Command
Run /peec_campaign_tracker using the Peec MCP with [campaign start date] and [campaign end date] as parameters. This will pull our brand report for both periods and return: overall sentiment score for each period, the top keywords AI uses to describe our brand, visibility broken down by AI model, and share of voice vs. top 3 competitors. Then write a side-by-side comparison showing exactly what changed: whether AI language shifted toward our campaign messaging, which models responded most, and whether any competitor gained or lost ground during the same period. Export the side-by-side comparison to a Google Sheet. Post the headline numbers and key shifts to [Slack channel] as a campaign performance update.
Brand tracking surveys take weeks to field and cost significant budget. The signal they return is a blended average of human perception across a sample audience, which is useful but slow.
This gives you the AI search equivalent in minutes: before and after sentiment, the specific language shift model by model, which AI platforms responded to the campaign and which did not, and how competitors moved during the same window. It does not replace brand tracking. It complements it with a faster, more granular signal that is directly tied to what buyers hear when they use AI to research a purchase.
