Business Context

Databricks ran a 6-week integrated campaign promoting Lakehouse for AI to enterprise data and AI leaders across paid LinkedIn, Google Search, email nurture, and webinar follow-up. Marketing leadership wants a clear framework to measure whether the campaign drove efficient pipeline, not just top-of-funnel engagement.

Metric Scenario

The campaign reached 1.8M impressions, generated 42,000 ad clicks, 9,600 landing-page visits, 1,440 form fills, 510 marketing-qualified leads (MQLs), 120 sales-accepted leads (SALs), 36 opportunities, and 9 closed-won deals. Total campaign spend was $420,000. Average ARR per won deal is $185,000, with first-year gross margin of 78%. Historical benchmarks for similar Databricks enterprise campaigns are: landing-page conversion 12%, MQL rate from leads 38%, opportunity rate from SALs 32%, and CAC payback target under 18 months.

Stakeholders disagree on success: the demand gen team points to strong click volume, sales says lead quality was weak, and finance wants to know whether the campaign created positive unit economics.

Requirements

1. Define the primary metric or metric set you would use to evaluate campaign effectiveness for Databricks.
2. Calculate the key funnel and efficiency metrics from the data above.
3. Identify where in the funnel performance is above or below benchmark.
4. Explain how you would separate campaign engagement from true business impact.
5. Recommend which metrics should be used as leading indicators versus final success metrics.

Data Available

• ad_platform_performance: channel, impressions, clicks, spend, campaign_id, date
• web_sessions: visitor_id, session_id, landing_page, utm_source, utm_campaign, form_submit_flag
• marketo_leads: lead_id, source_campaign, lead_score, MQL_date, SAL_date
• salesforce_opportunities: opportunity_id, lead_id, stage, created_date, closed_won_date, ARR
• account_dimension: account_id, industry, company_size, region, existing_customer_flag