Pipeline and forecasting
Forecast categories tied to stages, weekly snapshots of the call, and accuracy measured against what closed.
See the forecast in the live demo →
Finalist, 2016 Microsoft Power BI Dashboard Challenge
Wolfram Nothnagel · RevOps · Sales Ops · Business Intelligence
I build the reporting layer revenue teams run on: Salesforce data model to SQL to Power BI, end to end.
Pipeline, forecast, funnel and quota attainment, defined once, built so sales and finance agree on the number, and delivered where leaders actually look.
Demandbase · HMH · Launch ThatRevOps and BI work across B2B SaaS, education and digital marketing
Live pipeline demoSalesforce → BigQuery → metric layer → dashboards, refreshed from a real org
Power BI finalist, 2016Microsoft Power BI Dashboard Challenge
Definitions firstEvery metric written down, agreed with sales and finance, then built
What I do
Six things revenue teams hire me for
Each one links to proof: a case study, a page of the live demo, or a post on how I think about it.
Full-funnel and conversion analytics
Lead to closed-won with stage dates, source carried through, and the leak named, not guessed.
Case study: full-funnel conversion →CRM business logic and data augmentation
The objects, fields and rules a CRM needs before it can be measured, plus enrichment, reporting and recommendations on top.
Post: data model mistakes that break forecasting →Executive and board reporting
One agreed number for bookings, ARR and forecast, in a deck-ready view that ties back to the CRM.
Case study: one forecast, not five →Quota attainment and sales performance
Attainment by rep and team, plus coaching recommendations that say which part of the deal cycle to work on.
See rep coaching flags in the demo →AI-assisted analytics
Using AI to write SQL, draft briefs and find anomalies faster, with checks that keep a human accountable for the number.
Post: AI without trusting it blindly →Case studies
The work, in the same shape every time
The problem, what I built, the stack, and what changed. Company data is anonymized; the work is mine.
B2B SaaS · Demandbase
One forecast instead of five
Forecast calls lived in spreadsheets and nobody could say how accurate last quarter's call was. I built a snapshot-based forecast model and an executive view tied to the CRM.
Read the case study →K-12 education · HMH
Quota attainment and coaching at scale
A large field sales team, quotas in spreadsheets, and attainment settled by argument. I put quota and crediting rules in the data model and built rep-level performance and coaching views.
Read the case study →Digital marketing · Launch That
Full-funnel conversion, from lead to revenue
Marketing reported leads, sales reported revenue, and nothing joined them. I built a stage-dated funnel with source carried through, so the leak had a name.
Read the case study →Independent build · Max Funnel Analytics
A RevOps metric layer, built end to end
A Salesforce org loading into BigQuery daily, a metric layer for attainment, ARR, retention, funnel and forecast, and the dashboards that read it. The live demo below runs on it.
Read the case study →Writing
How I think about the numbers
Short posts on the definitions and data-model decisions that decide whether a revenue report can be trusted.
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How I define pipeline coverage, and why most teams get it wrong
3x coverage is meaningless if the numerator is stale and the denominator is the full quota.
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Five Salesforce data model mistakes that break forecasting
Most forecast problems are data model problems wearing a dashboard.
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Using AI to speed up analytics without trusting it blindly
Where AI saves me hours, where it quietly gets the number wrong, and the checks in between.
Toolkit
The stack I actually use
Nothing on this list is aspirational. Ask me about any of it in a technical screen.
Power BI
Data modeling, DAX, executive and rep-level dashboards. 2016 Dashboard Challenge finalist.
Salesforce
Data model, opportunity and forecast design, fields, validation and reporting.
SQL · BigQuery
Staging tables, metric marts, snapshot history and the definitions behind every number.
CRM Analytics
Salesforce-native dashboards and datasets for teams that live in the CRM.
Informatica
Data integration and pipelines between the CRM and the warehouse.
Azure ML
Applied models on sales data, such as scoring and propensity, kept behind clear business rules.
About
The person everyone asks for the number
For about ten years I've been the person the CRO, the CFO and the head of customer success all came to for the number, and the one who had to explain why their numbers didn't match. Most of my work is making sure they do: writing the definition down, fixing the CRM so it can be measured, and building the reporting that runs off it.
I've done that inside B2B SaaS at Demandbase, at K-12 publisher HMH, and at Launch That, and I built the pipeline behind the live demo on this site myself.
I live in Sanford, just outside Orlando, and I'm open to remote roles in RevOps, Sales Ops and revenue analytics.
Finalist, 2016 Microsoft Power BI Dashboard Challenge. Selected as a finalist in Microsoft's community dashboard competition, the same craft that goes into the live demo on this site.