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Six Applications That Benefit From Embedded Text Analytics

[fa icon="calendar"] Feb 2, 2017 10:23:15 AM / by Matti Airas

Open-ended customer feedback analysis is not a stand-alone application because it needs to be fed with text. And that text comes from outside, from another application or platform. Feedback analysis is a feature that enhances the functionality of an application.

In the ideal situation feedback analysis is an embedded analytics plug-in that is connected to an application via an API.

Advanced text analysis is not a mandatory functionality but it does make applications and platforms smarter and reduces the amount of monotonous and repetitive work done by humans.

Let’s look at six applications that benefit from using advanced feedback analysis:

 

1. SEMANTIC Customer Experience Management or Voice of the Customer Solution 

Today the majority of customer dialogue is in open text. Feedback analysis is a perfect addition to a CEM or VOC process to sort out what customers are talking about and to determine the sentiment of those discussions. In CEM and VOC another challenge is multi-language feedback. How to hire the staff to understand all languages? Multi-language feedback analysis takes care of that.


2. CONTEXTUAL Net Promoter Process

Net Promoter Score is a good indicator of customer loyalty, but understanding in real-time why customers give a specific score substantially increases the value of Net Promoter process.


3. CONTINUOUS Employee Engagement Pulse

Companies are moving from once a year type of structured surveys into quarterly, monthly, or even real-time employee engagement monitoring. If you send queries out often, the only way to get the response rate up, and cover the whole gamut of human working issues, is to ask for open-ended questions. Feedback analysis is required to analyze these, otherwise the processing cost is too high and the delay in extracting insights is too long.


4. INTELLIGENT Contact Center

A good and increasing part of customer claims and complaints come in via web forms and emails. It doesn’t make sense for humans to categorize these because we are not very adept at the discrete categorization of free-form text. Feedback analysis can automatically and in real-time categorize all incoming emails and web forms and instruct the Contact Center software to choose an appropriate response template or even automate simple claim-response cases.


5. SEMANTIC Data Warehouse

Data Warehouses store large volumes of unstructured information. Most of this information is open text. Feedback analysis can be connected to a data warehouse to automatically enrich or structure open text.


6. SEMANTIC Social Media

Just knowing how many times a brand is mentioned and calculating sentiment from good and bad word (-lists) is not sufficient to turn social media comments into actionable information. A feedback categorizer and NLP-based sentiment analysis, combined with semantic filtering (LINK), will turn social media data into truly actionable information.

 

Embedded text analytics runs automatically and invisibly in the background making your platform smarter

When connected to these systems, feedback analysis is often invisible to the user. Feedback analysis typically runs automatically in the background via an API connection, categorizing and sorting open text in a manner suitable for each process. The nice thing is that if your company has more than one of these applications, a single feedback analysis service can handle them all.

 


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Interested in making your application or platform smarter?

Etuma has embedded analytics connectors to many platforms. Here are just few examples. 

 

 

 

Topics: embedded analytics, customer support

Matti Airas

Written by Matti Airas

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