Prompt Playbook: Insight Agent
A structured guide to strategic, operational, and performance analysis using the Insight Agent.
This playbook is a curated set of tested query templates that enable deeper strategic analysis with the Insight Agent. Each template is derived from the top successful queries used by our enterprise customers in live environments.
How to Use This Playbook
- Start with the category that best matches your business goal
- Select the relevant use case within that category
- Use the Query Format as a template.
- Refer to the Example Query to see how it looks with real values
- Replace the placeholders (dates, channels, campaigns, segments) with your own data
Note
Our Insight Agent can:
- Displays results as on-screen tables and narrative insights.
- Does not generate downloadable files unless explicitly mentioned
1. Strategy & Scenario Planning
Category Focus: Validating plans, schedules, and strategic decisions
Use this category when you want to evaluate whether a strategy, timing plan, or campaign decision is working as expected.
Use Case 1.1: Strategy Validation
The "Is This Working?" Test
When To Use This:
When you launch a new content calendar or fixed communication schedule and want to understand how users are engaging across time slots. **_)
IS THIS PLAN WORKING? Starting [Date], we used a fixed [Channel] schedule:
● 7:00 AM: [Content Type A]
● 12:00 PM: [Content Type B]
● 8:00 PM: [Content Type C]
Analyze the performance of this specific schedule since launch.
Are users engaging with the morning or evening slots more?
IS THIS PLAN WORKING? Starting January 15, 2026, we used a fixed Email schedule:
● 7:00 AM: Product Education Tip + Quick Win
● 12:00 PM: Personalized Offer / Deal Drop
● 8:00 PM: Social Proof (Reviews/UGC) + CTA
Analyze the performance of this specific schedule since launch.
Are users engaging with the morning or evening slots more?
Use Case 1.2: Anomaly Investigation
When To Use This:
When you see a sudden spike or unexpected change in performance and want to understand whether it was driven by strategy or chance.
Revenue hit a massive peak of [Number] on [Date] (from a 0 baseline).
This correlates with the [Campaign Name] send.
Was this luck, or did a specific segment drive this?
Analyze the correlation.
Revenue hit a massive peak of INR 12,50,000 on December 28, 2025
(from a 0 baseline). This correlates with the
New Year Mega Sale – VIP Early Access send.
Was this luck, or did a specific segment drive this?
Analyze the correlation.
2. Retail & E-Commerce
Category Focus: Funnel leaks, revenue attribution, and forecasting
Use this category to understand where revenue is lost, what drives performance, and what to expect from future campaigns.
Use Case 2.1: Finding Revenue Leaks
When To Use This:
When campaigns drive clicks but fail to convert.
For each channel (Email, SMS, WhatsApp), identify the single campaign
with the highest Click-Through Rate (CTR) but the lowest Conversion Rate
in [Insert Month].
What are the possible reasons for this drop-off?
For each channel (Email, SMS, WhatsApp), identify the single campaign
with the highest Click-Through Rate (CTR) but the lowest Conversion Rate
in December 2025.
What are the possible reasons for this drop-off?
Use Case 2.2: QBR Comparison Table Generator
When To Use This:
When preparing monthly or quarterly business reviews.
Query Format
Create a comparison table for [Month A] vs [Month B] across all channels.
The table should showcase channel-wise engagement metrics,
followed by a text summary of what worked and what didn't.
Finally, provide 3 strategic recommendations for [Next Month].
Create a comparison table for November 2025 vs December 2025
across all channels.
The table should showcase channel-wise engagement metrics,
followed by a text summary of what worked and what didn't.
Finally, provide 3 strategic recommendations for January 2026.
Use Case 2.3: Predictive Performance Analysis
When To Use This:
Before launching a campaign or allocating budget.
Query Format
Based on past data, predict the potential performance of a campaign
with similar attributes to our best-performing [Sale Name] campaigns.
What is the expected Open Rate range?
Based on past data, predict the potential performance of a campaign
with similar attributes to our best-performing Diwali Dhamaka campaigns.
What is the expected Open Rate range?
3. Banking, Finance & Insurance (BFSI)
Category Focus: Risk detection and goal validation
Use Case 3.1: Risk & Anomaly Detection
When To Use This:
To identify broken pipelines or abnormal campaign behavior.
Query Format
Analyze campaign data from [Date Range] to identify anomalies,
outliers, and unusual patterns in performance metrics.
Show specifically which campaigns are performing significantly
differently (+/- 20%) from the expected average.
Analyze campaign data from January 6–12, 2026 to identify anomalies,
outliers, and unusual patterns in performance metrics.
Show specifically which campaigns are performing significantly
differently (+/- 20%) from the expected average.
Use Case 3.2: Goal Achievement Analysis
When To Use This:
To validate whether campaigns are meeting defined KPIs.
What percentage of our campaigns achieved their set goals
(e.g., CTR > 2%, Open Rate > 15%) last month?
Highlight the top 3 common factors among the successful campaigns.
What percentage of our campaigns achieved their set goals
(e.g., CTR > 2%, Open Rate > 15%) last month?
Highlight the top 3 common factors among the successful campaigns.
4. Travel & Hospitality
Category Focus: Creative performance and revenue contribution
Use Case 4.1: Creative Theme Analysis
When To Use This:
To optimize subject lines and creative messaging.
Identify the top 10 performing subject lines across all email campaigns
this quarter.
What common patterns, themes, or keywords do they share?
Group them by emotional trigger (e.g., Urgency, Discount, Curiosity)
and tell me which theme drove the highest Opens.
Identify the top 10 performing subject lines across all email campaigns
this quarter.
What common patterns, themes, or keywords do they share?
Group them by emotional trigger (e.g., Urgency, Discount, Curiosity)
and tell me which theme drove the highest Opens.
Use Case 4.2: Revenue Attribution
When To Use This:
To understand which channels or journeys drive revenue.
Provide the overall revenue for [Insert Month] and break it up by channel
(i.e., Email, APN, Journeys).
Which specific journey ID contributed the most to the total revenue?
Provide the overall revenue for December 2025 and break it up by channel
(i.e., Email, APN, Journeys).
Which specific journey ID contributed the most to the total revenue?
5. Operations & Reporting
Category Focus: Structured data and comparisons
Use Case 5.1: Perfect Data View
When To Use This:
When you need clean, structured data for further analysis.
Generate a detailed data table for [Channel Name] from [Start Date]
to [End Date].
The table must include these specific columns:
Campaign ID, Campaign Name, Sent Count, Delivered Count,
Unique Clicks, and CTR.
Do not summarize; just show the table.
Generate a detailed data table for WhatsApp from December 1, 2025
to December 31, 2025.
The table must include these specific columns:
Campaign ID, Campaign Name, Sent Count, Delivered Count,
Unique Clicks, and CTR.
Do not summarize; just show the table.
Use Case 5.2: Multi-Campaign Analysis
When To Use This:
To compare performance across selected campaigns.
Analyze specifically these campaigns:
[Campaign A], [Campaign B], and [Campaign C].
Compare their Click Rates side-by-side and tell me which one won.
Analyze specifically these campaigns:
Winter Clearance – Email Drop 1,
Abandoned Cart – WhatsApp Nudge 2,
and Price Drop Alerts – SMS Batch 3.
Compare their Click Rates side-by-side and tell me which one won.
6. Audience & Segment Health
Category Focus: Preventing fatigue and protecting engagement
Use Case 6.1: Over-Mailing Check
When To Use This:
To assess whether high send volume impacts unsubscribes.
Analyze the correlation between our Daily Campaign Volume
(number of campaigns sent) and our Unsubscribe Rate over the last 30 days.
On days where we sent more than 2 campaigns,
did the Unsubscribe Rate increase significantly?
Analyze the correlation between our Daily Campaign Volume
(number of campaigns sent) and our Unsubscribe Rate over the last 30 days.
On days where we sent more than 2 campaigns,
did the Unsubscribe Rate increase significantly?
Use Case 6.2: Segment Fatigue Analysis
When To Use This:
To monitor engagement health of your most active or valuable segments.
Analyze the Unsubscribe Rate for our [Most Active Segment Name]
over the last 3 months.
Is there an increasing trend?
If yes, does it correlate with an increase in our sending frequency?
Analyze the Unsubscribe Rate for our VIP Repeat Buyers (last 90 days)
over the last 3 months.
Is there an increasing trend?
If yes, does it correlate with an increase in our sending frequency?
Updated about 1 hour ago