Project Context

Databricks' Growth Marketing team needs to improve the ROI of a paid demand generation campaign promoting Lakehouse for AI to mid-market and enterprise prospects in North America. The campaign is already live across LinkedIn, Google Search, and programmatic display, but finance has flagged that pipeline efficiency is below plan and the CMO wants a corrective plan before next quarter's budget review. You are the Marketing Analytics Specialist partnering with a 9-person cross-functional team across marketing, sales, finance, and data.

Key Stakeholders

The VP of Demand Gen wants fast optimizations that preserve lead volume. Finance wants CAC and pipeline ROI improved within the current quarter. Sales leadership is concerned that tightening targeting will reduce MQL flow to SDRs. The Databricks campaign operations team needs any tracking or audience changes implemented without disrupting active spend.

Constraints

The campaign has 10 weeks left in quarter, a remaining media budget of $420,000, and no additional headcount. Current blended ROI is 1.6x against a target of 2.3x. You have access to campaign data in Delta Lake, dashboards in Databricks SQL, and audience/performance data from three ad platforms, but attribution is delayed by 7 days and 18% of opportunities still have incomplete campaign touchpoint mapping. Engineering can support only one tracking fix in the next 3 weeks.

Complications

1. The VP of Sales has already committed MQL volume targets to regional leaders and will resist any channel cuts that reduce top-of-funnel by more than 10%.
2. A major product webinar launches in 4 weeks and depends on this campaign for registrations, creating tension between short-term ROI and event attendance goals.
3. LinkedIn performance appears strong on lead volume but weak on sourced pipeline quality, and the attribution model is being questioned.

Deliverables

1. Build a 10-week execution plan to improve campaign ROI, including sequencing, owners, and decision points.
2. Define how you would diagnose the ROI problem using Databricks data assets and what trade-offs you would make across channels, audiences, and offers.
3. Recommend a launch plan for optimizations, including stakeholder alignment, reporting cadence, and rollback criteria.
4. Identify the top risks to delivery and performance, and explain how you would mitigate them.
5. Specify the success metrics you would use to judge whether the campaign turnaround worked.