Catch brand issues early with hourly Sprinklr listening alerts

Once an hour, watch my Sprinklr brand listening topic for emerging issues and alert my comms team before they turn into a crisis. Use a cron trigger running hourly. This is a scheduled check rather than an event subscription, because Sprinklr does not push webhooks for listening mentions, so every run pulls the current picture itself.

Start with the Sprinklr Listening Query operation against my brand listening topic. Pull two things from it: the stream of recent mentions covering the last hour, and the aggregated widget giving total mention volume plus the sentiment breakdown for that same window.

Then use the Sprinklr Run Reporting Query operation against the listening report engine to fetch the same metrics, mention volume and sentiment breakdown, across the previous seven days. Use that as the baseline: work out the typical hourly mention volume and the typical share of negative sentiment over that period, so the comparison is against my brand's own normal rather than an arbitrary number.

Compare the current hour against that baseline. Treat it as a spike when mention volume is meaningfully above the normal hourly rate, or when the negative share of sentiment jumps well above its seven-day norm. Both matter independently. A modest rise in volume that is overwhelmingly negative deserves attention just as much as a large but neutral rise.

If neither condition is met, do nothing at all. Post no Slack message, create no Jira issue, and end the run quietly. The value of this alert channel comes from it being silent most of the time, so never post a reassuring all-clear.

When there is a spike, read the mentions you pulled and cluster them into the two or three distinct narratives driving it. For each narrative: give it a short plain-language name, estimate roughly how many mentions belong to it, quote two or three representative example posts with links where available, and judge whether it looks organic or coordinated. Base that judgement on signals like near-identical phrasing across accounts, a very tight burst of posting times, or a cluster of new or low-follower accounts, and state which signal drove the call so a human can check your reasoning.

Post the alert to my comms team Slack channel using the Slack Send a Message operation. Lead with the headline numbers: how far mention volume and negative sentiment have moved against the seven-day baseline. Then list each narrative with its name, rough volume, example posts, and the organic or coordinated read. Keep it skimmable, because people will read it on a phone.

When a spike clears the higher severity bar I set, also create a Jira issue for the comms team using the Jira Create Issue operation. Put the narrative summary in the description, including the same volume and sentiment numbers and the representative posts, and reference the Slack alert so the two connect. This is what gets the response tracked to closure. Below that severity bar, the Slack post on its own is enough.

Keep the number of API calls per run small. Sprinklr allows roughly 1,000 requests an hour and 10 a second, and an hourly schedule sits comfortably inside that as long as each run makes a handful of calls. Pull mentions in a single query with a sensible page size instead of fanning out into hundreds of per-mention lookups. If a listening or reporting call comes back empty because that capability is not enabled on my tenant, note it once in the run output rather than retrying repeatedly.

Ask me for anything you need that is not already configured: my brand listening topic, the Slack channel for alerts, the Jira project and issue type, and the two thresholds, meaning the spike level that warrants a Slack alert and the higher severity level that also warrants a Jira issue.