Inconsistent Stage Definitions
Lead, MQL, SQL, opportunity, and sourced pipeline were not defined or measured through a single shared model.
B2B SaaS reporting system that connected channel spend to qualified pipeline
A mid-market SaaS team had acquisition, marketing automation, CRM, and pipeline data split across separate systems. The rebuild created one controlled view of spend, lead quality, funnel conversion, and qualified pipeline.
Lead
MQL
SQL
Opportunity
Won
Project Scope
This work covered a full reporting system built end-to-end: KPI definition, source mapping, funnel structure, data-model architecture, automated data ingestion & normalization, dashboard development, documentation, and a weekly operating cadence used to turn reporting into action. Marketing, sales, revenue operations, and finance stakeholders contributed requirements and validated definitions. The reporting model, workflows, and operating views were then designed and implemented as one connected system.
The Starting Point
Channel spend, performance, attribution, lead stages, and sales notes were available, but they were reviewed across separate systems, and pulled manually by separate teams. There was no source of truth, and accuracy between platforms varied by an unknown margin. As a result, the team could share reports on activity but did not have a consistent and confident operating view of lead quality, stage conversion, pipeline contribution, return on ad spend, or the actions required to improve performance.
Fragmented Reporting
Spend, funnel performance, lifecycle activity, and pipeline reporting had no single source of truth.
No Cohort-Based Attribution
Deals weren’t tracked back to the source channel and spend that generated them, blocking visibility into performance.
The System Built
A full end-to-end consistent reporting model connecting demand generation to pipeline performance.
Standardized definitions were created for all metrics and KPIs aligning teams around a shared model with full lifecycle visibility. Data integrations from multiple tools, including capability for ongoing custom inputs from offline activity, were built and designed to automatically pull fresh data every three hours. Data was stored and accessible in Google Sheets for quick access, and into Looker Studio for reporting and visualization dashboards that were built to leadership's specifications. Cohort modeling was included in this process to give full visibility into the marketing activity and spend that initially generated the lead, and a clear view on the sales cycle by product.
Defined KPIs & Metrics
Automated Data Ingestion
Data Storage
Data Visualization & Cohort Modeling
The Outcome
A unified reporting environment connected acquisition performance to funnel quality, qualified pipeline, and weekly decision-making.
The recreated dashboard uses mathematically consistent sample data to show the reporting structure, KPI logic, and operating workflow without exposing confidential business information.
Executive Visibility
Leadership can confidently review budget, spend, funnel conversion, cost efficiency, qualified pipeline, and performance changes in one concise, up-to-date view.
Channel-to-Pipeline Analysis
Marketing can track performance of KPIs from a single source of truth instead of manually in each platform.
Home/Growth Reporting Workbook/Executive Summary
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Date Range
May 1 – May 31, 2025
Compare To
Last Month
Business Unit
All
Channels
All Paid Channels
Data updated
June 1, 2025 at 8:05 AM
Counts reflect activity during the selected date range. Conversion rates use records that exited each stage during that period.
Overview
Paid Sessions
31,500
7.1%MoM
Leads Created
1,260
9.6%MoM
MQLs Created
420
16.7%MoM
SQLs Created
126
20.0%MoM
Opportunities Created
42
16.7%MoM
Closed Won
12
20.0%MoM
Closed Lost
18
10.0%MoM
Paid Acquisition Efficiency
Total Spend
$138,600
5.0%MoM
CPL
$110
4.2%MoM
CPMQL
$330
10.0%MoM
CPSQL
$1,100
12.5%MoM
CPOpp
$3,300
10.0%MoM
Sourced Pipeline
$1.01M
21.7%MoM
Avg. Pipeline / Opp
$24.0K
4.3%MoM
Pipeline ROAS
7.3x
1.0xMoM
Stage Conversion
| Transition | Advanced / Resolved | Conversion rate |
|---|---|---|
| Lead → MQL | 420 / 1,280 | 32.8% |
| MQL → SQL | 126 / 414 | 30.4% |
| SQL → Opportunity | 42 / 141 | 29.8% |
| Opportunity → Closed Won | 12 / 30 | 40.0% |
Win rate: 40.0%, up 6.7 points MoM from 33.3%.
Conversion rates use records that exited each stage during the selected date range.
Channel Spend Pacing
| Channel | Target | Actual | Pacing | Pipeline ROAS | Sourced Pipeline |
|---|---|---|---|---|---|
| Paid Search | $50,000 | $48,500 | 97% | 8.0x | $388,000 |
| $32,000 | $31,360 | 98% | 6.0x | $188,160 | |
| Review Sites | $18,000 | $18,360 | 102% | 10.5x | $192,780 |
| Paid Social | $16,000 | $14,720 | 92% | 4.5x | $66,240 |
| Content Syndication | $11,000 | $10,450 | 95% | 6.0x | $62,465 |
| Retargeting | $10,000 | $8,900 | 89% | 8.5x | $75,650 |
| Video | $8,000 | $6,310 | 79% | 5.5x | $34,705 |
| Total | $145,000 | $138,600 | 96% | 7.3x | $1,008,000 |
Channel Performance: Spend vs Pipeline ROAS
Lead to Opportunity Cohort Analysis
| Lead Week | Leads | Opps | Lead → Opp | 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 | 17 | 18 | 19 | 20 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1/6/25 | 255 | 12 | 4.7% | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 2 | 0 | 0 | 2 | 2 | 0 | 0 | 2 | 2 | 0 | 0 | 0 |
| 1/13/25 | 265 | 13 | 4.9% | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 1 | 2 | 0 | 1 | 1 | 2 | 2 | 0 | 1 | |
| 1/20/25 | 275 | 14 | 5.1% | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 2 | 0 | 0 | 2 | 2 | 0 | 0 | 2 | 2 | 2 | ||
| 1/27/25 | 285 | 14 | 4.9% | 1 | 0 | 0 | 0 | 2 | 0 | 0 | 1 | 2 | 0 | 0 | 2 | 2 | 0 | 0 | 0 | 2 | 2 | |||
| 2/3/25 | 260 | 9 | 3.5% | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 2 | 1 | 0 | 0 | 2 | 2 | 0 | 0 | 0 | ||||
| 2/10/25 | 270 | 10 | 3.7% | 0 | 0 | 0 | 0 | 2 | 1 | 0 | 1 | 2 | 2 | 0 | 1 | 0 | 1 | 0 | 0 | |||||
| 2/17/25 | 280 | 10 | 3.6% | 1 | 0 | 0 | 0 | 2 | 1 | 0 | 0 | 1 | 1 | 0 | 0 | 2 | 1 | 1 | ||||||
| 2/24/25 | 290 | 9 | 3.1% | 1 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 2 | 2 | |||||||
| 3/3/25 | 215 | 4 | 1.9% | 1 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | ||||||||
| 3/10/25 | 220 | 4 | 1.8% | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | |||||||||
| 3/17/25 | 225 | 3 | 1.3% | 1 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | ||||||||||
| 3/24/25 | 230 | 3 | 1.3% | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | |||||||||||
| 3/31/25 | 235 | 2 | 0.9% | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | ||||||||||||
| 4/7/25 | 275 | 1 | 0.4% | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | |||||||||||||
| 4/14/25 | 285 | 1 | 0.4% | 0 | 0 | 0 | 1 | 0 | 0 | 0 | ||||||||||||||
| 4/21/25 | 290 | 1 | 0.3% | 1 | 0 | 0 | 0 | 0 | 0 | |||||||||||||||
| 4/28/25 | 300 | 0 | 0.0% | 0 | 0 | 0 | 0 | 0 | ||||||||||||||||
| 5/5/25 | 300 | 0 | 0.0% | 0 | 0 | 0 | 0 | |||||||||||||||||
| 5/12/25 | 310 | 0 | 0.0% | 0 | 0 | 0 | ||||||||||||||||||
| 5/19/25 | 320 | 0 | 0.0% | 0 | 0 | |||||||||||||||||||
| 5/26/25 | 330 | 1 | 0.3% | 1 |
The Impact
The reporting system changed how performance was managed, not just how it was displayed. Marketing, revenue operations, sales, and finance worked from the same KPI definitions, reviewed the same channel-to-pipeline view, and tied weekly decisions to documented actions and owners.
44%
Relative improvement in MQL-to-SQL conversion
Clearer lifecycle definitions, routing rules, and channel-quality visibility increased the share of marketing-qualified leads that progressed to sales qualification.
55%
Reduction in cost per SQL
Budget shifted toward the channels and campaigns producing qualified leads more efficiently, reducing reliance on platform-level volume metrics.
37%
Year-over-year increase in qualified pipeline
A shared view of acquisition, funnel conversion, and pipeline contribution improved prioritization across marketing, revenue operations, and sales.
Impact figures reflect anonymized outcomes from the original operating environment. Dashboard visuals use recreated sample data.
Need one reliable view from spend to pipeline?
A reporting-system build can include source auditing, KPI definitions, data normalization, funnel standardization, dashboard development, QA, documentation, and weekly review built around the tools already in place.
- Project type
- Reporting and revenue operations
- Systems
- CRM, marketing automation, paid channels, BI
- Needed outcome
- One reliable view from spend to pipeline