Customer Segmentation & Targeting – M.A.C Cosmetics
Project Details
Name
Fordham University – Data-Driven Marketing Decisions (Simulated Project)
Tools Used
PSS, Excel, RFM Modeling, Two-Step Cluster Analysis, Persona Design, Customer Profiling, Data Transformation
Overview
As part of Fordham’s Data-Driven Marketing Decisions course, I conducted an in-depth segmentation and strategy simulation for M.A.C Cosmetics using dummy datasets. The objective was to analyze customer behavior and design data-backed targeting strategies to drive engagement and product innovation in a declining brand context.
My Role & Approach
I led the data analysis process — merging and transforming synthetic datasets with customer profiles and transaction history. Using SPSS, I conducted multiple two-step cluster analyses, testing segment stability through silhouette scores and managerial relevance. I engineered new variables including Recency, Frequency, and Profit and applied RFM modeling to enhance strategic clarity. I also developed detailed personas aligned to each cluster to guide product, messaging, and channel decisions.
“I don’t just market—I build systems that move people and performance. ”
Rhea Shah
Marketing Strategist
Tools & Techniques Used
- SPSS: Cluster modeling, segmentation iteration, interpretability validation
- Excel: Dataset cleanup, transformation, and exploratory analysis
- Segmentation Inputs: Age, income, order channel, product category, loyalty
- Analysis Models: RFM analysis, persona mapping, geo-targeted rollout strategy
Findings & Strategic Outcomes
- Identified Cluster 2 as high-LTV customers: frequent buyers, loyal, mid-aged, high-income
- Flagged Cluster 4 as a growth segment: older, affluent, under-engaged but highly profitable
- Designed four customer personas to guide tailored messaging and bundling strategies
- Proposed two product innovations and a phased omnichannel rollout strategy targeting M.A.C’s key regions (NY, TX, FL, AL, TN)
This project demonstrated my ability to apply rigorous statistical techniques to marketing strategy — bridging data science and consumer insight to deliver actionable outcomes, even in simulated environments.
Impact
The segmentation strategy revealed high-value customer clusters and untapped growth segments, enabling targeted messaging and bundling opportunities. The project provided a data-backed framework for revitalizing M.A.C’s marketing efforts and demonstrated how analytics-driven personas could guide product innovation and regional rollout plans.
Let’s talk
I’m here to connect — just share your details and i’ll be in touch.