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Posts Tagged Customer complaints

Building a competitive edge in a crowded app market

The App market is hyper-competitive – For example, there are 100+ apps on Google Play store that allow you to create custom photos by cutting the image from one image and pasting it to the other image.
Also, customers are highly engaged with these apps. 100k+ customers rated these competing apps, and their reviews are a gold mine for insights on what customers love, hate and request. Identifying and acting on these insights is crucial to building a competitive edge in such a crowded market.
For example, one of the app makers mined thousands of app reviews & identified a high priority feature that customers requested on a consistent basis – “Adding a zoom feature for more precise cut/paste operations”. When the app maker added the first feature to the app in late February, customers got what they wanted and the reviews requesting that feature no longer showed up as shown in the blue line below –
However, a new relevant feature request started showing up in the reviews in March – “Ability to hide/unhide the magnifying glass”, as shown in the purple line graph above. Customers said that the newly rolled-out zoom feature overwhelmed their photos. When the app maker acted on this request by May, those comments went away in subsequent months.
This is an example of how app makers can use the feedback from their customers to improve their apps, assess how well they are working and stay ahead of the competition. However, recognizing and prioritizing feature requests or bugs amongst thousands of reviews is not easy with a cursory reading of reviews. A more systematic and scalable review mining approach is required to capture the insights that are actionable.
Given the constant threat of churn & the crowded competition, it pays to pay close attention to what the customer tells you and to act on it.
If you want to learn more about how SetuServ deploys this optimized solution for its clients, please visit us at our website www.setuserv.com or click here for a demo on synthesizing reviews.

Posted in: App, App reviews, Review Synthesis, Survey Synthesis

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Unrealistic Expectations from Text Analytics Tools

Our last blog described how organizations can improve their Net Promoter ScoreTM by analyzing verbatim responses. In fact, today organizations have a wealth of information in various text data sources such as customer complaints, reviews and social media conversations. Most customer centric organizations deploy text analytics tools to glean actionable insights. But, still if you ask any analyst that “How often were you stuck with text analytics tools, and had to massage the data manually?” the answer would most often be, “Almost always.” And the underlying reason for this is the complicated and contextual nature of text analytics itself.


KeyFor example, an airline that used a leading text analytics tool to mine customer complaints had “upgrade to business class” as a key theme. However, this theme is not specific enough to drive any Action. It is unclear whether the customers complained about “upgrades that were cancelled” or “inability to use miles for upgrades” or were simply saying that the “upgrades are expensive”. Each of these specific themes would demand different action and it is very difficult for a software to extract insights at this level of specificity.


KeyAlso, accuracy is often a concern with text analytics tools as they can’t adequately interpret the context and nuance of human language. For example, as Andrew Wilson rightly noted in this article , most of today’s text analytics tools would classify the following statement as a negative comment about the Scion: “With the supercharger included on my Scion, it is one bad machine,” not being able to recognize the colloquial use of the word “bad” to actually mean “good”.


While specificity is needed to make the themes actionable, accuracy is needed to act with confidence (especially when the action is at a micro level). At the current rate of Natural Language Processing (NLP) technology advancement, it will be decades before technology evolves to the level of human interpretation. As a result, leading technology companies like Google and IBM have increasingly relied on humans to train, evaluate, edit or correct an algorithm’s work, as explained in this article in the New York times.


Organizations need to understand where NLP works best and where it doesn’t so that human review can be used to complement NLP. Because human review becomes expensive and time consuming as the scale increases, it is very important to optimize the combination of NLP and human review so that it is scalable & cost effective.


If you want to learn more about how SetuServ deploys this optimized solution for it’s clients, please visit us at our website www.setuserv.com or click here for a case study on synthesizing reviews.

Posted in: Social Media Synthesis, Survey Synthesis

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