Customer Growth & Retention Analytics Model
(New vs Returning Customers)

The business lacked visibility into customer growth dynamics, specifically how many customers were new versus returning and how their purchasing behaviors differed over time. This limited the ability to evaluate retention and long-term customer value.

Build a scalable analytics model to track customer counts over time and provide insights into customer acquisition, retention, and purchasing behavior.

Designed a customer analytics model in Power BI that segments customers into new and returning groups, enabling time-based comparisons and deeper insight into customer behavior and retention trends.

• Built customer segmentation logic to classify new vs returning customers

• Created time-based measures for monthly, quarterly, and yearly analysis

• Leveraged DAX to track first purchase date and repeat purchase behavior

• Implemented dynamic filtering for flexible time period comparisons

• Designed measures to calculate customer ratios and behavioral differences

• Used data modeling techniques to ensure accurate customer-level aggregation

• Provided visibility into customer acquisition vs retention trends

• Enabled identification of shifts in customer behavior over time

• Supported strategic decision-making around customer growth and retention

• Improved understanding of long-term customer value

• Defining accurate logic for identifying “new” vs “returning” customers

• Managing time-based calculations across multiple date contexts

• Ensuring consistent customer classification across reporting periods

• Balancing model flexibility with performance