Professional / Dave & Buster’s
Marketing analytics at Dave & Buster’s
I worked on marketing planning, web performance, attribution, and the data environment behind executive reporting.
- Role / scope
- Senior Analyst, Marketing. August 2024 to November 2025
- Timeframe
- August 2024 to November 2025
- Business problem
- Leadership needed a Q4 media budget recommendation and an explanation for declining web traffic. Marketing data was spread across separate platforms, making analysis and reporting slow.
- Key result
- Recommended $6.9M in Q4 spend against an outside proposal of about $11M. Reduced manual reporting by at least 35%. Unified more than five marketing sources in Databricks.
Marketing data was spread across separate platforms
Marketing data was spread across five major platforms. Analysts spent more than 35% of their time manually preparing data, and reporting on the marketing funnel and cross-channel ROI was difficult.
Leaderboard and Summer Season Pass lacked consistent performance reporting. Leadership also needed a Q4 media budget recommendation on a three-hour deadline, while web traffic was down about 42% year over year.
Integrating marketing data in Databricks
I designed a Databricks-centered marketing-data environment integrating more than five sources, improved Adobe Analytics reporting, and established UTM and attribution governance.
Sources
- SFMC
- Adobe Analytics
- Google Ads / GA4
- Meta Ads
- Sprout Social
Databricks
- ELT pipelines
- Modeling and unification
- Shared marketing metrics
Reporting and decisions
- Executive reporting
- Campaign ROI
- Leaderboard and Season Pass measurement
- Self-service in Power BI
Budget modeling and web-traffic analysis
I built a media-contribution model to evaluate the outside proposal of about $11M for Q4. In the web-traffic analysis, I identified a 40% to 75% deterioration in CTR or message resonance, depending on the comparison, as a major driver of the decline. I presented those findings to VP leadership.
I separately designed and prototyped the company’s first internal Marketing Mix Model using Google Meridian. The MMM was not deployed and was not the model behind the Q4 recommendation.
A three-hour deadline for the Q4 recommendation
The Databricks environment was still being built when the Q4 recommendation was needed. I had three hours to prepare a model for the CEO and CMO, so the recommendation used the data available at the time.
The $6.9M Q4 recommendation
The CEO and CMO used the $6.9M recommendation in their planning. The Databricks environment became the shared source for marketing KPIs, executive reporting, and self-service analysis in Power BI.
Internal contribution model versus outside proposal
Proposed Q4 media spend
~$11M
$6.9M
Three-hour deadlineContribution modelCEO and CMO planning
These are proposed budgets, not actual spend or realized savings. The internal recommendation shaped CEO and CMO planning.
Reporting and analysis results
Manual reporting fell by at least 35%. Integrating more than five sources made cross-channel ROI analysis possible and gave Leaderboard and Summer Season Pass consistent performance reporting.
The Q4 model recommended $6.9M against an outside proposal of about $11M. The web diagnosis identified weaker CTR and message resonance as an important driver of the 42% traffic decline.
Selecting and developing an analyst
I selected and recommended the candidate who was hired into Marketing, then trained him after he joined.