Customer Segmentation & Targeted Campaigns

Smart Marketing Through Customer Understanding
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Situation

Our marketing team was treating all customers the same, sending identical campaigns to everyone regardless of their engagement level or purchase history. This created several problems:

The Challenges:
  • Wasting marketing budget on inactive customers who rarely respond
  • Not giving enough attention to our most valuable customers
  • Generic "one-size-fits-all" campaigns with poor engagement rates
  • No systematic way to identify who needs re-engagement vs. who needs appreciation
  • Limited understanding of customer behavior patterns

We needed a smarter way to understand our customers and communicate with them based on their actual behavior.

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Task

My goal was to create a customer segmentation system that would:

  • Group customers based on their actual purchasing behavior
  • Identify our most valuable customers so we could protect that relationship
  • Find customers at risk of leaving before they're gone
  • Enable personalized campaigns that speak to each customer's needs
  • Help marketing allocate budget to the highest-value opportunities
  • Be simple enough for the team to understand and act on
A
Action

I implemented a proven customer segmentation framework called RFM (Recency, Frequency, Monetary), which analyzes three key behaviors:

The Three Dimensions:
R
Recency: How recently did they purchase?
Fresh engagement indicates active interest
F
Frequency: How often do they buy?
Repeat purchases show loyalty and satisfaction
M
Monetary: How much do they spend?
Higher value customers deserve premium treatment
Step 1: Analyzed Customer Data
I pulled purchase history for all customers and scored them on each dimension. This created a comprehensive view of customer behavior patterns.
Step 2: Created Meaningful Segments
Based on RFM scores, I grouped customers into actionable segments with clear characteristics and needs.
Step 3: Designed Targeted Campaigns
I worked with marketing to create specific campaign strategies for each segment, matching message and offer to customer behavior.
Step 4: Built Automated Workflows
I set up systems to automatically segment customers and trigger the right campaigns, making this sustainable without manual work.
R
Results

Customer Segments Identified:

🏆 Champions

12% of customers
Recent buyers, frequent purchases, high spending
Strategy: VIP treatment, early access to new products, loyalty rewards

💙 Loyal Customers

18% of customers
Regular buyers with consistent engagement
Strategy: Upsell premium products, encourage referrals, build community

⭐ Potential Loyalists

25% of customers
Recent buyers showing promise
Strategy: Nurture relationship, product recommendations, educational content

⚠️ At Risk

20% of customers
Were good customers, declining engagement
Strategy: Win-back offers, feedback surveys, re-engagement campaigns

😴 Hibernating/Lost

25% of customers
Long time since last purchase
Strategy: Aggressive win-back offers or minimal spend (low ROI)
42%
Higher Campaign ROI
3.2x
Better Response Rates
35%
Retention Improvement
28%
Budget Efficiency
Campaign Performance: Before vs. After Segmentation
3.5%
Before
Generic Campaigns
11.2%
After
Targeted Segments

Campaign Response Rate Improvement: 3.2x increase

Real Business Impact:
  • Smarter Budget Allocation: Marketing spend shifted toward high-value segments with proven ROI
  • Improved Customer Experience: Customers receive relevant messages instead of generic spam
  • Protected Revenue: Early identification of at-risk customers enabled proactive retention
  • Higher Lifetime Value: Potential loyalists nurtured into champions through targeted engagement
  • Data-Driven Decisions: Marketing strategy now based on customer behavior, not guesswork
Example Campaign Success:

"At Risk" Win-Back Campaign:

  • Identified 8,500 previously loyal customers showing declining engagement
  • Sent personalized "We Miss You" campaign with 20% discount
  • Result: 32% re-engagement rate, $425K in recovered revenue
  • Cost: $12K campaign spend → 35:1 ROI

"Champions" VIP Program:

  • Created exclusive early access program for top 12% of customers
  • Result: 45% increase in purchase frequency, 28% higher average order value
  • Customer satisfaction scores increased by 23 points

RFM Segmentation & Campaign Optimization

Behavioral Segmentation for Marketing Personalization
S
Situation

Marketing operations faced challenges with undifferentiated customer communication:

  • No behavioral segmentation framework—all customers treated uniformly regardless of engagement patterns
  • Suboptimal resource allocation—equal marketing spend across heterogeneous customer cohorts
  • Low campaign performance—generic messaging resulting in 3-4% average response rates
  • Lack of churn prediction—reactive rather than proactive retention strategies
  • No systematic approach to customer lifetime value (CLV) optimization
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Task

Design and implement a behavioral segmentation framework to:

  • Create actionable customer segments based on transactional behavior patterns
  • Enable personalized campaign targeting with segment-specific messaging and offers
  • Optimize marketing ROI through intelligent budget allocation
  • Implement automated segmentation pipelines for real-time customer classification
  • Establish measurement framework for campaign effectiveness by segment
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Action

RFM Model Development:

Feature Engineering:

  • Recency (R): Days since last purchase
    • Calculated as: current_date - max(transaction_date)
    • Scored on 1-5 scale using quintile-based binning
  • Frequency (F): Number of transactions in analysis window (12 months)
    • Aggregated via COUNT(DISTINCT order_id)
    • Scored on 1-5 scale using quantile transformation
  • Monetary (M): Total revenue contribution
    • Calculated as SUM(order_value) over analysis period
    • Scored on 1-5 scale, log-transformed due to right-skewed distribution

Segmentation Algorithm:

  • Composite RFM score: RFM_Score = (R_score × 100) + (F_score × 10) + M_score
  • Applied rule-based classification to create 11 segments based on score combinations
  • Validated segments using K-means clustering as comparison baseline
  • Segment definitions stored in configuration-driven lookup table for easy modification

Data Pipeline & Infrastructure:

  • ETL Pipeline: Python-based Airflow DAG extracting from transactional database (PostgreSQL)
  • Feature Store: Materialized views in Snowflake for low-latency segment lookups
  • Refresh Cadence: Daily incremental updates for recency, weekly full refresh for F/M scores
  • Integration: Segment assignments pushed to Salesforce Marketing Cloud via API for campaign activation
  • Monitoring: Segment distribution tracking via custom dashboards (Tableau)

Campaign Strategy & Execution:

Developed segment-specific campaign playbooks:

Champions (R=5, F=5, M=5):
  • Objective: Retention & advocacy
  • Tactics: VIP program enrollment, exclusive previews, referral incentives
  • Channel mix: Email (personalized), SMS, direct mail
  • Frequency: Weekly touchpoints with high-value content
At Risk (R=2-3, F=4-5, M=4-5):
  • Objective: Churn prevention
  • Tactics: Win-back offers (15-25% discount), preference surveys, abandoned browse retargeting
  • Channel mix: Email, retargeting ads, push notifications
  • Timing: Triggered when recency crosses 45-day threshold
Potential Loyalists (R=4-5, F=2-3, M=2-3):
  • Objective: Frequency & value increase
  • Tactics: Product recommendations (collaborative filtering), educational content, cross-sell bundles
  • Channel mix: Email nurture sequences, in-app messaging
  • Success metric: Migration to Loyal/Champions within 90 days