer demographic. Some jump on the current trend bandwagon without an analysis of what this means stragecially for the company.

I have been compiling a list of “half truths and total nonsense” about management philosophies and technologies in Support. Which ones resonate with you? Which ones do you believe are not myths and work for you?

Kate’s List of Common Services and Support Myths

• Social CRM is giving customers control of your brand
• Established best practice apply to my call center
• Longer calls are not good web self-service candidates
• Discussion forums cut call volumes
• Good web self service cut call volumes
• Front-line support agents don’t know anything
• When you measure operational activities, you measure business outcomes
• Support can act independently of brand –Support can have a different brand identity than marketing and sales
• Customers can’t create reliable knowledge
• Complex interactions are not repeatable / process-oriented
• Better search means that I can find everything
• Email doesn’t work as a support medium
• Chat won’t work for tech support.

What is your favorite myth?

Numbers, Numbers Everywhere but How should we THINK?

Call centers are notorious at producing STATISTICS – they can tell you how many people called, average talk time by product, agent, what percentage of people are on hold for how long, when people abandon, which calls get escalated to 2nd tier, etc. etc.  Often organizations have regular meetings to review the stats, wring hands over worrysome trends, take action on emergencies indicated by the numbers.  It can become THE management method, especially when the organization has certain service levels or utilization targets that are critical.

When it comes to knowledge I wish I could say there was a similar zeal for measurement and metrics-based action.  In my experience there isn’t.  Why?

Organizations often track simple things like which documents are viewed the most, search queries, documents created, but these are mostly transactional measures.  They don’t add up to insights on HOW and WHERE knowledge is driving value for an organization, or what to do when it’s not.

I’ve spent quite a bit of time thinking through this problem, as the interesting paradox is that organizations that invest in KM unilaterally demand a clear business case and ROI for technology, services and program investments.  So up front anyone who wants to initiate or update their KM capabilities must generate some definite projections about how knowledge will impact the business.  Once the project is funded, often months or even years after completion, however, I find again and again that organizations aren’t measuring the things that were committed up front to demonstrate KM value.  This makes it difficult for these initiatives to garner the mind-share and focus they need to get more resources and funding, which in turn keeps the resources available for measuring at a minimum.  You get the pattern.  Measurement is a CRITICAL component of KM, both in managing the day-to-day process and in keeping an organization aware of how well KM is working and what’s required to improve overall.

In a nutshell this situation might be attributed to the following factors:

• KM value measures require a multi-layered, multi-source reporting that isn’t natural or easy for tools or organizations to generate
• KM analysis isn’t easy to do either, with the data – some business intelligence expertise is usually in order
• There’s often a poor understanding of what analysis is needed, and actually a ‘missing layer’ of value correlation

Here are the layers I’m talking about:

Layer 1:  Knowledge Availability

Is the right knowledge actually online for the questions and issues involved?  Is that information findable for representative queries?  Is it consumable – up to date, accurate, easy for the audience to read?  These are the baseline stats on knowledge availability you’re most likely to find.  They can tell you some things about the distribution of content & demand, where there may be trouble spots, and overall quality requirements.  But in themselves they say little about how knowledge affects support & service as a business.

Layer 2: Knowledge Usage

In order to define how knowledge development and delivery activities drive business value it’s necessary to figure out when the act of FINDING and USING knowledge actually helped solve, simplify or shorten time to resolution.  This itself requires some form of user feedback that’s correlated with the knowledge being used, which can be rolled up into trends by products, questions, information types, etc. Still, this information is typically available in KM tools today, and can be readily correlated with user feedback or support agent input.  It just needs to be done – and often isn’t.

Layer 2: Knowledge Value

In order to correlate the effective usage of information to top-line business value one must be able to assess where knowledge is actually driving improved KPI’s for the business.  Where is knowledge re-use shortening handle time, improving consistency or accuracy of answers, driving call deflection?  THIS is the missing layer of analysis in most organizations.  It requires the ability to roll up the Knowledge Availability and Usage data and cross-reference it against a well-defined feedback about the KPI one desires to track.  Typically this is labor-intensive and requires a focused program in itself, to be sure all the numbers and variables are properly tracked, since the top-line measures involve a lot more than just the knowledge component.  It’s necessary to find an area with relatively stable support issues, not a lot of business reorganization around it and enough traffic to get good data.

Folks this CAN be done!  I’ve seen it and done it.  I worked at Microsoft in the late 90’s as the Content Architect for the support website- through targeted analysis and response to the trends we saw we were able to progressively introduce additional content, improved search features, and overall user experience features that drove user-defined success from the 30 per cent range to the 60 per cent range and up.  We did this by carefully studying which queries and document views were driving success, where the gaps were, what types of failure were occurring.  We had tools and techniques for gathering the right data, we had a business intelligence analyst for the support organization who helped us craft the right analytics, and we had the people who could own follow-up to make the necessary content, tool or web improvements.

It CAN be done!  It just needs that passion and zeal that we reserve usually for our call stats.  For any organization with high support volume – whether it be a huge self-service hit rate, or lots of call center traffic – it’s well worth the effort to find out where and how to push satisfaction and success numbers. Even a few percentage points of improvement add up to millions of dollars in savings.  Yet we don’t “eat our vegetables” – this stuff is hard, relatively unglamorous work.  And we as an industry haven’t done a great job of pushing our organizations to embrace a clear measurement methodology.  But I know that when the day comes that we CAN and DO measure the value KM pushes for the organization will be the day we may suddenly move to the center and forefront of perceived value from support knowledge.  For it’s still true that “knowledge is golden” – it’s our stock and trade, really, it’s what people look to us for, and the more we can show the value of that the greater service we’re doing to ourselves and our business.

Viva La KB!

John Chmaj
“The Knowledge Advocate”

KM is Crossing the Chasm!

In the late 90’s Geoffrey Moore published his famous book “Crossing the Chasm”, in which he developed a compelling model for understanding a common lifecycle pattern of technology adoption.  Essentially the “chasm model” pointed out the difference between the initial phases of a technology and associated products coming to market, and later phases, between which he posited a CHASM existed.  The chasm is the gap between the expectations, expertise and needs of the Innovators & Early Adopters,  and those of the Early and Late Majority (often referenced as “the herd”).  The Chasm Model is represented as a curve – here’s a simplified version I’ve done  for Knowledge Management:

Innovators & Early Adopters are willing to accept limitations in adopting new technology, the need to figure out and/or customize components of it themselves, in favor of the advantages they gain from being first to market for the new capabilities the technology provides.  The Majority needs more proof, market acceptance and guidance to adopt the new approach.

I assert that Knowledge Management for Support & Service is crossing this chasm now.  The early adopters in support KM have all implemented – these were the hi-tech companies, many telcos, other technology-related businesses with deep technology expertise and existing knowledge bases, as well as a critical business need to provide hi-quality access to complex technical content.  The Majority is now coming to the fore:  I’m now working with financial services institutions, banks, retail, insurance, utilities, airlines, online consumer and many other types of businesses which are new to KM.  Self-service is driving a lot of this market – the need to drive as much successful service traffic online as possible.  But I’m also seeing a level of maturity of internal understanding evolving.  Organizations are becoming aware of the internal need to ORGANIZE and FOCUS their information development and delivery activities.

All this activity suggests that ‘the herd is on the move’ – which means it’s time for us knowledge advocates to embrace the principles and practices required to successfully engage them.  A key insight of the chasm strategy is that the majority has different perspectives and needs than the early adopters.  The majority needs to see how others have done it, proof that it worked, and a clear recipe for moving forward.  Core KM practices to date have been as much art as science.  We need to frame and tool our methods in more consumable ways.  We need to make the process for engaging core practices clear and easy to understand, for everyone from the support agent to the self-help managers to the managers and executives who drive such initiatives.  We need to provide compelling, complete examples of success that show people how it’s done.  In short, we need better KM about KM!

What does this mean for KM?  My experience is that the ‘chasm’ is the gap between capabilities and emerging objectives for organizations.  I tend to get requests to address technology, content, metrics and organizational issues, which all add up to challenges in getting to a fully functioning KM capability. Considered together they actually comprise the gap in capabilities, expertise and focus needed to get to a well-functioning KM system:

If organizations can keep their eye on the ball, stay focused on developing these key capabilities, they will themselves be crossing the chasm!

This is an exciting time for Knowledge Management – if we can learn to cross this chasm KM practices may clarify and standardize rapidly for a much larger set of organizations, which in turn will create more examples and reference points of value for everyone else.  Then, as Geoffrey Moore describes, we’ll be prepared to engage a “KM Tornado”!  But that discussion is for another time – let’s get those bridges built first!

John Chmaj
“The Knowledge Advocate”

Evidence Based Service – The How is harder than the Why

Service organizations would perform better if leaders applied the best evidence. I’ve dedicated several posts to this subject. It’s a great concept and it’s another thing to do it.

Some experimentation is easy – for example like what Yahoo! does on its homepage where they typically run over 20 experiments at a time, changing things like colors, placement of ads and location of text or buttons. Outcomes of these experiments can have a huge effect – like moving the search box from the side to center of the home page generated enough click-throughs to bring in about $20M more revenue a year.

But this requires a mind-set shift–instead of debating which screen design or content works better, you gotta try it, and analyze the results to see what works.

#1–Treat your service offering as an unfinished prototype. Baseline your performance. Then try to vary something and measure how you do – trying something half baked quickly with the data at hand is often much better than just waiting until you have all the data you need as the urgency to react may have passed.

#2-Don’t get blindsided by corporate mantra, and organizational half-truths. Many service organizations are managed based on industry best practices, and historical precedence. Base your decisions on just data. Understand what works for you and your customers and do a root cause analysis to understand why changes you have made work or don’t.

#3-Look at yourself using your customer’s eyes. Do a mystery shopping exercise on your site, and document your strengths and weaknesses. Look at your competitors and see what they do. Do it in a systematic, scientific way, not based on emotion.

You can apply evidence based service at a micro or macro level-to a simple installation and tuning of a product–for example a new ERMS system. And, you can apply it to optimize a particular service offering, or transform your complete service offering to the next level of operational maturity