As marketing teams grapple with fragmented data across CRMs, social channels, and analytics platforms, Marketscience has released a landmark report that evaluates how leading generative AI models perform on the everyday challenges marketers face. Titled “Streamlining Marketing Data Management with Generative AI,” the report compares ChatGPT, Claude, and DeepSeek on practical tasks like data cleaning, cross-table recognition, time series analysis, and code generation for ETL pipelines.
“Many businesses know their data is underused, but they don’t know where to begin,” said Sebastian Shapiro, Managing Partner at Marketscience. “This research helps executives see how AI tools actually perform on the work marketers do every day.”
Research Findings: Claude Comes Out on Top
Marketscience evaluated the models across four core use cases in marketing data automation:
1. Data Processing & Cleaning
Claude produced structured, annotated code with strong error handling. ChatGPT was effective but more assumption-prone. DeepSeek struggled with stability and missed key logic checks.
2. Cross-Table Data Recognition
Claude excelled at understanding relationships and delivering reusable output. ChatGPT performed well but occasionally mislinked table joins. DeepSeek required re-prompting frequently.
3. Time Series Analysis
Both Claude and ChatGPT handled common marketing trends (e.g., seasonality, anomalies), though Claude provided more robust context and documented assumptions.
4. Code Generation for ETL Pipelines
Claude led in clarity and explanation. ChatGPT was fast but less transparent. DeepSeek often delivered incomplete or non-functional code.
Strategic Takeaways for CMOs & Data Teams
- AI = Time Savings, Not Total Autonomy: Generative AI can reduce the grunt work in data prep—but it still requires well-structured inputs and human QA, especially when dealing with ambiguous business rules.
- Claude Sets the Standard: For enterprise-grade marketing data workflows, Claude currently offers the best mix of reliability, documentation, and contextual accuracy.
- Know When to Intervene: AI models can hallucinate or make silent assumptions. Use them as co-pilots, not replacements, in workflows with compliance or financial impact.
New Diagnostic Offering: AI Automation Readiness in 2–3 Weeks
To help brands act on these findings, Marketscience now offers a Marketing Data Diagnostic—a 2–3 week engagement that:
- Maps your current data workflows (collection, transformation, integration)
- Identifies where AI/LLMs can reduce cost and manual effort
- Flags high-risk areas where human oversight is still needed
Ideal for marketing ops teams, CMOs, and data leads looking to modernize marketing infrastructure without overspending on unproven tools.
Curious about how LLMs could transform your data stack?
Subscribe to AdTechEdge for our upcoming breakdown of the Marketscience Diagnostic and exclusive interviews with marketing data leaders using AI to drive ROI.
