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Customer Behavior & Retention Analytics
Customer Behavior & Retention Analytics
Case Study
Case Study
Customer Behavior,
Retention &
Cohort Analysis
Customer Behavior,
Retention &
Cohort Analysis
Customer Behavior,
Retention &
Cohort Analysis
Built a cohort-based analytics framework to track customer lifetime value, repeat purchase behavior, and retention patterns across a D2C eCommerce brand.
Built a cohort-based analytics framework to track customer lifetime value, repeat purchase behavior, and retention patterns across a D2C eCommerce brand.
🏪
🏪
🏪
Industry
Industry
eCommerce / D2C
eCommerce / D2C
📅
📅
📅
Engagement
Engagement
8 Months
8 Months
🛠
🛠
🛠
Platform
Platform
MySQL, Tableau
MySQL, Tableau
🎯
🎯
🎯
Focus
Focus
Retention, LTV, Cohort Analysis
LTV, Cohort Analysis
The challenge
The business wanted to understand customer lifetime value, repeat purchase behavior, and retention patterns — but had no structured framework to track how customers evolved over time. Data existed across platforms but was never stitched together into a coherent picture.
The business wanted to understand customer lifetime value, repeat purchase behavior, and retention patterns — but had no structured framework to track how customers evolved over time. Data existed across platforms but was never stitched together into a coherent picture.
✓
✓
No clarity on how often customers return after first purchase
No clarity on how often customers return after first purchase
✓
✓
Inability to measure customer lifetime value (LTV) accurately
Inability to measure customer lifetime value (LTV) accurately
✓
✓
No visibility into repeat purchase timelines
No visibility into repeat purchase timelines
✓
✓
Difficulty identifying high-value vs low-value customer segments
Difficulty identifying high vs low value customer segments
✓
✓
Lack of insights into cross-platform customer journeys
Lack of insights into cross-platform customer journeys
What this meant for the business
📉
High customer acquisition costs with poor retention ROI
High customer acquisition costs with poor retention ROI
🔁
Low repeat purchase rate with no clear driver identified
Low repeat purchase rate with no clear driver identified
🔍
No visibility into which segments drive long-term revenue
No visibility into which segments drive long-term revenue
🧩
No understanding of cross-sell or bundling opportunities
No understanding of cross-sell or bundling opportunities
📊
Decisions made on intuition, not data
Decisions made on intuition, not data
Our approach
Our approach
01
Data integration
Unified data from website, app, payment gateway, CRM, and marketing platforms into a central warehouse.
02
Cohort framework
Built cohort analysis tracking customer performance over 1, 3, 6, and 12-month windows from acquisition date.
03
LTV & segmentation
Designed LTV tracking by segment, purchase frequency distribution, and days-to-second-order analysis.
04
Dashboard/drill-down
Created executive dashboards with customer-level drill-down views across orders, journeys, and metrics.
Key outcomes
Delivered measurable improvements in retention visibility and long-term revenue strategy.
Delivered measurable improvements in retention visibility and long-term revenue strategy.
🔄
🔄
23%
23%
Increase in repeat purchase rate within 90 days
Increase in repeat purchase rate within 90 days
📈
📈
18%
18%
Improvement in 90-day customer retention
Improvement in 90-day customer retention
💰
💰
15%
15%
Higher customer lifetime value (CLV) identified
Higher customer lifetime value (CLV) identified
🏷
$200K+
$200K+
Identified revenue opportunity through retention and lifecycle optimization
Identified revenue opportunity through retention and lifecycle optimization
What the client said
"
NMK Infotech demonstrated strong responsiveness and attention to detail, particularly in handling complex Tableau reporting and multi-layered data transformations.
Over a long-term engagement with multiple iterations, the team consistently delivered high-quality outputs aligned with business requirements.
NMK Infotech demonstrated strong responsiveness and attention to detail, particularly in handling complex Tableau reporting and multi-layered data transformations.
Over a long-term engagement with multiple iterations, the team consistently delivered high-quality outputs aligned with business requirements.
NMK Infotech demonstrated strong responsiveness and attention to detail, particularly in handling complex Tableau reporting and multi-layered data transformations.
Over a long-term engagement with multiple iterations, the team consistently delivered high-quality outputs aligned with business requirements.
Head of Analytics
D2C eCommerce Brand
Tech stack used
Tech stack used
MySQL
MySQL
Tableau
Tableau
Excel
Excel
Facing similar challenges?
Let's solve it.
Facing similar challenges?
Let's solve it.
Facing similar challenges?
Let's solve it.
Let's discuss your use case and build clarity around what matters.
Let's discuss your use case and build clarity around what matters.
Let's discuss your use case and build clarity around what matters.