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Customer and Marketing Analytics in Ecommerce Company

PostgreSQL
Python
Challenges
  • Understanding churn reasons and predicting at-risk customers to reduce revenue loss.
  • Identifying high-potential customer segments and optimizing marketing efforts.
  • Staying ahead by understanding customers better than competitors. 
  • Enhancing customer experience through personalized recommendations..
  • Promoting a data-driven culture for improved decision making.
Solutions
  • Analyzing churn, identifying at-risk customers, and implementing retention strategies  
  • Scoring and ranking customers for personalized marketing and tailored offers.
  • Efficient resource allocation based on customer analytics for maximum ROI.
  • Identifying cross-selling and upselling opportunities for increased spending and revenue.
Benefits
  • Increased conversion by 15% in Catalog and 20% in Email Channel using predictive analytics.
  • Enhanced Customer Understanding.
  • Incremental revenue using targeted Campaigns.
  • Improved ROI.
  • Increase customer loyalty.

US based Health products company (ecommerce)

The customer struggled with the imperative need of leveraging customer and marketing analytics to proactively address churn, optimize marketing efforts, and elevate customer experiences through tailored recommendations, all while cultivating a data-centric decision-making culture.