Use a Bubble-Heat Map that outlines where managed businesses in the platform are located, and the collection of mentions and insights about said business locations.

Use filters to break down a certain area on the map by a regional, local, or institution view. Dynamic zoom-in to look at metrics like mention volume, experience/sentiment score, and star rating.

Know which locations are driving the most mentions or have the best/worst experience score. You can then make decisions on how to handle/improve certain locations.

Use smart word clouds to perform deep analysis of customer feedback by positive and negative sentiment.

Breakdown sentiment by what customers are talking about, and how they’re talking about it. This is a more granular way of viewing conversation streams by “Subjects” and “Attributes”.

With just a few clicks, understand, for example, that sentiment on service is driven by negative comments on the long lines, while friendliness is a strong point.

Use tables to analyze performance in different areas of the business.

Create tables for “Attributes” and “Subjects” with metrics like experience score, star rating, and mentions.

Insight into which subjects or attributes locations excel or lag in. This allows you to make decisions on how to improve your locations.

View the conversation stream of reviews coming from specific locations.

For messages which have had Intelligence run, individual insights found are highlighted in the verbatim text, shown in the same 5-point color scale reflecting the sentiment.

See the actual verbatims, star rating, and source of a message in your data set.

Log responses for Location Insights message

When you make a native response to a review on a channel, you can enter that response in Sprinklr where your team is able to see that the message has received a reply from a member of your company.

You are able to gather and analyze data on how many messages have been responded to, making sure no customer inquiry has been left unanswered.