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TRANSCRIPT
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tVolume 4Number 1
Mark Keeley
Managing Partner, Telecom & High Tech Practice
Strategy & Business Analytics
312.255.5642
Contact:
© 2004 DiamondCluster International, Inc. All rights reserved.
This Viewpoint was prepared by Steve Rudolph, Amaresh Tripathy, and Michel DiCapua.
Steve Rudolph
Principal, Telecom & High Tech Practice
Strategy & Business Analytics
312.268.3752
OR
I.
II.
III.
IV.
V.
The discussion includes:
Need for Strategic Root CauseAnalysis in Customer Service
Challenges of Strategic RootCause Analysis
Identifying Drivers ofDissatisfaction – Crafting theSolution
Benefits
Can Your Company Find the RootCauses of CustomerDissatisfaction?
A Root Cause Analysis Strategy forImproving Customer Satisfaction
Introduction
In the largely agrarian 17th century economy,
farmers struggled to improve centuries-old
inefficient processes. Traditionally, seeds
were thrown, or "broadcast," which made it
difficult to weed and harvest the crop. Finally,
farmers used a "dibber," a board with holes
evenly spread apart, for planting crops. A
stick would be pushed through the holes, and
a seed placed in the hole made by the stick.
This was effective and targeted but also
tedious and time-consuming. It was during
this time that a farmer named Jethro Tull (in
addition to inspiring flute-playing rock-and-
rollers everywhere), invented the seed drill
using parts from the foot pedals of his local
church organ. Tull's drill sowed seeds in
uniform distance and the runners on the drill
made holes that allowed the seeds to be
sowed at uniform depths. It also enabled a
consistent, precise method for harvesting
since the seeds would grow in predictable
patterns. Ultimately, his invention enabled
mechanized farming and is recognized as one
of the driving forces of the industrial
revolution.
In many ways, customer service operations
today are riddled with the same inefficiencies
that were faced by 17th century farmers. This
paper examines the successful, innovative and
cost-effective solution one customer-centric
company deployed to identify and address the
causes of customer dissatisfaction.
“What Can Jethro Tull Teach Us
About Customer Service?"
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2
Despite all of the money spent on customer
satisfaction and CRM systems, many
companies are no closer than they were 20
years ago in understanding the specific drivers
of customer dissatisfaction. With only 16% of
companies stating that CRM implementations
result in "measurably improved business
performance," the most basic questions still
remain unanswered:
Just as Tull's seed drill mated the range
afforded by the broadcasting process with the
targeted precision offered by the dibber,
answering customer service questions
requires a strategic approach that is at once
wide-ranging and targeted.
This paper outlines a root cause analysis
strategy developed by DiamondCluster in
collaboration with a national
telecommunications carrier
.
1
to address
customer service issues
1Krass, Peter, "CRM: Once More, Without Reeling," , March 17, 2003. Citing AMR Research, Krass notes
the following percentages of companies achieving various levels of success with CRM implementations: 12% - Failure:
started but failed to go live; 47% - Implemented: went live, succeeded in the technology aspects, but business change
and adoption failed; 25% - Adopted: Succeeded in both adoption and systems, but could not quantify business benefit;
16% - Improved performance: Reached the promised land. It measurably improved business performance.
CFO Magazine
�
�
�
�
Why are customers dissatisfied?
Why are customers calling care?
Why do customers defect?
And most importantly: What can we
do to increase customer satisfaction
cost-effectively?
Root cause analysis reveals the underlying
causes behind customer dissatisfaction and
leads to targeted actions to resolve them. It is
based on the understanding that strategic,
rather than merely operational, methods are
required if companies aim to be serious about
solving the challenges of customer
satisfaction.
The strategy follows the design and
implementation of "code-based intervention."
Code-based intervention enables targeted call
monitoring and analysis by employing a
coding schema for inbound calls that is then
matched to actual call recordings. In this way
the carrier can hypothesize about root causes
and subsequently validate, quantify, and
tactically respond to these problems. When
combined with existing primary and
secondary research tactics, code-based
intervention greatly increases a company’s
confidence in decisions that have far-reaching
impacts on customer satisfaction, retention,
and the cost of service.
Implemented for less than $1million, the
solution has allowed the carrier to identify
over $60 million in variable cost savings
within the first 6 months after
implementation.
I
3
Need for Strategic Root CauseAnalysis in Customer Service
Conventional approaches to customer
dissatisfaction have encountered the
weaknesses of either the broadcasting
technique or the dibber. Traditionally,
companies have used primary research tools
such as customer surveys and focus groups to
understand customer dissatisfaction. They
rely on third-party firms who are divorced
from the day-to-day business operations. A
broad analysis such as a closed-ended
customer satisfaction survey may capture
general feedback: customers are disgruntled
with the company's customer service; or
customers primarily choose products based on
price; or the company suffers from an overly-
conservative brand. But these types of results
are often already obvious or are not specific
enough to be actionable. Additionally, self-
selection or other biases to which surveys and
focus groups are prone may skew the results.
Similar to broadcasting, there is no efficient
and clear way of harvesting actionable
information from this process.
On the other hand, more rigorous hands-on
analysis, such as live monitoring of inbound
calls, suffers from the same issues as the
dibber. It is very time-consuming and
expensive to make the process effective and
actionable. A small sample of calls may give
the listener insightful detail into particular
customer concerns but it may not be
representative of the customer population as
a whole. Conversely, the call may be
representative of the customer population but
due to the broad range of issues covered in
the calls monitored, may not be sufficiently
detailed or actionable.
As the shortcomings of these methodologies
are exposed, the need for a strategic
approach to customer service becomes even
more important. Barriers to switching
between products or service providers are
constantly falling. The growth of the Internet
has empowered customers with access to a
wider range of consumer choices in an
increasingly competitive business
environment. Customers are becoming savvier
in their choices and would not give a second
thought to switching loyalties when faced
with a situation where their needs are not
consistently met. These current realities,
coupled with regulatory changes, such as
number portability in telecommunications and
HIPAA in the healthcare industry, make
dissatisfied customers even more likely to
defect. In short, companies in many industries
need to be able to identify drivers of
dissatisfaction in order to reduce costs,
increase customer satisfaction, and prevent
defection (churn). Moreover, companies need
to strike a careful balance between cost
reduction and the potential loss of customer
satisfaction. In most cases, this is easier said
than done.
“Companies in many industries
need to be able to identify
drivers of dissatisfaction in order
to reduce costs, increase
customer satisfaction, and
prevent defection.”
4
A wireless telecommunications operator was facing high call
volume in customer service operations and declining customer
satisfaction. The average call per subscriber was above the
industry benchmark. With the cost between $3-$5 per call,
customer service operations were a significant portion of their
general and administrative expense. Realizing that customer
service is a key competitive lever in an industry with declining
margins and high churn, the operator decided to elevate their
customer service operations from a tactical to a strategic level.
The carrier charted a three-step process:
Creating a coding schema for inbound calls, allowing the
operator to categorize the root cause prompting a customer call
and to obtain a "radar view" of the issues;
Developing a statistically significant tentative causal model
for the relationship between coded events and their value (i.e.
hypothesize and prioritize);
Intervening when the causal relationship is established
("deep-dive" analysis) – and creating strategies/tactics to
address and resolve the issues.
The solution integrated statistical process analysis with off-the-
shelf technology already in use by the call centers. It also
required staffing a specialized root cause analysis team to
support the ongoing process. The solution allowed the carrier to
conduct accurate, actionable, cost-effective root cause analysis
for the first time. This paper explores the challenges presented
by strategic root cause analysis, how the solution created by the
organization met their challenges, the benefits achieved, the
obstacles that were faced in implementation and execution, and
the applicability of this solution to identifying drivers of
dissatisfaction in other industries.
1)
2)
3)
Keeping Customer Service a Key Competitive Lever
CASE STUDY
II
5
Many companies perform some type of root cause analysis to drive out the underlying reasons of
customer dissatisfaction; however, most do not validate and quantify their hypotheses prior to
implementing proposed solutions.
Strategic Root Cause Analysis Overview
A
B
C
D
E
F
An
aly
zeim
pa
ct
of
resu
lts
tod
ete
rmin
efu
ture
imp
rove
me
nts
Gather Data
GenerateHypotheses
Validate& Quantify
DevelopAction Plan
ImplementActions
Track Results
Objective
� A lightweight front-end to existingoperational and transaction systems toallow a faster, more flexible application
� Generate hypotheses as to theunderlying causes of customerinteractions based on available datasources
� Validate hypotheses through directobservation (call monitoring, marketsurveys) and statistical analysis
� Identify the “quick hits” and long-termchanges required to optimize thetargeted customer interaction
� Form teams to implement requiredactions across the organization
� Track the impact of the results anddetermine if the root cause has beenaddressed
Outputs
� Summary of available data; determinelevel of statistical accuracy andactionability
� Brief articulation of several keyhypotheses that need to be validated ordisproved
� Quantification of detailed reasons forroot causes of customer interactions
� Detailed workplan: timeline,dependencies, and responsibilities
� Implementation of identified actions
� Resolution of problem or generation ofnew hypotheses if not resolved
Typical Issues
� Current data sources and collectionmethods require revision to ensureaccuracy and actionability
� Hypotheses often based on incompleteor anecdotal evidence (e.g. Executivecomplaints, one-off call monitoring)
� Resources and methodology notavailable to perform “deep dive” ofstrategic root causes of dissatisfaction
� Determining proper action difficultwithout understanding of specific,actionable causes of dissatisfaction
� Implementation cannot be prioritizedcorrectly without validating scope orscale of problem
� Data sources and tracing methodologynot available to track results and isolateimpact of actions
Challenges of Strategic Root Cause Analysis
Do you ever wonder why you often hear the
message, "Your call may be monitored for
quality assurance purposes," but you seldom
hear, "This call may be monitored for the
purpose of improving customer satisfaction?"
Most likely, the reason is that many
companies perform primary analysis for purely
operational reasons. The carrier in the case
study (p.4), for example, was already
performing call monitoring on a regular basis.
Nonetheless, when it did so, the monitoring
was typically targeted at the agent level,
enabling call center managers to grade their
specialists. Call monitoring for root cause
analysis purposes, on the other hand,
represents a strategic version of primary
analysis. Indeed, many companies are not
accustomed to leveraging a tool such as call
monitoring for strategic purposes such as
understanding and improving customer
satisfaction.
Yet even for those companies that do use
primary analysis to address customer
satisfaction, it is extremely difficult to
validate and quantify the extent of customer
satisfaction issues, forcing most companies to
jump from hypothesis generation immediately
to implementation. Often, problems are
identified through anecdotal information – the
"problem of the moment" that catches the
attention of a customer service supervisor or
department VP.
6
There are typically two problems encountered
by companies that attempt a systemic
validation and quantification of hypotheses
through primary analysis. The first problem is
that many forms of primary analysis have
basic statistical biases. An outbound survey
of sources of customer satisfaction and
dissatisfaction may evoke responses from
only the most frustrated customers with the
most reason to vent (or, it may be asymmetric
on the other side, drawing responses only
from the most pleased customers who are
happy to contribute a short amount of time to
their preferred service provider). If the first
problem with primary analysis conducted
through surveys and focus groups is the
slippery slope of bias, the second problem is
that primary analysis conducted too broadly
(e.g. through generalized call monitoring)
gives statistically valid and believable results
but which are often too obvious and
generalized to be actionable.
Based on our extensive work with
telecommunications carriers there are two
alternatives they typically consider in
executing primary analysis through call
monitoring that is at once broad enough to
give statistically valid, unbiased results as
well as detailed enough to provide actionable
conclusions. Either the company hires a large
team dedicated exclusively to monitoring and
analyzing large, random sets of calls, or the
company tries to implement sophisticated
automated tools to perform these functions.
Both solutions turn out to have drawbacks.
Hiring a team of call monitors is prohibitive
due to labor costs and the massive amount of
data that requires analysis. Automated call
monitoring tools have various strategic or
technological shortcomings. For example,
text-mining software (which combs through
notes typed in by specialists as they listen to
calls) is vulnerable to inaccuracy, sensitive to
user-defined key words, and prone to human
typing errors. Also, notes entered by
customer service reps often contain the
solution to the problem (e.g. "gave customer
$5 credit"), rather than the root cause of the
trouble (e.g. "customer stated that they were
misinformed of service fee"). On the more
technical side, voice recognition systems
would be useful but may not yet be ready for
implementation.
It is clear that sound root cause analysis is
required to strategically address customer
dissatisfaction, and that such analysis is
dependent on some form of primary analysis.
But it is also clear that conventional call
monitoring approaches either do not make
economic sense or fail to provide significant
and/or actionable value. Instead, a winning
solution for customer satisfaction root cause
analysis would deliver a complete,
statistically valid picture of the issues; it
would lead to actionable recommendations;
and it would be capable of being implemented
at a manageable cost.
“A winning solution for customer
satisfaction root cause analysis
would deliver a complete,
statistically valid picture of the
issues.. ”.
III
7
Creating a call library of recorded calls coded by call reason allows a team of analysts first to
obtain a “radar view” of major call drivers and then to perform a “deep dive” analysis to identify
actionable underlying causes of customer dissatisfaction.
A Strategic Root Cause Analysis Solution
Workstep
Output
Issues
Obtain “Radar View” ofCall Drivers
�
�
Select random sample of CSRs to codecalls with standardized call reasons
Record 100% of these CSRs’ calls
�
�
“Radar view” providing directionalguidance as to call drivers
Ability to accurately track month-to-month changes in call reasons
�
�
Sample of CSRs cannot contain bias
Sample size of coded calls must besufficient to track changes over time
Create a Call Library
�
�
Store 3-6 months of recorded calls incentral database
Match list of coded calls to therecordings of those calls
� Library of coded calls allowing primaryresearch team to select specific callreasons to monitor
� Technical issues such as storagespace for recordings need to beresolved early on
Perform “Deep Dive” Analysisto Identify Call Reasons
�
�
Perform primary analysis on specific calldrivers through targeted call monitoring
Perform more complex, secondary analysesusing customer information databases
�
�
Actionable, accurate insights into root causereasons of customer dissatisfaction
Validation and quantification of root causesprior to launching initiatives
� A mix of primary and secondary analysisrequired to identify and validate call driversand quantify their financial impact
reason 1reason 2reason 3
1 – Billing education
2 Network coverage
3 Dispute charges
4 Change plan
5 Other
–
–
–
–
Generate new hypotheses
Validate previous hypotheses
Track impact of initiatives
Identifying Drivers of Dissatisfaction –
Crafting the SolutionThe solution to identifying accurate,
actionable drivers of dissatisfaction is a
comprehensive methodology and underlying
system that begins at the level of reasons for
customer calls and culminates in detailed
customer experience-enhancing initiatives
with quantifiable ROI. The solution involves
statistically rigorous call reason tracking
matched to highly-targeted call monitoring
efforts and is structured on three components:
1)
2)
3)
A call reason tracker which presents a
running "radar view" of the reasons behind
customer calls;
A call library of recorded customer calls
that enables rapid, efficient, targeted "deep-
dive" call monitoring;
A root cause analysis team to perform
"deep dives" into specific call reasons through
targeted call monitoring.
8
The combination of the "radar view" to detect
the major call drivers with the "deep dive"
analysis to pinpoint the underlying causes
behind these calls gave the carrier an
efficient, results-oriented strategy for tackling
customer service issues. From this strategy
emerged recommended actions the carrier
could adopt to increase customer satisfaction.
Examples of these actions included
improvements to the customer experience for
credit-challenged customers, resolution
management for the most frequent callers,
and evaluation of potential call savings that
could be achieved from specific IT
investments.
To see how these types of recommendations
could be driven from the strategic root cause
analysis solution, let us examine each of the
solution's components in more detail.
The first component is a call reason tracker,
which records call reason frequencies of a
random representative sample of calls each
month. The application is designed to detect
slight but statistically significant deviations
among these call reasons from month to
month. Data generated from this tool proffers
a "radar view" – current and historical – of
call reason patterns across the enterprise,
allowing the carrier to identify major reasons
for customer calls, anticipate growing
problems in the customer base, and quantify
the costs attributable to different call reasons.
For example, "10.9% of all calls in April were
billing inquiry calls" or "7.2% of calls were
from customers inquiring about their minute
balance.”
But while these results provide a statistically
sound view of the big picture, real root cause
insights can only be found in the details. It's
good to know that many customers are calling
with billing issues, but what particular aspect
of the billing process can be repaired to
dramatically lessen this pain point?
Here is where the strategic component of the
solution fits: highly targeted call monitoring.
Once executives have studied the "radar view"
of call reason results and identified issues or
customer segments of concern, a call
monitoring team performs "deep-dives" into
the issue, validating and quantifying
hypotheses for root causes. This team,
comprised of five full-time employees with
analytical expertise and operational
experience, is charged with driving root cause
insights from listening to calls, carrying out
statistical validation of results, and delivering
actionable recommendations. These
recommendations are uniquely tailored to
address specific root causes and come with
quantified projections of the impact that could
be expected from their implementation.
The third component, a call library of recorded
calls, allows for targeted call monitoring and
thus fills the gap between the "radar view" of
the call reason tracker and the "deep dive" of
the call monitoring team. While it is easy to
gather a large set of previously recorded calls
that the call monitoring team could access to
perform the call monitoring deep-dive,
providing the user with a targeted call sample
is a trickier matter. Trying to locate among a
large set of randomly-recorded calls a valid-
sized sample of calls that fit the call reason or
customer segment under investigation is
simply too time-intensive and costly. The
solution to this challenge was a "call library,"
a catalogued inventory of recorded calls,
searchable by call reason or other target
specifications. This innovation has permitted
call monitoring projects with targets as
focused as callers requiring a second
activation call or customers from a specified
credit segment calling on holiday weekends.
Combining these three components to
produce a complete strategy for addressing
customer dissatisfaction yielded significant,
tangible value for the carrier. Findings
generated by the call monitoring team
allowed the carrier to identify critical root
cause themes. Among these themes were
problems in the customer experience for
credit-challenged customers that were
resulting in dissatisfaction and higher cash
cost per user (CCPU) for this segment.
Recommendations based on these findings
led to the development of business cases that
identified $11 million in annualized cost
savings. A separate call monitoring project
confirmed the hypothesis that a decisive
driver behind extraneous calls related to
balance inquiries was misleading or
inconsistent posting times being reported by
different payment channels.
Sometimes the targeted call monitoring can
reject an existing hypothesis. One example
looked at the call reasons for the most
frequent callers. It was assumed that there
were specific recurring issues that drove
these subscribers to call with such regularity,
and that therefore they would have a
substantially different call reason profile than
the rest of the base. In fact, the opposite was
true. While secondary analysis revealed that
less than 4% of callers were responsible for
more than 20% of calls, call-monitoring
efforts revealed that these customers were
calling for quite similar reasons as everyone
else. The findings indicated that many of
these frequent callers called out of personal
habit rather than for a particular reason. The
concomitant recommendation suggested that
the carrier single out, through a review flag on
the subscribers' accounts, those frequent
callers with legitimate issues (a small portion
“Recommendations based on the
root cause analysis identified
$11 million in annualized cost
savings.”
9
11% of callers who could have used automated options chose to place a live call
instead because they have learned not to trust the systems.
Account
Balance
Account
Information
Minute
Balance
Lost/Stolen
Phone
Password
Reasons for Account Management Calls from Frequent Callers
50%
40%
30%
20%
10%
0%
Root Causes Related to Automated Options
Trust/system
update issues
Prefer live rep
Tried but failed
Unaware of
self-serve option
0% 5% 10% 15%
76% of account management calls from frequent callers could have been handled via
automated systems.
call just to chat or simply to inundate the call
center) and resolve the problem, thus
improving the customer experience and
averting continued frequent calling in the
future.
Yet another project demonstrated the
importance of hypothesis validation and
quantification. As noted earlier, a flaw in
some forms of primary analysis is that
anecdotal evidence may bring to the forefront
problems that seem glaring but are actually
not as crucial as other root causes. Without
validation and quantification, the “problem of
the moment” can often lead to a knee-jerk
reaction that fails to consider the full
cost/benefit picture. One example of such a
problem at the company was balance and
billing information provided across automated
systems (e.g. web, IVR). Within the company,
there existed a perception that update lags or
unavailable balance/billing information were
prompting a high volume of calls.
To understand the extent of this problem, the
call monitoring team first measured the
amount of calls attributable to account
management, and the percent of those calls
that could have been avoided. The first graph
shows that for frequent callers calling about
account management, 76% of the calls
concern “Account Balance,” “Account
Information,” or “Minute Balance,” all of
which represent types of calls that could have
been avoided had the caller used automated
systems. The second graph quantifies the
root causes behind why the callers chose to
place a live call rather than use the
automated systems. The findings show that
callers are sometimes not aware that such
systems exist or have learned not to trust
these systems as they sometimes provide
delayed information.
Once the call monitoring analysis had given
an estimate of the call volume that was
attributable to problems with the
synchronization of account balances across
systems, a business case was constructed to
weigh the savings generated from fixing the
problems versus the costs of these
improvements. Since IT improvements often
involve large amounts of fixed and variable
cost, this case serves to illustrate the
importance of targeted call monitoring
determining where to focus human and
capital resources. Using inputs from the call
monitoring effort it was determined specific
fixes would generate cost savings in the
range of $8M-$10M, others would likely
produce less than $25,000 due to the low
frequency with which they occurred, thereby
not justifying a costly IT fix. Without targeted
call monitoring to validate and quantify
specific issues, problems were often
identified through anecdotal feedback,
leading to expenditures that outweighed the
resulting cost savings. The example illustrates
how root cause analysis strategy not only
offers a clean, exact way of locating and
validating call volume drivers but also
presents a method through which to quantify
the extent of the calling costs.
IV10
While the carrier's root cause analysis solution has been a quantified success
and is easy to understand in theory, execution posed various challenges.
From the program management perspective, establishing a standardized
process that filters, prioritizes, and schedules call-monitoring projects and
dictates the consistent methodology to be followed each time is critical. It is
necessary to employ a system for ensuring compliance among the advocates
that are responsible for tracking call reasons.
Technical challenges include development of a call-matching algorithm,
acquisition of sufficient storage space for the library of recorded calls
(approximately 1TB), and dependency on existing infrastructure capabilities
at a company or on the willingness of management to pursue requisite IT
expenditures. Justification of these expenditures hinges on clear
demonstrations of ROI.
Finally, there are numerous "people" challenges. One of the problems the
carrier encountered was the initial reluctance of executives to adopt a
strategic approach to customer satisfaction. Faced with limited resources,
extreme pressure to deliver results, and short timeframes, some executives
balked at what they viewed as an overly cautious methodology of validating
hypotheses. Others questioned the ability of primary analysis such as code-
based intervention to yield concrete results and significant returns on
investment. Overcoming opposition required building a "proof-of-concept"
prototype. The simplicity of the technical design allowed the team to create a
small-scale call library, storing a month's worth of calls using existing server
space. This low-cost, small-scale solution enabled several targeted call
monitoring projects before the full build-out was approved. The results of
these projects allayed executives' concerns and justified full-scale production
of the call library.
Choosing the right personnel for call monitoring team and getting them to
deliver consistently actionable analyses presented a different type of
challenge. The five members of the call monitoring team brought invaluable
internal know-how; they had excellent operational experience from prior
positions at the carrier that could be leveraged to gain an understanding of
specific problems with internal processes, systems, and service channels. On
the other hand, because their background was predominantly operational, it
was not second nature for them to perceive customer satisfaction issues
strategically and drive action-oriented tactics to resolve the problems.
Targeted training and frequent after-project reviews helped the team
increase its value.
Implementation Challenges BenefitsCode-based intervention to improve customer
satisfaction has produced substantial
benefits. Quantitatively, root cause analysis
enables the operator to optimize spending and
maximize the returns on investment. The call
library was used to make specific, actionable
recommendations for improvement in
processes and prioritizing and quantifying the
benefits of various technology projects.
Code-based intervention is another powerful
weapon in a company’s analytical arsenal. By
improving the precision of primary analysis
through code-based intervention and
combining it with existing secondary research,
the carrier has identified more than $60
million in variable cost savings in the first six
months after implementation for a cost of less
than $1 million to implement and operate.
Qualitatively, the solution has added precision
to primary research. The solution's
methodology and tools allow for ongoing
identification, verification, and measurement
of actionable drivers of dissatisfaction. The
methodology can be applied across the
organization (such as to understand network
problems and churn reasons).
V
11
Can Your Company Find the Root Causes
of Customer Dissatisfaction?
Any company facing mounting customer
service costs and customer dissatisfaction
challenges could stand to benefit from such a
strategy. Within financial services, for
example, code-based intervention could be
employed to understand precisely why
customers are switching to other banks, or
why customers feel that product offerings are
not tailored to their investment needs.
Cable companies could seek to understand
why subscribers are unwilling to purchase
premium content or why broadband users are
migrating to DSL.
Companies such as software distributors or
appliance manufacturers that field technical
support questions could use this combination
of "radar view" and "deep dives" to draw
attention to the most frequent call drivers and
determine the underlying causes behind
recurring problems.
In fact, any organization with extensive
customer service operations, including
insurance companies, utilities, and some
governmental agencies, could potentially
decrease costs and increase customer
satisfaction by applying the principles of this
carrier's successful approach to customer
satisfaction root cause analysis.
By creating uniformity in the way that primary
customer information is categorized and
stored, code-based intervention can
revolutionize customer service in the same
way that Jethro Tull's seed drill changed the
face of farming. Harvesting primary
information becomes a secondary concern,
allowing companies to focus on what really
matters: identifying and addressing the root
causes of customer dissatisfaction.
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