Unleashing the Voice of the Customer - WHITE PAPER A Next Generation Automated Customer Feedback Application
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ATTENSITY | WHITE PAPER
WHITE PAPER
Unleashing the Voice of the Customer
A Next Generation Automated Customer Feedback Application
ATTENSITY PRIVATETABLE OF CONTENTS PAGE
I The Dynamic Nature of Customer Feedback 3
II Following Feedback’s Flow 3
III The Customer Feedback Organization 5
IV Why Know Why? 6
V Understanding Unstructured Customer Feedback 8
• The Search Approach 8
• The Statistical Approach 8
• The Linguistic Approach 9
VI Recognizing “Voice” for Actionable Data 10
VII Attensity’s Automated Customer Feedback Application 11
• Attensity’s Voice of the Customer Domain 13
• Attensity’s Semantic Voice Engine 13
• Attensity’s Model Factory™ 13
• Attensity’s Voice of the Customer Analytic Dashboards and Views 13
VIII Conclusion 14
ATTENSITY WHITE PAPER Unleashing the Voice of the Customer 2
ATTENSITY PRIVATEI THE DYNAMIC NATURE OF CUSTOMER FEEDBACK CUSTOMER FEEDBACK EXAMPLES
There’s a good reason many people dislike the label “consumer.” A consumer is someone • “My product isn’t working; the
who buys or uses a product, service or solution. Period. The word consumer connotes a electrical connection seems to
be broken.”
one-way relationship between seller and buyer that fits poorly in today’s connected market-
place. A “customer,” however, can do far more than merely consume. Depending on their • “You lost my reservation.”
needs, experiences and desires, customers are far more inclined to get involved in the
marketplace. Today’s technology offers ample opportunities to start conversations with and • “I really like your new store
concept.”
among customers, fans, foes, competitors, and the press — any person or group who
cares to listen and, perhaps, act on the messages received. • “I love this airline, the service is
great and the online reservations
Customer feedback flows into organizations, dynamically and continually, every hour of are easy to use.”
every day directly and indirectly. Customers place calls, send emails, complete surveys, and
• “I wouldn’t recommend your
talk among themselves online in blogs and product forums. They share their thoughts about products because the process to
products and services, their likes and dislikes, and their hopes for future features. Customers buy your services online is too
tell companies about product failures. They request help. And they offer opinions about their hard.”
experiences that may contain valuable insights for organizations that care to listen.
• “I discovered a bug in your
software with the tax calculation.”
Customer feedback can tell companies which products will be a success, where future
sales will come from, what aspects of their services are good or bad, why people would • “The motor in my dishwasher is
making loud noises when I use it,
recommend a product or service to others why customers are loyal, why they aren’t, and
I think it’s broken and I need
much more. The information they provide can also offer companies insights into potential someone to come and fix it.”
product issues, service failures, cost overruns, or expensive recalls.
• “I want to return this product
because I am not happy with it,
All this information can drive sales, service, marketing and even organizational strategies.
it didn’t work as advertised.”
But none of this information is of the least bit of value if companies can’t find, parse, organ-
ize, compare, manage, and act on the data in their customer feedback quickly, accurately • “If I can get this new software
and intelligently. installed properly, I would be
happy!”
• “I thought I was the only one that
II FOLLOWING FEEDBACK’S FLOW ran into rude flight attendants. I
feel much better now.”
Complicating matters is the volume and variation of customer feedback flowing into various
groups within an organization. Feedback flow typically isn’t coordinated across groups and,
in most cases, primarily consists of unstructured prose.
Databases historically have collected “structured” data that is relatively simple and inexpen-
sive to access. Structured data is organized in a rigidly defined format within columns and
rows in the database. It can be queried, filtered, and sorted to help draw conclusions and
make decisions.
Unstructured data — the freeform text in customer emails, accident descriptions, survey
responses, surveillance reports, slide presentations, web sites, and dozens of other formats
— may be easy for people to read but nearly impossible for databases to understand.
According to the TDWI1, unstructured data is increasing exponentially faster than structured
data. In fact, seven million web pages are published every day. Traditional channels for
feedback are being joined — and often superceded by — new channels such as blogs,
forums, wikis, and other web-enabled spaces not authorized, organized, controlled, or often
even monitored by the company. Thanks to more dynamic collaborative technologies, many
feedback channels are easily created, amplified and distributed in ways their subjects don’t
always intend.
1
Russom, Phillip. BI Search and Text Analytics; New Additions to the BI Technology Stack. TDWI Best Practices Report,
2nd Quarter 2007; Renton, WA, 2007.
ATTENSITY WHITE PAPER Unleashing the Voice of the Customer 3
ATTENSITY PRIVATEOften, certain channels may contain both structured and unstructured data, either compiled
by the customers themselves or representatives of the company. Typical sources of THE MANDATE TO
unstructured data at a large company may include: UNDERSTAND THE VOICE
• Email • Chat sessions OF THE CUSTOMER
• Web forms • Defect reports
• Blogs • Customer service notes A Manufacturing Company:
• Wikis • Warranty notes A durable-goods manufacturer
• Online forums • Repair notes can collect tens of thousands
• Surveys • Trial tests of service records and
• Focus groups transcripts warranty claims annually,
representing direct warranty
Feedback from and about customers flow in and out of different organizations across the costs of two to three percent
company. Marketing departments solicit feedback via surveys (both printed and online), in of revenues. (Total warranty
focus groups and via email. Marketing also analyzes feedback that comes in across the expenses are typically five to
organization. Customer service departments hear from patrons after they buy something and 10 percent of revenues.) To
have either experienced a problem, have a question, or in certain cases want to buy more. effectively analyze what cus-
They typically get the early warning signs of product failures, issues with a company’s tomers say about products,
offerings and more. Sales departments (call centers, online stores, brick-and-mortar estab- issues and repairs in service
lishments) hear from the customer throughout their lifecycle. Those who create products or logs and warranty claims,
choose the products and services a company sells (product management, engineering, manufacturers need to
design, merchandising) talk to customers to learn about their needs and wants and conduct understand not only dates,
research (focus groups, surveys, online forums) to get feedback from customers. part numbers, and coded
issues but also unstructured
Often, information collected from customers is not used at all. The only available means information captured as
most companies have to understand unstructured data is to have humans read it. While no notes and comments. This
computer will likely equal the human intellect’s ability to comprehend text written by or about freeform text comprises the
customers, humans are poorly suited to read hundreds, thousands, or millions of text majority of information in the
records to find facts, track trends and discover dangers. If used at all, text information is email, service report and
relegated to anecdotal support or the last line of defense: “If all else fails, we’ll just have to repair note such as:
read these comment cards.”
• What failed?
And the information remains stuck in a silo. • What were the
circumstances?
FIGURE 1 Feedback Channels and • How is this failure related to
Organizations That Use the Feedback other incidents that have
been reported?
• What did the customer
experience?
ATTENSITY WHITE PAPER Unleashing the Voice of the Customer 4
ATTENSITY PRIVATEThe feedback that flows into each of these organizational silos often doesn’t flow back out. In some cases, the feedback finds its
way into a data warehouse or customer relationship management (CRM) application where different groups can access it. In most
cases, however, feedback is used by the single organization that either solicits or searches for it. Unfortunately, groups that may
otherwise develop keen insights into their company’s business never get close to the data even if they have access to the CRM
application.
Meanwhile, customers don’t care how or why a given company is organized — they just provide feedback and assume the right
organization sees it and takes the responsibility for reacting to it. And what happens when customers assume incorrectly? What
happens when information systems cannot automatically leverage the information across the organization? Companies, despite
good intentions, risk ignoring and possibly alienating the very people who cared enough to share their thoughts on a potentially
important situation. Customers may conclude they’re opinions or patronage isn’t important.
Inside the company, the effect of not leveraging all the data stored in unstructured text of customer feedback can be enormous.
When the customer takes the time to give a company feedback, and much of that feedback is missed by the groups that need to
hear it the most, the company as a whole suffers.
III THE CUSTOMER FEEDBACK ORGANIZATION
As customers share their thoughts more frequently in more ways to more people, organizations are tasked with the increasingly
challenging responsibility to understand and react to the feedback. Many companies are learning the only way to be customer-
centric and to have a customer-driven business strategy is to leverage this feedback across the organization methodically,
comprehensively, efficiently, and effectively.
To do this, companies are staffing senior roles in the organization that focus on the customer and report to the CEO, the VP of
marketing or other top executive. While no standard group name has emerged, companies are calling this role VP of customer
loyalty, VP of customer champions, VP of the voice of the customer, VP of the customer experience or VP of customer satisfaction.
This customer-centered organization (sometimes made up of just a few people who manage and distribute customer feedback
across the organization) acts as the catcher’s mitt for customer feedback.
The goal of these groups isn’t to merely access and understand the information available in structured surveys or coded fields.
These groups are striving to make customer analysis a strategic part of the business. To do so, they must yield statistically
supportable findings from unstructured data for a new generation of executives and managers trained in and supportive of results
measurement. Typically, these groups analyze various forms of feedback coming into the organization and monitoring the
company, product and market-related buzz outside the organization. In some cases, these groups are also charged with building
the enterprise data warehouse (EDW), consolidating multiple data marts into an EDW, or creating a customer data mart that
contains a complete view of customers and their interactions with the company.
As these roles become more prevalent, and as these organizations begin analyzing the freeform customer feedback from multiple
sources, companies soon realize they have only been getting about one-fifth of the story. According to research from TDWI2, 80
percent of business data is unstructured information, and a large portion of that information comes from customers. As they
begin to grasp the size and importance of analyzing their customer feedback, companies realize they need to do two things:
1) Expand their analysis to the unstructured components of feedback that can answer such questions as why customers gave
certain survey scores, why they report specific service or product issues, and what — at least in their opinion — might be
done to improve or correct the situation.
2) Build processes that automatically understand and analyze the detail of the information found in unstructured data, which they
then can leverage throughout the organization to help make key business decisions by merging the results with those found in
structured data.
Russom, Phillip. BI Search and Text Analytics; New Additions to the BI Technology Stack. TDWI Best Practices Report, 2nd Quarter 2007; Renton, WA, 2007.
2
ATTENSITY WHITE PAPER Unleashing the Voice of the Customer 5
ATTENSITY PRIVATEFIGURE 2
Channeling THE MANDATE TO
Unstructured UNDERSTAND THE VOICE
Customer
Feedback OF THE CUSTOMER
A Financial Services
Company:
Financial services companies
today offer a full gamut of
financial products. To maintain
and grow their businesses,
they need to retain and grow
their current customer’s
“share of wallet.” These
customers are continually
offering valuable feedback to
the financial institution
through email requests, on
websites, when talking to
service representatives, or in
IV WHY KNOW WHY? more formal feedback chan-
Data warehouse and business intelligence implementations help organizations capture, store, nels like surveys. Financial
maintain, and report on customer feedback. Until recently, however, these efforts primarily services companies can gain
focused on the structured portion of the feedback — the scalar questions in a survey, the insight into new product
problem codes recorded during a service interaction, a formal rating provided by a customer, ideas, what customers are
and so on. The specifics and nuances of why a customer feels a certain way, recommends a saying about their products
product, or demands a return typically lies in the prose written by or about the customer in and services, whether they’re
the channels previously mentioned. This information can drive how companies react to likely to leave, if they may be
customer input as well as shift sales, marketing, and support strategies. interested in additional prod-
ucts like credit cards or
investment accounts, and
FIGURE 3
more through a deep and
Example of a Scale-Based Question3
thorough analysis of all that
feedback.
Figure 3 illustrates the scale-based question made popular in “The Ultimate Question” by Fred Reichheld. The question is simple
yet profoundly important: “Would you recommend us to a friend or colleague?” The answer tells companies how customers feel
they are being treated, if they are likely to return for more, and if they are willing to recommend the company’s products and
services to the people who are most important to them.
As part of research into customer loyalty and growth, Reichheld looked for a correlation between survey response and actual
behavior — repeat purchases and recommendations — that ultimately correlates to profitable growth and positive shareholder
value. From this question, a company gets a score — known as the Net Promoter® Score (NPS)4, which indicates in aggregate
how much of the customer base is willing to recommend the company and its products to their friends and colleagues. An NPS
also indicates, according to the book, how loyal a customer is to the company. In Figure 3, the customer clearly is not a promoter
given the low score. According to “The Ultimate Question” this customer is a detractor5. Now the company knows there is
something wrong. What the company does not know without supporting feedback is why the customer is a detractor. Why is the
customer dissatisfied, what could be done to satisfy the customer, and why?
3
Reichheld, Fred. The Ultimate Question: Driving Good Profits and True Growth. Boston, MA, HBS Press, 2006.
4
Reichheld, Fred. The Ultimate Question: Driving Good Profits and True Growth. Boston, MA, HBS Press, 2006.
5
Reichheld, Fred. The Ultimate Question: Driving Good Profits and True Growth. Boston, MA, HBS Press, 2006. pp 6-7.
ATTENSITY WHITE PAPER Unleashing the Voice of the Customer 6
ATTENSITY PRIVATEThe “why” is critical for companies that want to discover the root cause — and best solu-
tions — to product, service, marketing, and operational issues. The “why” offers context for THE MANDATE TO
the low score and provides companies their first opportunity to react appropriately to a cus- UNDERSTAND THE VOICE
tomer’s feedback. Should the company offer a refund? Should the company just apologize? OF THE CUSTOMER
Is there a bigger operational problem causing many customers to cite specific issues in their
feedback? This can only be driven by customer explanation — there is no other way to know. A Telecommunications
Company:
The need to truly understand customer feedback has prompted many companies to Customers now have the
explore how best to capture and analyze unstructured data. Their objective is to manage choice of who to use for cell
and analyze unstructured information seamlessly with structured data. Doing so enables phone, Internet and even
them to connect the reason why a customer gives a high or low score with a particular LAN services, making the
product, customer segment, or even an individual customer identified by structured fields need to retain and grow
such as customer identifiers, product SKUs, scalar feedback scores, and assigned codes. existing customers more
When companies connect structured data with the “why,” they have their first real opportunity competitive and crucial to
to see a complete view of their customers. Organizations that take on this challenge the successful growth of the
successfully gain access to finer details, deeper insights, and additional opportunities about telecommunications company.
their customers and products. Marketers and product
development in telecom want
to know which new products
FIGURE 4 Example of a Scale-Based
Question With the “Why” are going to be a success
and what the problems are
with current offerings,
Customer service executives
want to mitigate issues rapidly
and increase a customer’s
satisfaction level and willing-
ness to recommend products
to their friends and family,
while repair managers want
to fix issues and understand
issues coming down the pipe.
Understanding customer
feedback is critical for each
one of these roles driving
product development,
marketing, and service
decisions every day.
When a company analyzes a verbatim response, it not only gains a real understanding
about why this customer is a detractor but also discovers what could transform the
detractor into a promoter. Historically, the cost of saving a good customer is lower than
acquiring a new one, so transforming detractors is critical.
For the first response in figure 4, a simple call by the store manager to apologize for the
experience may be all that’s needed to restore a positive customer relationship. For the
second response, a different action from a different part of the organization is more likely
to turn the customer back into a promoter. Without the verbatim information, however, the
score provides a general sense of a customer’s sentiment but offers no specific insight into
a course of action to maintain or improve customer loyalty.
ATTENSITY WHITE PAPER Unleashing the Voice of the Customer 7
ATTENSITY PRIVATEV UNDERSTANDING UNSTRUCTURED CUSTOMER FEEDBACK
Today, companies can employ three common approaches to understanding verbatim data: search, statistical, and linguistic
analysis. Each approach offers a different level of granularity and is appropriate for different goals.
The Search Approach
Most popular with users, the search approach works best for finding keyword terms in a body of data. Feedback analysts
typically use search to test a hunch about a certain issue or rising sentiment on a specific topic. Using a standard search
process, analysts hunt for keyword terms that might give them clues about where and how often that topic is mentioned.
In the example in Figure 5, the analyst is searching a corpus of feedback to see if a certain product is mentioned.
Example 1: Searching for Feedback on a Product FIGURE 5 Typical Search Query
on Verbatim Feedback
Nokia 5300 XpressMusic Phone
Example 2: Searching for Issues About the Product
XpressMusic Phone and Carrier Issues
Analysts can iterate through a set of issues, entering keywords or phrases, testing their hunches about them, and then reading
the feedback that has come in from customers on the topic. This is useful in situations where the analyst wants to test a
hypothesis or research something already known and specific. However, the search approach begins to break down for the
analyst who:
• Has no hunches left
• Lacks time to read the details but still needs a clearer sense of the issues
• Cannot quickly produce a search query that returns any information
• Has different customers articulating the same issue using different terminology (such as “carrier problems” versus “provider
issues” versus “T-Mobile concerns”)
Search is good as an ad hoc tool for finding specific words in verbatim feedback. Search also works well as a filtering
mechanism to narrow queries within feedback results to a specific topic area. However, search cannot provide the analyst with
an in-depth understanding of what the customer is saying.
The Statistical Approach
Also used for both structured and unstructured customer feedback analysis, standard statistical approaches enable analysts to
identify issues and to understand the magnitude of occurrence. Statistical tools provide a conceptual understanding of feedback
in general terms. They compare the frequency of word occurrence within a document to the frequency of word occurrence in
general. For example, in a piece of feedback where a customer uses terms such as “happy,” the statistical approach would
count the occurrence of the word and then compare it to the average to determine if the document could be classified as
positive feedback.
Statistical approaches to analyze unstructured data include:
• General word counts and averages
• Categorization groups documents into categories (such as “quality issues”) based on the occurrence of predefined words that
illustrate the category (such as “defect” or “broken”)
• Cluster algorithms, such as K-means and Bayesian modeling, which put documents into groups whose members are similar
in some way and the data in the group or subset share a common trait (often proximity) according to some defined distance
measure
Statistical approaches are powerful in their ability to organize large amounts of unstructured feedback, which provides the analyst
with a sense of emerging themes and issues. For example, running categorization or a cluster algorithm against responses in a
feedback survey might uncover a lot of feedback centered on a specific issue with one or more products. The statistical run
might find a significant occurrence of something “breaking” or “failing.” With this information, the analyst can then review that
specific group of feedback, reading the responses to understand how and why the breaking or failing action occurred. This is
very useful for analysts to rapidly uncover general sentiment or product issues.
ATTENSITY WHITE PAPER Unleashing the Voice of the Customer 8
ATTENSITY PRIVATEUnfortunately, statistical approaches begin to break down when specificity and accuracy
become critical in analysis because: THE MANDATE TO
• A general category or group offers no native dimensionality. It might provide a general label UNDERSTAND THE VOICE
for a set of feedback, but it doesn’t explain why, how, when, and under what condition the OF THE CUSTOMER
issue occurred. The dimensionality is the information that drives action by the organization
that receives the feedback. A Travel Services Company:
• It can produce false positives or negatives. The statistical approach might indicate that The travel industry has been
positive words like happy, cool, or great appear in feedback and classify the feedback as turned upside down with the
generally positive. In common speech communication, negations — particularly in cases advent of the Internet.
when they are not close to the word they modify — are missed. For example, a statistical Consumers can make their
approach to the sentence, “I’m not really very happy,” could be misclassified as positive. own reservations, search for
• When different customers articulate the same sentiment in various ways, statistical cheap fares, design their own
algorithms might miss the connection and fail to classify or group the feedback together travel packages and change
correctly or even at all. things on the fly. With this
new order comes self-service
Statistical analysis is a great way to rapidly organize unstructured feedback. It does not require offerings from hotels, airlines,
a lot of knowledge about what is in the feedback. Unlike search, it does not require users to car rental companies and
first decide what they want to find. And it provides the users with a general sense of the more as well as a new set of
themes of customer feedback. However, statistical approaches lack the granularity necessary companies that provide travel
to advise managers on the actions that need to be taken to better serve customers. aggregation services for con-
sumers online. The ease that
The Linguistic Approach the Internet provides makes
Natural language processing (NLP) is a linguistic approach to analyzing verbatim customer it easy for the consumer to
feedback. NLP provides analysts with the most granular and factual understanding of the shop around, looking for the
feedback and provides the most insight to drive the organization towards action based on the best combination of service
feedback. To achieve this level of understanding of customer feedback, systems have evolved and price. Knowing how
beyond counting the occurrence of terms or features to being able to identify the linguistic customers feel about travel
roles and relationships among words, terms and facts. Treating language as a linguistic rather products, the process to buy
than statistical phenomenon is challenging to achieve in the binary world of computing these products and their
because it involves symbolic processing. requirements for new prod-
ucts allows travel companies
The challenge intensifies when dealing with “real world” language: unknown terminology, to gain the competitive edge
run-on sentences, sentence fragments, misspellings, and poor grammar. Systems that offer a that drives repeat business
factual understanding enable organizations to conduct analytic work that involves tabulating, and loyalty. This information
calculating, comparing, charting, and graphing feedback at a level granular enough to make from customers is typically
a business decision. This approach also increases the accuracy of the analysis relating to hidden in the text.
false positives or negatives while providing the detail required for a company to know why a
customer gave certain feedback.
TABLE 1 Natural Language Processing Based Extraction of Facts From Customer Feedback Using
Attensity’s Voice of the Customer Solution
Example Feedback Sentence Facts Extracted Using NLP
“I sent a request to close the account Fact: account : close [ASAP]
immediately.” Time: immediately
“The staff was incredibly professional.” Fact: staff: professional [more]
“I am not very happy with your service.” Fact: service: happy [not]
Table 1 illustrates simple facts extracted from text using a linguistic approach. In these examples,
the linguistic approach goes beyond recognizing and counting that a word exists to actually
defining the “who, what, where, and when” about the word. Using the statistical approach, the
word “happy” in the last sentence would have been counted as positive feedback. With the lin-
guistic approach, the correct sentiment behind the word happy is captured because the software
understands “not” modifies “happy” even though the two words are separated. In this case, only
a linguistic approach correctly identifies the service experience as negative.
ATTENSITY WHITE PAPER Unleashing the Voice of the Customer 9
ATTENSITY PRIVATEVI RECOGNIZING “VOICE” FOR ACTIONABLE DATA
Complicating customer feedback analytics even further — which makes the linguistic approach even more powerful — are
customers who do not all use the same words to describe their opinions, issues, thoughts, or feelings. Customer sentiments and
issues are not always recorded using perfect grammar and in the same tense. There are many different “voices” customers can
use — such as negative voice, urgent voice, and conditional voice — when articulating their experiences and opinions about a
company’s products and services. These voices provide additional information and can even change the meaning of the feed-
back. The change, whether subtle or extreme, can provide crucial insights into what customers are trying to tell a company. This
additional information comes in the form of adverbs, modals and even clause markers. Analyzing voice types through a linguistic
approach identifies information that other approaches would never discover in unstructured customer feedback.
Table 2 shows examples of the many different voices a customer can use when communicating.
TABLE 2 Examples of “Voice” Types Found Using the Attensity Voice of the Customer Solution
Voice Type Captured By Attensity Example
Augmented
The staff was incredibly professional.
to enlarge meaning as a superlative does; really
Fact: staff: professional [more]
unhappy, seriously ticked, over-inflated
Diminished
The tractor barely works.
diminish or constrain meaning, lowered expectations,
Fact: tractor: work [less]
under-inflated
Urgent
Please call customer at once.
depicts urgent nature of feedback/request; fixed now;
Fact: customer : call [ASAP]
fix it asap
Recurrence
My Web browser often crashes.
action has happened before or is ongoing; tried to fix
Fact: web_browser: crash [again]
it again, three times now, still happy
If he calls customer service, then we can fix the problem.
Conditional
Fact 1: customer_service: call [if/then]
if/then
Fact 2: problem: fix [if/then]
Indefinite Customer might exchange his broken headset.
depicts uncertainty; probably called, might exchange Fact: headset: exchange [maybe]
Intentional
I want to order model-XB311.
depicts intentions or desires; will be returning, plan on
Fact: model-XB311 : order [intent]
returning
Question
Has the department issued my refund?
form of a question, communicates requests for goods,
Fact: refund: issue [?]
services, information and instructions
Negation He never fixed the icemaker.
negate the meaning of the Mode Fact: icemaker: fix [not]
Customers use many nuances to articulate emotion, opinion, and requests. These nuances affect the entire meaning of the
response. Companies that don’t automate the linguistic analysis of feedback may miss nuances necessary to compile accurate
data, mine key business insights, take appropriate action, and make the most of customer feedback.
ATTENSITY WHITE PAPER Unleashing the Voice of the Customer 10
ATTENSITY PRIVATEVII ATTENSITY’S AUTOMATED CUSTOMER FEEDBACK APPLICATION
THE MANDATE TO
Attensity’s Automated Customer Feedback Application enables businesses to unleash the
UNDERSTAND THE VOICE
voice of the customer locked in unstructured data. The application is comprised of Attensity’s
OF THE CUSTOMER
core technology platform with specific voice of the customer (VoC) elements including:
A Civilian Government
• Attensity’s Voice of the Customer Domain is a voice of the customer solution specific
Agency:
dictionary and term library.
Many federal, state, and local
• Attensity’s Semantic Voice Technology extraction engines are tuned for VoC-specific
government agencies have
language and syntax patterns — “voices.”
thousands of customer serv-
• Attensity’s Voice of the Customer Analytic Tools are management dashboards and
ice agents in call centers to
analysts views that cover VoC data including customer sentiment, customer satisfaction,
serve civilian needs every
Net Promoter details, new product introduction facts, and more.
day. Seniors can call Medicare
• Attensity’s Model Factory™ is a tool that promotes facts, variables, and flags to predictive
to learn more about their
and other statistical models for use in churn, segmentation, and other predictive analytics.
regions treatment policies or
to complain about a provider.
At the core of the Attensity VoC solution are Attensity’s patented extraction engines, which
Veterans can call the VA to
mine and transform various forms of unstructured information into a structured form. The
ask about services. Citizens
solution then creates output in XML or in a structured relational data format. This output is
can report issues to the EPA
fused with existing structured data and made a part of the company data warehouse or data
or the CDC. The list goes on.
mart. The newly structured data then can be accessed, analyzed and acted on by various
Feedback, questions, and
departments to drive customer-focused business objectives. Figure 6 illustrates how different
requests come in every day
organizational groups leverage fused data.
by the thousands. Customer
service agents capture them
as notes. Each federal agency
is required to not only
respond to the specific issue
but to look at the issue in
aggregate, understand
sentiment trends, identify
issues with services provided
by both government and
non-government providers,
and to take action when
appropriate.
FIGURE 6 Attensity Fuses Facts Extracted From Unstructured Text With Existing Structured Data to
Create a 360-Degree View of Customers and Their Feedback
Altogether, Attensity’s extraction engines offer a comprehensive approach to transforming text
into structured data for analysis. Attensity’s extraction technology includes search, statistical,
and linguistic approaches to analyze the voice of the customer. Feedback analysts can use
Attensity search technology to test hunches and rapidly find information about known issues
in text. They can use Attensity’s statistical offering to understand the general occurrence and
magnitude of issues. And they can use Attensity’s patented linguistic approach to gain a rich,
actionable understanding of feedback. Figure 7 illustrates the Attensity text extraction process.
ATTENSITY WHITE PAPER Unleashing the Voice of the Customer 11
ATTENSITY PRIVATE1 Unstructured Data 2 Fact Extraction 3 Analyze Answer business FIGURE 7
ATTENSITY’S TEXT
questions hidden in text EXTRACTION PROCESS
Are customer satisfied Attensity extracts facts
Surveys, emails, with our service? Why not? from your text providing
answers to key business
web forms,
Will customers recommend questions, once hidden
service notes, etc.
you to others? Why? in text
Attensity BI Applications
Extracts Facts from Text Attensity for Text Discovery Are we offering the right
and Relationalizes Them and Analysis products & services?
•
Business Intelligence and
How do people feel about
Modeling Applications
Data for Cross Business
the services we provide?
Warehouse Reporting and Analytics
Attensity offers three linguistic approaches to text analytics, each of which is designed for a specific goal:
• Entity extraction identifies entities or pre-defined lists of words or phrases about people, places or things.
• Targeted event extraction mines predefined event specific roles or themes — cause and effect from text.
• Exhaustive fact extraction automatically extracts facts — events, actions, things and behaviors from text — without pre-definition.
While targeted event extraction offers the richest results with the greatest potential reduction of linguistic expression, it also
requires the most implementation effort. By definition, this technique assumes that the organization has identified which events
(such as product or service issues) the system should extract. Event definitions enable a wide range of linguistic expressions to
be mapped to a standard set of events and attributes.
Other events, such as emerging customer issues, cannot be pre-defined because analysts won’t have enough pre-existing data
to be aware of the problem. In those cases, Attensity’s patented Exhaustive Extraction™ approach is a uniquely valuable
mechanism supporting exploration and discovery. Because this approach requires no event definitions, the burdensome and
time-consuming task of specifying each event is virtually non-existent.
Figure 8 highlights the breadth of Attensity’s key extraction capabilities in the illustration’s Attensity Server tier. Attensity offers the
widest breath of text extraction approaches to meet the many needs of customer feedback analysis discussed in this paper. The
diagram also highlights Attensity’s business user applications, dashboards and views.
Sentiment Net Product Quality Customer FIGURE 8 The Attensity Automated
Provides VOC Analytics Customer Feedback Application
Satisfaction & Promoter & New Product & Market
Dashboards & Views
Analytics Analytics Intro Analytics Buzz
Includes Business User
EXPLORE : VOC SEARCH : VOC VOC Applications
Industry Standard
Populates a Data Warehouse/
Relational Databases Mart & Fuses Text Facts with
HP Oracle Teradata IBM Microsoft MySQL Structured Data
MODEL FACTORY Populates Predictive Models
with Facts from Text
Exhaustive™ Targeted Statistical
Attensity Server Processes the Voice of the
Customer via Patented
Semantic Voice Engine Text Extraction Engines
Voice of the Customer (VOC) Domain
CUSTOMER FEEDBACK CHANNELS
Analyzes a Wide-Range of
Customer Data Sources
Email Surveys Chats Blogs, Web Customer Repair
Forums Service Notes Notes
ATTENSITY WHITE PAPER Unleashing the Voice of the Customer 12
ATTENSITY PRIVATEAttensity’s Voice of the Customer Domain
Attensity’s Voice of the Customer Domain is the culmination of research and real-world data combined to create a foundation for
Attensity to accurately extract information from customer feedback. It includes common terms, abbreviations, morphologies,
classification, and category sets for capturing customer opinions and experiences. Unstructured feedback data runs through
Attensity’s customer domain-driven engines to mine facts from customer feedback that are easy to understand and analyze.
Automatic categorization of feedback enables analysts to rapidly and accurately find trends and themes in mountains of
unstructured feedback. Attensity also organizes the output so managers have a construct for looking at feedback over time.
Out of the box, some supported categories include:
• Positive and negative communication
• Positive and negative sentiment
• Positive and negative responses at the product and service level
• Requests for information about opening an account, ordering goods and payments
• Return and discount requests and issues
• Types of goodwill gestures
• Positive and negative staff feedback
• Follow-up, action requested, cry for help
Attensity’s Semantic Voice Engine
The Attensity VoC solution also includes Attensity’s Semantic Voice engine, which is tuned to accurately identify the many voices
a customer uses when articulating feedback. The engine provides additional information about the tone of the feedback, which
is critical for getting an accurate picture of what the customer is trying to say. Attensity’s Semantic Voice Engine provides
companies crucial insights into their data as it can be used to distinguish information that would be lost in any non-linguistic
based method of text analysis. Attensity has built into its engine the most robust set of voices in the industry which enables it to
provide in-depth customer feedback analysis, making the application a leading offering for customer feedback analysis.
Attensity’s Model Factory™
Someone analyzing a single piece of feedback, such as an email or the text fields in a survey response, can easily detect the
written clues about customer’s potential to churn, return products, promote the company, or expand their relationship with the
company. However, a single person can hardly be expected to parse and process the thousands upon thousands of customer
comments, requests, or demands large organizations collect monthly. In those cases, analysts may never find the clues hiding in
their verbatim feedback. Attensity’s Model Factory makes these unstructured indicators available to customer segmentation and
predictive models. Attensity’s Model Factory is a tool that promotes facts, variables, and flags to predictive and other statistical
models for use in churn, segmentation, and other predictive analytics.
Attensity’s Voice of the Customer Analytic Dashboards and Views
The cornerstone of Attensity’s Automated Customer Feedback Application is the Voice of the Customer analytics tools that offer
business managers and feedback analysts rich and actionable information about customers. Attensity’s Voice of the Customer
analytic dashboards and views include:
• Critical customer feedback data on customer sentiment, customer satisfaction, Net Promoter details, new product
introduction facts and more
• Automated alerts that notify customer feedback analysts, service representatives, marketers, and other constituents about
emerging issues, problem areas, or any important information they choose to receive
• The ability to analyze unstructured feedback in conjunction with structured data including specific customers, customer
segments, products, and more to paint a complete picture of a customer’s characteristics and behaviors in conjunction with
their thoughts and opinions
• Easy drill-through access to underlying verbatim text for additional context as required by the analyst
ATTENSITY WHITE PAPER Unleashing the Voice of the Customer 13
ATTENSITY PRIVATEAttensity offers views and dashboards for analysts and managers
that cover some key questions companies want to understand
out of customer feedback. They are focused on providing
actionable information so managers can clearly see strong and
weak points, major issues and areas for improvement, and more.
These views and dashboards include:
• Customer Satisfaction and Sentiment
– Customer sentiment detail by products, services, customer
segments and any available structured fields
– Churn: identification of churn indicators and information
around how many and which customers are potentially
going to churn
– Cries for help: topic areas where customers are asking for
some immediate action FIGURE 9 ATTENSITY AUTOMATED CUSTOMER FEEDBACK
• Net Promoter7 APPLICATION EXAMPLE DASHBOARD Attensity provides
dashboard views of the insight extracted from verbatim customer
– Promoter and detractor sentiment and why
feedback, surveys, blogs, email, service notes, and more.
– Promoter and detractor themes, related issue root cause
• Product Quality and New Product Introduction
– Product introduction: general sentiment about new products, requests for new features, and issues with the new offering
– Product issues: information about top product issues including reliability, defects, and safety issues
– Early warning and alerts: early notification on new product issues and specifics around the issues
• Customer and Market Buzz
– Initial buzz: early views on the market chatter about new products and services, typically derived from blogs and
online forums
– Marketing messages: feedback regarding marketing messages and positioning from the customer
VIII CONCLUSION
Customer feedback contains critical information needed to drive businesses. Information flows into organizations through many
channels and to many functional departments. The vast majority of it is unstructured prose contained in service notes, emails,
survey responses, blogs, and more. Getting a complete picture of customer sentiment, product, and service issues as well as
general customer satisfaction is a serious challenge. Technology is evolving to the point where companies can now effectively
and efficiently gain significant value by driving business strategy and competitive differentiation through the analysis of large vol-
umes of unstructured feedback data coming from these multiple sources. The power of this untapped reservoir of mission-critical
information is resulting in many market leaders forming customer feedback organizations as strategic operating units. These
newly formed organizations are playing a significant role in driving an organization’s strategy tied to products, services, markets,
communications, and employees.
Many different approaches can be applied to understanding unstructured customer feedback. The most common approaches
are search, statistical, and linguistic analysis. Understanding the true “voice of the customer” typically requires a combination of
each of these approaches. While each approach has benefits and is appropriate for achieving different goals, natural language
processing (NLP) provides the only means for understanding the underlying “why” around a customer’s feedback and resulting
actionable information. Attensity’s Automated Customer Feedback Application offers all of the key elements required to under-
stand, analyze, and communicate VoC findings and is based on Attensity’s core technology platform, which combines search and
statistical methods with an NLP engine that provides companies with a deep and actionable understanding of customer feedback.
Reichheld, Fred. The Ultimate Question: Driving Good Profits and True Growth. Boston, MA, HBS Press, 2006. pp 6-7.
7
Attensity’s text analytics software rapidly and accurately transforms unstructured data into valuable, actionable information. Global enterprises and government
agencies automatically extract all the facts from freeform text, integrate these facts with structured data and leverage the fused data set to make decisions using
Attensity’s text analytics suite. The company's patented Exhaustive Extraction technology enables investigators, analysts, managers and executives to speed
detection and response to critical events and issues related to intelligence analysis, insurance claims analytics, service and warranty analysis, customer care,
anti-money laundering and fraud detection. Attensity teams with Hewlett Packard, Business Objects, IBM and Teradata to offer comprehensive solutions integrated
with data warehousing and business intelligence systems. Attensity is a 2007 winner of the Red Herring 100 North America award, an honor reserved for top private
technology companies. The company is headquartered in Palo Alto, Calif. with a technology center in Salt Lake City, Utah. More information is at www.attensity.com.
Corporate Headquarters 3600 West Bayshore Road, Suite 200 • Palo Alto, CA 94303 Phone: (650) 433-1700 Fax: (650) 433-1799
Technology Center 440 West 200 South, Suite 450 • Salt Lake City, UT 84101 Phone: (801) 532-1125 Fax: (801) 532-1164
Government Systems 8400 Westpark Drive, Suite 100 • McLean, VA 22101 Phone: (650) 433-1712 Fax: (650) 433-1799
© 2007. All rights reserved. Attensity, Exhaustive Extraction and the Attensity
logo are trademarks of Attensity Corporation. All other company or product
names may be trademarks and/or registered trademarks of their respective
organizations.
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