Just released: ListenLoop reports allow you to add labels to categorize and filter customer feedback responses.
In the last 6 months, we have received plenty of user feedback (yes, through ListenLoop!) asking us to build a labeling function for responses and users. We’re happy to announce that this feature is now live in the ListenLoop dashboard.
This is yet another step toward perfecting our feedback management system for B2B and e-commerce businesses.
Here you’ll find a few use cases and how-to tutorial for the new labels functionality.
– The ListenLoop Team
The Job: you deploy a question inside of your web or mobile application, and you’re getting hundreds of responses per week. Usually a product manager or marketer on your team would be tasked with “categorizing” the feedback into various buckets to make the data more presentable to stakeholders. You could export the information, open the CSV in Excel, and label the responses there. But that won’t work if you need to do this every month, and you really need the categories to persist over time.
Solution: For each response, you can now create and tag a response (or visitor) with a custom label. The label persists over time, and the labels appear in your CSV exports so you can slice and dice the information further.
Here is a brief video overview:
In addition to categorizing user feedback, you can also filter responses according to applied labels. This is useful when you want to show a pattern of responses to a teammate or stakeholder.At ListenLoop, we often use patterns of customer anecdotes to end any “great debates” that result from many smart people providing plausible explanations for the same behavioral data. I’m sure you’ve seen this at your company, too. You know there’s a problem with a user flow, but now you need to know WHY there’s a problem. Labels will help you make a compelling argument.
Beyond winning arguments, labels also help you segment data, so you can perform advanced calculations. Suppose you review and label 276 open-ended responses with the following tags, “positive”, “negative”, “printing reports”, “late payments”, and “mobile issues”. Assume further that each open-ended responses is associated with a Net Promoter Score response. With ListenLoop, you then create a report that segments your NPS scores according to the labels you applied, like this:
This is a great way identify customer satisfaction according to the issues identified in your open-ended responses!
We look forward to your questions, comments, and use cases about ListenLoop’s labels.
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Net Promoter, Net Promoter Score, and NPS are trademarks of Satmetrix Systems, Inc., Bain & Company, Inc., and Fred Reichheld. Usage herein does not imply affiliation or support of this content.