Automated service captures hidden wealth of unstructured customer feedback data (IT Toolbox Blogs)

Customer comments contain some of the richest, most actionable insights of any data available anywhere. The challenge? Effectively analyzing and accessing those insights when and where you need them. InMoment’s Explore resolves that challenge by providing CRM teams with new and practical views of unstructured data.

Social media and other communications forums are constantly generating valuable feedback on customer experiences and interaction with companies, much of which is highly valid to current and future customer interactions. But actually getting a handle on this data in a format that provides practical advantages to customer-facing teams can be a tall order. 

“Brands often struggle with getting real business value from customer comments,” explains JD Nyland, EVP for product at InMoment. 


Yet this is an area of extremely valuable customer intelligence for those companies that can harness it.

Explore is built on a powerful and accurate CX text analytics engine, purpose-built to surface insights from customer comments. This can provide companies with instant insights into what their customers are saying.

By using InMoment’s CX-tuned and real-time text analytics, Explore allows business users to mine customer comments from any source, including social, web and mobile surveys, voice and video to find answers to questions in real time. They can search and filter comments by topics of interest, including products, services, employees and even competitors. It is even possible to slice and dice analytics by time period, sentiment, topic and keyword.

Key Takeaways:

• With the increasing availability of DIY survey tools, it’s easy for brands to assume the habit of barraging customers with more questions and longer surveys – even if they shouldn’t. 

• Manual analysis of unstructured data can quickly consume insight teams’ precious resources. 

• Most automated text analytics models deliver only marginal accuracy rates, failing to capture large quantities of critical data.


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Source: SANS ISC SecNewsFeed @ March 6, 2017 at 11:15AM