One of the challenges our organization faces has been determining how long in the case process it takes for a customer to start becoming frustrated. With StaircaseAI’s ability to detect negative sentiment combined with it’s integration with Salesforce Service Cloud cases, I’d love to see the ability to do reporting based on the case opened date to the date of the detected frustration.
We currently collect this kind of data with CSAT surveys at case closure, but that doesn’t give us insight into the moment when sentiment starts to plummet. We’d love to provide our support and engineering organization with data that shows customer expectations in order to set performance goals around those targets.
While I’m specifically talking about cases, I think it would be equally useful for other date based analysis. I could see it being used for future opportunity tracking integration, escalation management processes, or even basic analysis of customer age and/or lifecycle stages.
Sentiment Reporting Based on Timing/Stages/Lifecycle
+1
+7This is a really great suggestion. Having this kind of visibility to figure out roughly how many days/hours we have before a customer threatens to (insert negative action) would really help my team (specifically Support) improve service and impact NRR.
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This is a compelling use case. Being able to see sentiment mapped against lifecycle stages would reveal systemic patterns -- like "sentiment typically drops 60 days post-onboarding" -- that individual account views miss.
Today Staircase captures sentiment per-interaction, but the aggregate lifecycle reporting layer isn't available yet. I want to deliver more Cross-Account insights and have that as a top priority for our near term development. I have added these use cases to the possibilities.
Are there any other Cross-Account insights that you would want the AI intentionally scanning for and identifying across your customers?
+9
Do you have customers asking about a particular feature or features that aren’t being delivered on (or delivered on quickly, or really seeing any meaningful engagement about) and then might start shopping elsewhere? This could be useful in both identifying churn risk and product risk.
Churn risk would be at a customer level, similar to “time to frustration” where maybe it’s not a particular feature, but rather the lack of meaningful engagement/progress on some number of features that have them looking elsewhere (or maybe even poor delivery of the feature when it comes, that shows they didn’t engage with feedback).
The aggregate then would be looking at those features/behavior patterns and flagging issues with features and/or feedback loops.
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@bradley interesting angle. Feature-specific sentiment is something we've been thinking about, especially as we build out our Multi-Product intelligence with Relationships. The idea of knowing "customers who mention Feature X tend to have declining sentiment" would be powerful for both PM prioritization and proactive CS. I've had this come up at least 10 times this year in conversations with customers... it is something I want to tune our Staircase "Topics" feature to become an expert at.
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