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Three Case Studies on Automatic Contact Center Ticket Classification

[fa icon="calendar"] Feb 9, 2017 4:25:13 PM / by Matti Airas

Here are a three real-life examples where real-time text analysis service has been successful in identifying systemic issues in our customers’ products and services.


CASE 1: Identifying hotels that don’t fulfill security requirements

Our customer, an international charter travel company, gets tens of thousands of open-text comments from their customers every week in multiple languages. Because of the high volumes of comments they received, it was not possible for them to read through all feedback. Their solution was to take a sample from one of the half-dozen languages in which they received comments and analyze them manually. This was slow and expensive, and it made it difficult to find out what people were talking about. After taking our service into use, it took only a few seconds for the feedback bot to pinpoint the hotels (out of over a thousand) that had inadequate fire-protection procedures (sprinklers, fire doors and extinguishers).

CASE 2: An online gaming company’s challenge with logins

An online gaming company’s contact center received tens of thousands of complaints annually (some are ideas and thanks but…), but their systems were only able to highlight very high-level trends. Their customer intelligence team had only a vague understanding that something was wrong with the login process in some of their apps. They came to us looking for a way to dig deeper into the feedback to find product- and service-specific issues and ideas. After analyzing their contact center’s open-text complaints and chat logs, our feedback bot succeeded in identifying those customers who were dissatisfied with the login process. This enabled the gaming company to send a focused survey to those specific customers and to find out in detail what was wrong with the process and why they were not satisfied.

CASE 3: A regional department store chain with shortcomings in its product selection

A large regional department store chain was facing a challenge maintaining an optimal selection of products to meet its customers’ wants and needs. They had only anecdotal evidence of their clients’ dissatisfaction, and they were solely relying on the experience and insights of their purchasing managers to come up with new product ideas. After taking our service into use, our feedback bot quickly detected a frequently requested brand that was missing from their selection. By monitoring bot’s analysis results in real time, the purchasing managers were able to fulfill the selection quickly and proactively.

We wrote a white paper on the topic CX Professionals Guide to Implementing an Enterprise Insight Process. 

Please download and read it and we guarantee that you will learn something new.

Topics: cx benefits, contact center

Matti Airas

Written by Matti Airas

My passion is to figure out how to turn open-text feedback into well structured usable information.