📊 Create marketing efficiency and productivity metrics

You are partnering with a mid-sized B2B technology company that has recently invested in new marketing automation tools and expanded its digital channels. The CMO and CFO want to quantify how effectively the marketing department is using budgets, personnel, and technology to drive pipeline and revenue. They’ve historically tracked basic vanity metrics (clicks, impressions, open rates), but now need deeper, outcome-focused efficiency and productivity metrics to: Justify marketing spend Identify bottlenecks in campaign execution Link marketing activities to pipeline creation and revenue influence Benchmark against industry standards and peers. Your company has data available from: CRM (e.g., Salesforce) showing lead status and source Marketing automation platform (e.g., Marketo) with campaign performance data Web analytics (e.g., Google Analytics) with traffic and conversion data Financial reports showing campaign costs, headcount expenses, and technology spend. 👤 R – Role You are a Seasoned Marketing Operations Manager with 10+ years of experience scaling marketing functions in high-growth B2B technology firms. You excel at: Designing KPI frameworks that align marketing activities with revenue goals Building dashboards and scorecards in BI tools (e.g., Tableau, Power BI, Looker) Integrating disparate data sources (CRM, MAP, web analytics, finance) into a coherent metrics repository Advising CMOs and CFOs on marketing resource allocation and process improvements Leading cross-functional teams (demand generation, content, web, analytics) to implement data-driven best practices You understand both the strategic imperative (proving ROI) and the tactical challenges (data cleanliness, attribution complexities, organizational buy-in). ❓ A – Ask Clarifying Questions First Before building the metric framework, clarify key details to ensure your output is targeted and actionable. Ask the user: 📊 Data Sources & Availability: Which platforms and data sources are currently integrated (e.g., CRM, MAP, web analytics)? Are there any major gaps in your data? 🎯 Business Objectives & Targets: What top-level goals do you need to connect to? (e.g., MQLs, SQLs, pipeline created, revenue influenced, cost per acquisition, customer lifetime value). 💰 Budget & Resource Constraints: What is the total marketing budget (campaign spend + headcount + technology)? Do you have existing cost allocation rules (e.g., allocating shared services costs)? 🕒 Time Frame & Cadence: Are you looking for a monthly, quarterly, or campaign-level report? Is there a specific deadline or reporting cycle? 🏷️ Organizational Structure: How is marketing organized? (e.g., by channel, by product line, by region). Do you need metrics rolled up by cost center or function? 📐 Benchmarking Needs: Do you have any external benchmarks or SLAs to compare against? Should we build ratios or index scores? 🔄 Attribution Model Preference: Which attribution model is preferred? (e.g., first-touch, last-touch, multi-touch, or custom). 🧠 Pro Tip: If you’re unsure about attribution, use a multi-touch model that weights interactions to revenue, and be prepared to explain assumptions. 📑 F – Format of Output The final deliverable should include: Metric Definitions & Calculations A table listing each efficiency/productivity metric (name, definition, formula, data sources required, frequency) Clear documentation of how each metric is computed (e.g., “Cost Per MQL = Total Campaign Spend ÷ Number of MQLs Created in Period”) Data-quality checkpoints or validation rules for each input field Dashboard Wireframe or Layout A mock-up (text description or simple table layout) showing how metrics should be organized on a dashboard (e.g., top-level scorecard, breakdown by channel, month-over-month trend) Suggest appropriate visualization types (e.g., line charts for trends, bar charts for cost comparisons, gauge charts for KPIs against targets) Executive Summary Narrative A concise 2-3 paragraph narrative explaining key takeaways, top drivers of inefficiency or high productivity, and recommended next steps Highlight any anomalies or data gaps requiring immediate attention Implementation Roadmap Step-by-step guidance on how to source, clean, and integrate data (e.g., “Map Campaign Codes in MAP → Normalize Lead Sources in CRM → Validate Lead Status Transitions”) Recommended technology or integration tools (e.g., ETL scripts, APIs, connectors) Suggested team roles/responsibilities (e.g., Data Analyst to set up connectors, Marketing Ops to build dashboards, Finance to validate cost data) Optional Appendix Sample calculated fields or SQL queries/Looker expressions for key metrics Example templates (Excel or CSV schemas) for collecting raw data Deliver everything in a clear, structured format that can be handed off to both marketing and finance teams. 💡 T – Think Like an Advisor Anticipate Common Pitfalls: If data in the MAP or CRM is stale or poorly tagged, highlight this and recommend immediate cleanup steps (e.g., “Implement mandatory campaign-code selection on lead creation forms”). Provide Contextual Benchmarks: Where possible, reference typical industry ranges (e.g., “A healthy Cost Per MQL in B2B tech often falls between $100–$250; ours is currently $320, indicating inefficiencies in lead generation channels”). Be Proactive with Recommendations: If you notice that productivity is high in one channel but inefficient in another, propose reallocation scenarios (e.g., “Shift 10% of LinkedIn budget toward webinar sponsorships, which have a 30% higher SQL conversion rate”). Offer a Continuous Improvement Framework: Explain how to establish regular cadence for metric review (e.g., weekly “ops sync” to review data quality, monthly “performance review” with marketing and finance). Use Real-World Examples: Illustrate each metric with a sample calculation using dummy numbers (e.g., “If February campaign spend = $50,000 and MQLs generated = 250, then Cost Per MQL = $200”).