Data Analyst

Digital Marketing

Role

These
professionals analyse digital marketing data using Python and SQL to extractinsights, optimize campaigns, and support decision-making. They serve as thebridge between raw marketing data and actionable business intelligence.

Key

• Data Extraction & Transformation:

• Use SQL to query large marketing databases (e.g., from
  

Google Analytics, ad platforms, CRMs).

• Clean, transform, and prepare datasets using Python
  

(Pandas, NumPy).

• Marketing Analytics:

• Track performance of digital campaigns (SEO, SEM,
  

social media, email marketing).

• Build marketing attribution models to understand ROI.
  

• Dashboard & Reporting:

• Develop automated reports and dashboards using tools
  

like Tableau, Power BI, or Python libraries (e.g., Plotly, Dash).

• Provide stakeholders with real-time insights.
  

• Predictive Modelling & Insights:

• Build models for customer segmentation, churn
  

prediction, LTV analysis, and campaign optimization.

• Use machine learning (e.g., scikit-learn, XGBoost) for
  

targeting and personalization.

• Cross-functional Collaboration:

• Work with marketing, product, and sales teams to
  

define KPIs and optimize strategies.

• Translate business questions into data problems and
  

communicate results clearly.

Typical

• Languages:

  Python,

SQL

• Libraries:

  Pandas,

NumPy, scikit-learn, Matplotlib, seaborn

• Platforms:

  Google

Analytics, Facebook Ads, HubSpot, Salesforce

• Visualization:

  Tableau,

Power BI, Looker, or Python-based dashboards

Impact:

• Drive better targeting and personalization
  
• Improve campaign efficiency and ROI
  
• Support strategic decision-making with data