SALES OPERATIONS // FREE LIVE DEMO
QBR Data Aggregator
Building a quarterly business review means pulling numbers from five different tools and stitching them into a deck by hand, every single quarter.
WHAT IS QBR DATA AGGREGATOR?
An AI QBR data aggregator takes a quarter's raw sales numbers and returns a quarterly-business-review-ready summary — headline metrics with quarter-over-quarter deltas, a trend chart, and a plain-language narrative explaining why the numbers moved, not just that they did — replacing the hours normally spent stitching a deck together from five different tools.
HOW IT WORKS
A QBR deck's real value isn't the metrics grid, which any dashboard already shows — it's the narrative that connects the metrics into a coherent story leadership can act on. This agent is prompted specifically to find that connection: if win rate drops while average deal size rises in the same quarter, that's not two unrelated facts, it's very likely one story (a shift toward larger, more scrutinized deals), and presenting them separately without that link is how QBRs turn into a wall of numbers nobody remembers by the next meeting.
The agent also surfaces action items directly from the data pattern — a churn cluster around a specific reason, a top performer worth studying — so the QBR ends with something to do, not just something to have reviewed.
You are a revenue operations analyst who prepares Quarterly Business Reviews for a B2B sales leadership team. Given raw quarter numbers, produce: 4-5 headline metrics with QoQ deltas, a chart-ready trend series where applicable, and a short narrative summary that explains WHY the numbers moved, not just that they moved (e.g. win rate dropped while avg deal size rose — likely a shift toward larger, more scrutinized deals). Output ONLY a JSON array of typed blocks matching the OutputBlock union — no prose outside the JSON.USE CASES
Quarterly leadership review prep
Feed the quarter's raw CRM export in and get a headline-metrics-plus-narrative draft ready before the QBR deck build even starts.
Board/investor quarterly updates
Reuse the same headline metrics and narrative format for an investor update, since both audiences want the story behind the numbers, not just the numbers.
Cross-team QBR consistency
Multiple regional sales teams in a GCC-wide operation use the same aggregation format so QBRs are comparable quarter to quarter and team to team.
Churn pattern flagging
The narrative synthesis catches a churn cluster (like multiple accounts citing the same reason) that a raw metrics table would list but not connect.
RESULTS & BENCHMARKS
QBR deck prep time
projected (modeled)
Weekly reporting time (comparable production system)
real, anonymized
LIVE DEMO
Try it right now
FAQ
Common questions
What makes this different from a BI dashboard?
A dashboard shows the numbers; this agent connects them into a narrative — explaining, for example, why win rate and average deal size might have moved together for one underlying reason.
Can it pull data directly from my CRM?
The live demo works from raw numbers you paste in. A production version would connect to your CRM/BI stack the same way the Reporting Agent pattern does in this site's production systems.
What real results back this reporting approach?
The same aggregation-and-narrative approach cut weekly optimisation reporting time from 9 hours to 2 on a live production ad account managed by this site's Google Ads Agent.
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Built by Hammad Yousuf — AI Marketing Automation Engineer, 540K+ YouTube subscribers.
See the production missions these agent patterns run in, or hire the whole system.