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The Art of Conversation and the Science of Service

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.

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