Operational & People Analytics to Improve Marketing and Sales Effectiveness

Revenue teams don’t struggle due to a lack of tools—they struggle because performance signals are fragmented across marketing platforms, CRM, sales activity, and people/process data. We built an operational + people analytics layer that connected marketing performance → pipeline → sales execution → productivity outcomes, and added AI capabilities (forecasting, anomaly detection, explainable drivers, and assistant-style exploration) so leaders could act faster and improve conversion efficiency.

At-a-glance

• Industry: B2B / SaaS / Services (Revenue operations)
• Core problem: Fragmented data across marketing + CRM + sales activity made ROI, pipeline health, and rep productivity hard to measure reliably
• What we delivered: A unified revenue operations analytics layer + governed KPIs + AI-driven insights and action queues
• Primary impact: Better attribution clarity, improved pipeline predictability, productivity visibility, and faster decision cycles
• Core stack: OneLake (data layer), Fabric Warehouse (analytics), Python (ML + modeling), Domo/Power BI (dashboards), Fabric Copilot (self-serve insight layer)

The challenge: the team had data—just not decision-grade truth

Marketing had performance reports. Sales had CRM dashboards. Leadership had forecasts. But none of it reconciled cleanly. Teams spent too much time debating “whose numbers are right,” and too little time improving funnel performance. The biggest gaps were: inconsistent KPI definitions, attribution ambiguity, and no clear link between people’s actions (sales activity, follow-ups, SLA adherence) and pipeline outcomes.

What we set out to solve:

• Build a single source of truth across marketing, CRM, sales activities, and productivity signals
• Standardize KPI definitions across teams (MQL/SQL, stages, velocity, conversion, CAC/ROI)
• Make pipeline health explainable: drivers, drop-offs, and bottlenecks
• Add predictive capability: forecast pipeline outcomes and risk
• Add an AI layer: anomaly detection, driver explanations, and guided self-serve Q&A/coprocessing
• Operationalize actions: rep-level and team-level “what to do next” views

“When sales and marketing share one truth, you stop arguing about the funnel—and start improving it.”

What “good” looked like (success criteria)

We aligned success around measurable outcomes for revenue leadership and RevOps: trusted metrics, predictable pipeline, and clear productivity-to-outcome visibility.

Success criteria:

• Trust: one governed KPI layer used across all dashboards
• Explainability: clear funnel drivers (why conversion changed, where leakage happens)
• Predictability: pipeline forecasting with confidence bands and risk flags
• Productivity clarity: rep and team activity → outcome linkage (not vanity activity metrics)
• Actionability: daily/weekly action queues and exception-first views
• Scalability: easy onboarding of new channels, regions, products, and teams

Solution overview

We implemented a revenue intelligence foundation: data lands into OneLake, curated into a governed warehouse model (Fabric Warehouse), and served through dashboards (Domo/Power BI). On top of reporting, we added AI layers: funnel driver decomposition, anomaly detection, and predictive pipeline forecasts. Finally, we operationalized actions with prioritized queues for RevOps leaders and sales managers.

Gallery

1. Unified data foundation (OneLake)

We consolidated data across:

• Marketing spend and performance (channels, campaigns, cohorts)
• CRM pipeline (stages, owners, deal metadata, lifecycle events)
• Sales activity (calls, emails, meetings, SLA adherence)
• People structure (teams, territories, quotas, onboarding cohorts)

Key complexity handled: identity resolution (lead/account/contact), campaign-to-opportunity mapping, and clean definitions for lifecycle stages.

2. Decision-grade revenue model (Fabric Warehouse)

We created an analytics model designed for real questions:

• Funnel conversion by stage, segment, and channel
• Pipeline velocity (time-in-stage, aging, stuck deals)
• Sales capacity and productivity (coverage, load, response SLAs)
• Attribution-ready views (campaign influence, cohort performance)
• Executive rollups with drilldowns to rep/territory/campaign

3. AI layers: prediction, anomalies, and explainable drivers (Python + Copilot)

We layered practical AI—focused on decisions, not demos:

• Predictive pipeline forecasting: expected bookings and slippage risk by segment/stage
• Deal risk scoring: likelihood of close / slip using historical stage patterns and activity signals
• Anomaly detection: alerts for sudden conversion drops, velocity spikes, or channel ROI regressions
• Driver explanations: “what changed” breakdowns (mix shift vs true conversion change)
• Copilot layer: guided exploration so leaders can ask “why did MQL→SQL drop?” and get metric-consistent answers tied to the governed model

4. Action workflows: exception-first dashboards + queues

We operationalized insights into daily workflows:

• Rep-level queue: aging deals, missing next steps, SLA breaches
• Manager view: team bottlenecks, coaching opportunities, capacity risks
• Marketing view: channel quality, cohort conversion, CAC/ROI movement
• Leadership view: forecast, pipeline health, scenario sensitivity

This reduced “spreadsheet ops” and increased execution speed.

Implementation playbook (how we delivered without chaos)

Revenue analytics fails when teams jump to dashboards before governance. We delivered in sequence: definitions → model → reporting → AI → actions, with validation checkpoints each step.

• Phase 1: KPI governance — stage definitions, attribution rules, ownership
• Phase 2: Data foundation — OneLake ingestion + identity resolution + quality checks
• Phase 3: Warehouse model — Fabric Warehouse + reconciliations
• Phase 4: Reporting rollout — Domo/Power BI control tower + drilldowns
• Phase 5: AI enablement — forecasting, anomalies, risk scoring, Copilot prompts
• Phase 6: Enhancement and ongoing support — weekly operating rhythm and continuous improvement

Impact

• Improved pipeline predictability with forecast + risk flags
• Faster funnel diagnosis via driver decomposition (mix vs true performance change)
• Higher operational clarity on what activities correlate to outcomes
• Reduced time wasted on reconciliation due to KPI governance and one model
• More effective interventions through exception-first queues and coaching views

Technology stack

• OneLake — unified landing zone for marketing/CRM/activity data
• Microsoft Fabric Warehouse — governed analytics model
• Python — forecasting, risk scoring, anomaly detection, driver decomposition
• Domo / Power BI — dashboards and control tower views
• Fabric Copilot — guided insight exploration on top of governed KPIs