---
esco_uri: http://data.europa.eu/esco/skill/11c56452-fcec-4b00-9695-cca4728e5048
esco_label: "marketing analytics"
relation: optional
onet_soc: 15-2051.00
source: model-knowledge
confidence: high
qa_count: 0
generator: gemma3:27b (prompt-designed and spot-checked by Claude)
generated: 2026-07-10
---

# marketing analytics — Data Scientist

As a Data Scientist working with marketing analytics, forget broad 'marketing effectiveness' – think *attribution* and *optimization*. Your daily work isn’t creating campaigns; it’s building models to understand which touchpoints (ads, emails, social posts) truly drive conversions. This means heavy use of statistical modeling (regression, time series), causal inference techniques (propensity score matching, difference-in-differences) to move beyond correlation and prove impact, and A/B testing analysis. You'll be constantly evaluating ROI not just at a campaign level, but down to keyword bids or creative variations.

Typical tasks involve building marketing mix models (MMM), customer lifetime value (CLTV) prediction, segmentation for personalized targeting, and churn prediction focused on marketing-influenced retention. Tools go beyond basic BI – expect SQL/Spark for data wrangling, Python/R for modeling, cloud platforms (AWS, GCP, Azure) for scalability, and potentially specialized tools like Google Marketing Platform or Adobe Analytics APIs to pull in granular campaign data.  You'll likely be working with messy, high-dimensional marketing datasets requiring significant cleaning and feature engineering.

A common pitfall is falling into the 'last-click attribution’ trap – assuming the final interaction gets all the credit. Another is overfitting models to historical data without considering external factors (seasonality, competitor actions). Good looks like not just *reporting* on metrics but providing actionable insights: ‘Increasing spend on channel X by Y% will likely yield Z% lift in revenue,’ backed by robust statistical evidence and clear communication of uncertainty.  Being able to translate complex model outputs into business-friendly recommendations is crucial.

Finally, remember marketing data often has inherent biases (selection bias in ad targeting, self-reported data). A strong Data Scientist proactively identifies these biases, incorporates them into the modeling process or clearly communicates their limitations. It's about building *reliable* predictive power, not just achieving high accuracy on a training set.

## Weitere Anreicherung

Stage-2 source for future practitioner grounding: arXiv cs.LG/stat.ML (ML preprints).
