Slate AI: Analyzing Query Results for Actionable Insights
- 22 Aug 2025
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Slate AI: Analyzing Query Results for Actionable Insights
- Updated 22 Aug 2025
- 2 minute read
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This article is part of our Slate AI series, each focused on a single, high-impact prompt, why it works, what Slate AI might say, the power of a follow-up, and a template you can try yourself.
The prompt
“Summarize this query output and identify any significant trends, red flags, or actionable next steps.”
This prompt transforms Slate AI into an analytical partner, helping you move beyond raw exports and straight into insight generation.
Why it works
Data-Aware: Slate AI quickly interprets the structure and content of your query results, detecting meaningful patterns across populations, tags, timestamps, and other key data elements.
Strategic: It goes beyond summarizing, offering next steps that align with enrollment, engagement, or retention strategies.
Customizable: You can feed it queries related to applications, events, campaigns, workflows, and beyond.
What Slate AI might say
You might see a response like:
🪪 Admissions Example
“Out of 3,200 inquiries, 1,240 have submitted an application—indicating a 38.7% conversion rate. However, only 19% of applicants who attended a campus tour event have applied. International inquiries also show lower conversion performance. Consider segmenting future outreach by event attendance and region to tailor your messaging.”
🎓 Student Success Example
“Students who attended an advising session in their first month were 40% more likely to register for the upcoming term. Those flagged for academic concern were least likely to attend a session. Consider prioritizing outreach to students with flags who haven’t scheduled advising yet.”
The Power of a Follow-Up
After your first response, try layering in more direction:
“Which segments demonstrate the strongest or weakest application activity?”
“How should I prioritize outreach based on this data?”
“How do retention markers vary across advising flags?”
“Which cohorts require proactive outreach based on these patterns?”
This keeps AI grounded in your data, but fluent in strategy.
Try reframing it
What happens when you shift the lens?
“...based on form start but no submission” | Focuses on behavior gaps |
“...during the last 30 days” | Narrows timeframe for insight |
“...excluding test records” | Cleans the dataset for meaningful output |
|“...excluding students with transfer status” | Cleans dataset to isolate first‑year trends |
Each of these reframes is still the same question at its core, but how you phrase it changes the kind of insight you’ll get back.
Prompt template
Here’s a version you can reuse:
“Analyze this query output and tell me what stands out, along with recommendations.”
A few swaps to get you thinking:
Applications by source | lead quality patterns |
Incomplete reviews | process bottlenecks |
Yield populations | follow-up opportunities |
Enrolled students by flag | retention / engagement risk |
Your turn
Try one of these yourself:
“What percentage of inquiries from the last 60 days have submitted an application?”
“Which sources in this query are converting above average?”
“How does yield compare across population tags in this dataset?”
“What stands out about this segment of students who started but didn’t submit?”
“What’s unusual in this query that might warrant a closer look?”
“What stands out about students on academic warning with no advisor interaction?”
“Which sources bring in the most successful first‑year completers?”
Let Slate AI do what it does best: analyze patterns, ask the next question, and give you a head start on acting early. From export to insight to action, in minutes, not meetings.
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