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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.
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.
ad_platform_performance: channel, impressions, clicks, spend, campaign_id, dateweb_sessions: visitor_id, session_id, landing_page, utm_source, utm_campaign, form_submit_flagmarketo_leads: lead_id, source_campaign, lead_score, MQL_date, SAL_datesalesforce_opportunities: opportunity_id, lead_id, stage, created_date, closed_won_date, ARRaccount_dimension: account_id, industry, company_size, region, existing_customer_flag