1. Text Mining
Going Way Beyond Just Listening to The
Voice of The Customer
|
May 2010

2. Text Mining I Going Way Beyond Just Listening to The Voice of The
Customer
How about making use of the 80% of customer data you have on hand but
haven’t tapped into yet? And how about if that data can help you reduce churn
by 50%? Text mining is one of the latest trends in data mining today, with many
companies already benefiting significantly from their efforts around this practice.
What?
About 80% of customer data captured within enterprises is believed to be in
unstructured format, in the form of call center notes, customer / employee e-
mails and survey responses. Especially in industries (particularly
telecommunications and finance) where there is a high level of interaction with
customers through various channels (such as through a call center or a customer
service office), significant knowledge about the customer base resides in this
unstructured but captured data. Although business intelligence and customer
analytics have become an essential part of corporate life in most leading
companies today, very few look into opportunities residing in this 80% of
customer data, keeping their analytics focus on structured data only.
Text mining is the means to actually being able to analyze this unstructured
customer data so that valuable customer insight can be extracted from it. This
method of analytics identifies the keywords, phrases, and patterns hidden in
data kept in free text format, assisting companies make sense of each piece of
communication.
But, Why?
A recent study conducted at a major mobile telecommunications provider found
that there was a 40% increase in the performance of the telecom’s churn
prediction models after the inclusion of customer complaint details via text
mining. A financial services provider case demonstrated a 20% improvement in
credit-risk decision models after the inclusion of call center conversations via
text mining. Text mining is providing a means for companies to utilize extremely
valuable customer insight in their decision making processes, and clearly the
results justify the effort.
Focusing on the use of text mining in analyzing contact center records, there
exist various opportunities, such as:
1. Converting qualitative information into quantitative data, providing ability to
categorize reasons, risk, and opportunity levels of contacts, which enables
enrichment of almost all data mining models (e.g. churn, cross-sales, etc.)
2. Identifying repeating complaint and inquiry reasons automatically, which can
be used to resolve root causes and decrease contact center load, while
increasing customer satisfaction

3. 3. Identifying recently begun categories of complaints or inquiries, by
automatically categorizing them, which can be used as early indicators of
operational problems
4. Extracting competitor intelligence from customer interactions, by
automatically identifying calls related to competitor prices, campaigns, and
products from contact center records
The benefits are not only limited to for-profit companies. The Hong Kong
government, for example, text mines its call center records so that is can quickly
discover new complaints – “For instance, we can spot districts with frequent
complaints received concerning public health issues such as dead birds found in
residential areas. We can then inform relevant government departments and
property management companies, so that they can allocate adequate resources
to step up cleaning work to avoid spread of potential pandemics.”
So, How?
Six key steps exist towards making full utilization of text mining on unstructured
customer data:
1. Channel Analysis Identification: The first step is to analyze all the channels
that customers interact with your company through, to determine where the
text mining practice can be applied to. The most traditional channel for text
mining is call centers, by analyzing the input made by customer service
representatives into customers’ profiles in their CRM systems. This can apply to
customer service center / dealer / branch interactions as well, if and only if the
interaction is recorded by the customer service representative. A separate but
emerging channel of interaction that also needs to be text minded are electronic
channels, in the form of customer e-mails or web form entries. As long as the
interaction is recorded via a company employee or via the customer into some
form of accessible system, text mining can and should be conducted.
2. Lexicon Customization: Most tools for text mining have a built-in dictionary of
common words and their categorization (e.g. locations, numbers). Although
highly effective in identifying generic words and patterns within a document,
their effectiveness can be multiplied by introducing business and mission specific
words. For example, a text mining tool would not be able to categorize a
company’s product names or the names of its competitors by default. It would
simply treat them as yet another word. Or it would not necessarily search for
‘value added service’ as a phrase when analyzing a telecommunications
company’s contact center data. All such business-specific words and phrases
need to be defined and categorized for a more intelligent analysis of the text,
through preliminary analysis of the records and establishment of a business
dictionary.

4. 3. Text Mining: Once the custom text mining dictionary is defined for the
business problem, the next step is performing the actual analysis, using a text
mining tool, which is readily available in most statistical analysis and data mining
solutions. The outputs from the text mining activity, which is the list and
frequency of keywords and phrases, needs to be reviewed by business users in
order to identify those which have business meaning, as well as any
requirements for further fine-tuning the business dictionary. Like data mining
models, text mining models need to be run periodically to categorize new
records and continuously identify new patterns emerging from them.
4. Analytics Integration: Text mining, on its own, is a valuable tool for
understanding what customers are most interested in or concerned about.
However, its value add to other customer analytics models is also immerse, and
hence needs to be used as an input for most data mining models (such as in
churn prediction and cross-sales analyses). By categorizing details of complaints,
requests and inquiries, text mining provides the ability to flag customers for
critical interactions and needs, which then becomes yet another input parameter
for quantitative data mining techniques.
5. Reporting Integration: As text mining can categorize complaints and inquiries,
it provides an ability to track the changes in different reasons for customer
contacts, which needs to be integrated into the enterprise reporting platform.
Monthly reports on ‘new keywords/phrases’ or ‘keyword/phrase trends’ would
enrich standard contact center reports significantly, enabling identification of
increasing customer interest or concern in specific areas or early alerts on certain
services or offerings. These reports can also provide an understanding on the
repeated customer call reasons and focus on their root cause or moving their
resolution to alternative channels.
6. Operational Integration: Integrating text mining findings into operational
systems in a real-time manner can help companies increase the efficiency and
effectiveness of their customer interactions – such an effort can provide
guidance to the contact center agents on the right department to forward
requests to or warn them when the call details they enter present a high-
opportunity or high-risk customer interaction.

5. Similar to traditional data mining models, text mining requires ongoing
maintenance, as what the customers talk about changes over time, with
changing products, services and propositions. As a result, companies need not
see text mining as a one-time activity, but rather, need to assign ownership and
resources continuously.
What Next?
As in all types of customer analysis, text mining is as accurate as its inputs are.
Companies which start investing in text mining need to make sure that their
customer touch-points enter enough details about each interaction with clear
phrasing. ‘Cst complains on price’ is very different than entering ‘Customer
compared our international prices to ABC Telecoms for India and is complaining
about our off-peak prices. He mentioned that he would churn unless we offer a
good package soon’ and would not result in any in-depth insights even with the
best text mining techniques. This requires training and customizations in
performance management for such personnel. A more advanced alternative
solution to this problem is making use of speech recognition, to automatically
translate customer interactions into detailed scripts for analysis.
Text mining customer data is only the beginning for companies seeking to
strengthen their analytical practices and understanding of their customers – the
next steps can be an analysis of web chatter, press releases, blogs, etc., using a
similar approach, for identification of what the market is talking about and what
the key competitors are up to.

6. About Forte Consultancy Group
Forte Consultancy Group delivers fact-based solutions, balancing short and long term
impact as well as benefits for stakeholders. Forte Consultancy Group provides a variety
of service offerings for numerous sectors, approached in three general phases -
intelligence, design, and implementation.
For more information, please contact
info@forteconsultancy.com
Forte Consultancy Group | Istanbul Office
www.forteconsultancy.com