Data Collection and Integration:

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The Solution: A Multi-Dimensional Segmentation Strategy
To address these challenges, Evergreen Financial embarked on a comprehensive customer segmentation project. Their approach was multi-dimensional, combining various data points to create robust and actionable segments.


The first critical step involved consolidating data from various internal systems, including:

Demographic Data: Age, gender, income, marital status, education level, geographic location.
Transactional Data: Account balances, transaction frequency, average transaction size, product holdings (savings accounts, checking accounts, loans, credit cards, investments).
Behavioral Data: Online banking activity, mobile app usage, call center interactions, branch visits, response to previous marketing campaigns.
Psychographic Data (inferred): Lifestyle preferences, financial goals, risk tolerance (derived from product choices and historical behavior).
Choosing the Right Segmentation Variables:

Evergreen Financial didn't just rely on demographics. They strategically combined variables to uncover deeper insights:

Recency, Frequency, Monetary (RFM) Analysis: This classic marketing technique was applied to transactional data to identify high-value customers based on how recently they transacted, how frequently they transacted, and how much money they spent.
Product Holdings and Usage Patterns: This helped identify customers interested in specific financial solutions (e.g., mortgage holders, active traders, small business loan recipients).
Digital Engagement Levels: Differentiating between "digital natives" who prefer online channels and "traditionalists" who frequent branches.
Life Stage and Financial Goals: Categorizing customers based on their likely financial needs at different points in their lives (e.g., young professionals saving for a down payment, families planning for education, retirees managing wealth).
3. Employing Advanced Analytics:

Evergreen Financial leveraged advanced analytics techniques to identify distinct clusters within their vast customer dataset.