The frequent
launch of a variety of products across emerging customer segments has led to
the proliferation of a number of devices and equipment. With increasing customer
expectations and the ease in which complaints can be lodged and enquiries sought
online, there is a surge in synchronous and asynchronous conversation.
Consequently, we see product manufacturers and OEMs competing to provide higher
quality of customer service. The first step in addressing customer needs is to
converse, engage and address. This is imperative to retain or gain market
share.
This competition
in “service lifecycle management” has resulted in the rapid rise in data mining
activities on customer complaints, inquiries and the like, as recorded in
contact center logs. These activities are aimed at identifying the root causes
of specific issues .The proactive resolution through alerts is often through
the deployment and usage of smart algorithms.
Recently our
team interacted with OEMs in the telecom, manufacturing and healthcare industry
segments. We discovered the root cause analysis and the subsequent proactive
resolution of customer issues, to be the significant element in their overall
customer acquisition and retention strategy. Not surprisingly, the spurt of
such activities has been catalyzed by the evolution of marketing strategies,
leveraging customers’ sentiments about different product or service offerings,
expressed through social networks. The arcane world of Natural Language
Processing has now come to the forefront as an application to aid in addressing
these novel business challenges.
Above all,
research has added a host of new algorithms in this area, such as, dialog act
tagging, topic modeling, sentence boundary detection, text tiling and so on,
enabling to bring in solutions to hitherto unsolvable problems in this field.
However, when
I delved a bit deeper into the problem, I found that there are distinctly three
kinds of issues which come from the data sources in this field:
(i)
mining
event logs from devices
(ii)
analyzing
tweets or comments from social networks
(iii)
identifying
root cause of problem from call notes.
Initially we
thought they are very similar as they have a few common characteristics: they
are unstructured text, are event-driven and have an underlying taxonomy of
products, events or sentiments.
Interestingly,
however, they are significantly different in some key aspects, necessitating
the need for applying different solution strategies. While device logs are
easier to deal with due to the standardized response pre-configured in systems,
such as, SMART logs, sentiment analysis is more complex due to the wide
diversity of the ways in which people react to similar scenarios and express
their sentiments.
The human
element in social network feeds introduces a lot more noise, such as, acronyms
and polysemy. We still have scope to model discussion topics based on a apriori
defined taxonomy or a taxonomy built on-the fly based on known language
constructs, such as, synonyms, phrases, adjectives, and so on. So the problem
of extracting actionable insights from device logs, can be considered, as a
subset of the problem of developing targeted marketing strategies based on the
analysis tweets or other forms for social network feeds.
Identifying
root cause of problems from contact center or technical support logs, sometimes
known as conversation mining or interaction mining, is an even more complex problem,
due to several reasons,for example:
(i)
difference
in terminology of the expert call center agent and the novice customer,
(ii)
iterative
and asynchronous nature of messages,
(iii)
huge
boundary of the problem scope, and so on. Several approaches have been
developed in recent times, but most of them seem to elude the real solution to
this problem.
Our attempts
to solve the problem by integrating topic modeling, hyperclique pattern
discovery and sequence mining to detect anomalies and thereby identifying root
causes underlying customer complaints is in well underway. The stated
evaluation metrics as reference points for conversation mining will be key to
defining the success.
Somjit Amrit is the Chief Business Officer of Technosoft Corporation
He can be reached at somjit.amrit@technosoftcorp.com
