SALES OPERATIONS // FREE LIVE DEMO
QBR Data Aggregator
THE PROBLEM
Building a quarterly business review means pulling numbers from five different tools and stitching them into a deck by hand, every single quarter.
9h → 2h
Weekly reporting time (comparable production system)
PROVEN IN PRODUCTION
No pitch — just a plan.
Name the quarter and what leadership cares about — I'll render the whole review as an executive wall: KPIs, the pipeline chart, top accounts, milestones, and the story in under 80 words.
Execution trace — recorded run (this agent's real pipeline)
Pulling the quarter's raw numbers
ok
Computing quarter-over-quarter deltas
ok
Identifying the headline narrative
ok
Structuring the QBR-ready summary
ok
QBR deck prep time
~4h → ~2 min
projected
Weekly reporting time (comparable production system)
9h → 2h
real · anonymized
Pipeline created (amber) vs pipeline lost (grey), AED thousands, by month-pair
| Falcon Freight | Logistics | AED 480K | +22% |
| Nahda Retail Group | Retail | AED 390K | +9% |
| Gulf Assemble | Manufacturing | AED 310K | +31% |
| Marhaba Clinics | Healthcare | AED 240K | flat |
| Oryx Cloud | B2B SaaS | AED 195K | −6% |
Week 2 — Enterprise pricing tier launched
Opened the AED 100K+ deal band; directly behind the avg-deal-size jump and the longer scrutiny cycles.
Week 5 — Gulf Assemble closed at AED 310K
Largest deal of the quarter and the new playbook reference: 3 stakeholders, 2 procurement rounds, won on ROI math.
Week 9 — Two mid-tier accounts churned on pricing
Both cited the same gap between mid-tier price and perceived value — flagged for the pricing review, not a support issue.
Week 12 — Pipeline hit AED 2.1M, a company record
Record entry pipeline for Q4, but weighted toward larger deals — forecast should assume the lower 24% win rate holds.
Executive summary
Pipeline (+16.7%) and average deal size (+18.4%) both grew while win rate fell 5 points — one story, not three: the new enterprise tier pulled the team into larger, more scrutinized deals that close less often but are worth more. Net effect is positive. The real risk is the mid-tier: both churned accounts cited pricing. Recommend entering Q4 with the current motion intact and a mid-tier pricing review before renewals cluster in month two.
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 a quarter and a focus area, produce EXACTLY these blocks from the quarter's dataset (use the synthetic quarter numbers supplied, or a plausible internally-consistent quarter when raw numbers are sparse): (1) a metricCards block with exactly 4 quarter KPIs — label is the KPI name, value is the quarter figure, sub is the QoQ delta; (2) a chart block with a series of exactly 6 entries {sent, bounced} where sent = pipeline created and bounced = pipeline lost for each month-pair, plus a caption that honestly states what the two bar colors mean; (3) a table of the top 5 accounts with columns Account, Segment, ARR, QoQ; (4) a timeline of exactly 4 quarter milestones, each with a label and a detail explaining why it mattered; (5) a copyBlock titled 'Executive summary' of at most 80 words 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. Any figures filled in beyond what the user supplied are an illustrative sample quarter for demo purposes — never present them as this user's real, currently-measured results.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
GET THIS RUNNING ON YOUR BUSINESS
Want QBR Data Aggregator solving this for you?
This runs in production today, not a mockup. Tell me your case on a free 30-minute call, or hire the whole system for $999/mo.
No pitch — just a plan.
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.
RELATED AGENTS
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.