Case study · B2B sales
A pipeline you can forecast from
Sales leaders need to know whether the pipeline will deliver the number, not just how big it is. This is how we built Power BI analytics on CRM data that show coverage, conversion, cycle times and forecast accuracy by team and product.
Project overview
Sales analytics on Power BI.
- Industry
- B2B sales
- Client
- Confidential
- Solution
- Pipeline analytics
- Platform
- Power BI
- Timeline
- 6–10 weeks
- Team
- 3 people
- Source
- Dynamics 365 / CRM
- History
- Opportunity snapshots
- Users
- Sales leaders, sales operations
Expected business outcomes
What the client set out to gain
Forecast accuracy and earlier warnings are the realistic gains.
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10–20%
Better forecast accuracy
Forecasts compared with outcomes, and conversion history used to weight pipeline.
Target range measured over several quarters
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Weekly
Pipeline coverage by team
Coverage against target shown per team and product.
From weekly pipeline snapshots
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Visible
Stalled deals
Opportunities with no activity or slipping dates flagged.
Where CRM activity is recorded
Business challenges
How sales reporting worked before
The CRM holds the pipeline; nobody trusts the totals.
Pipeline
Totals include stale and unrealistic opportunities.
History
No record of how the pipeline changed week to week.
Forecast
Built by adding sellers' guesses.
Conversion
Win rates unknown by product or segment.
Reviews
Pipeline reviews spend time on data instead of deals.
Proposed solution
What the Power BI build covers
Snapshots make pipeline change visible over time.
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Pipeline snapshots
Weekly snapshots of every opportunity to track movement.
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Coverage
Weighted and unweighted pipeline against target by team.
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Win rates
Conversion by stage, product, segment and seller.
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Cycle times
Time in stage and total sales cycle.
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Forecast accuracy
Forecast versus actual by period and team.
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Deal hygiene
Stale opportunities and missing data flagged.
Project requirements
What the client asked us for
The request was a dashboard. The foundation is history: weekly snapshots of the pipeline so movement, slippage and accuracy can be measured.
Deal hygiene reports come early, because they improve the data everything else depends on.
What the project had to deliver
- 01Pipeline history
- 02Coverage against target
- 03Win rates by segment
- 04Forecast accuracy tracking
Key challenges & considerations
What made it harder, and what we did
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Inconsistent stage use
What we did
Stage definitions were agreed and enforced in the CRM before the reports relied on them.
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Opportunities never closed out
What we did
Stale opportunities were flagged to their owners and closed out on a schedule.
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Territory changes
What we did
Territory history was kept, so past performance stayed with the right team.
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Multiple CRMs after acquisitions
What we did
The CRMs from acquisitions were mapped into one model.
What we watched for
- Start snapshots immediately
- Fix stage definitions first
- Show sellers their own hygiene
- Measure forecasts against outcomes
Development process
Snapshots first
History starts accumulating on day one.
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Definitions
Stages, coverage and win rate defined with sales operations.
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Snapshots
Weekly snapshot process started.
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Hygiene
Deal hygiene reports launched.
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Analytics
Coverage, conversion and cycle views built.
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Forecast accuracy
Accuracy tracking after the first full quarter.
Technology stack
Power BI on CRM data
Dataverse or CRM exports feeding a snapshot store.
Reporting
- Power BI
- Row-level security by team
Data
- Dataverse / CRM API
- Snapshot tables
- Fabric
Automation
- Scheduled snapshots
- Hygiene alerts
Governance
- Measure definitions
- Deployment pipelines
Quality assurance & deployment
Every figure reconciled before anyone relied on it
A dashboard is only useful if its numbers are believed. Each measure was reconciled to its source, and to finance where money was involved, before the report was released.
- Each measure reconciled to its source system
- Financial figures signed off by finance
- Data quality checks on every refresh: completeness and freshness
- Row-level security tested with real user accounts
- Report performance tested at full data volume
Deployment
- 01AnalyticsCoverage, conversion and cycle views built.
- 02Forecast accuracyAccuracy tracking after the first full quarter.
- 03Deployment pipelinesReports moved from development to test to production through controlled pipelines.
- 04Adoption and warrantyTraining for report users and builders, usage reviewed, and six months of free bug fixing.
Final deliverables
Forecasts backed by history
Sales leaders see coverage, conversion and cycle times by team, and forecasts are weighted by what actually converts.
Pipeline reviews focus on deals, because data quality issues are flagged automatically.
- Weekly pipeline history
- Coverage and conversion
- Forecast accuracy
- Deal hygiene alerts
Handed over to the client
- 01Data model and pipelinesDocumented and scheduled
- 02Reports and dashboardsPublished with security roles
- 03Measure definitionsEvery figure documented
- 04TrainingFor report users and report builders
- 05Six-month warrantyFree bug fixing after delivery
Success metrics
How success is measured
| Measure | Outcome | How it is measured |
|---|---|---|
| Better forecast accuracy | 10–20% | Target range measured over several quarters |
| Pipeline coverage by team | Weekly | From weekly pipeline snapshots |
| Stalled deals | Visible | Where CRM activity is recorded |
Verified reviews
What clients say about our custom software work
Verified reviews from our clients on the same kind of work, published on Clutch.
-
Custom Software Development
Web-Based Coupon App Development
The work produced by the team was of exceptional quality.
Jon Thies
CTO, Model Rocket Feb 2022 – Mar 2023
-
Custom Software Development
Mobile & Web Platform Development & Design
Silver Scintilla was responsive, respectful, and enjoyable to work with, quickly addressing any questions or requests.
DeShawn Brown
CEO & Founder, Lithios Jun 2021 – Nov 2022
-
Custom Software Development
Web-Based HRM System Development
Silver Scintilla provided the best solution with scalable functionality, personalized features, and robust security.
Suheb Khan
Director, Collaborative Insight Technologies Jul 2020 – Jun 2022
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