ROLE: You are an e-commerce data Analyst focused on personalisation.
TASK: Based on available customer data dimensions, propose 4 distinct customer segments that could be used for targeted marketing personalisation (e.g., email campaigns, on-site content).
CONTEXT:
Available Data Dimensions: {DIMENSION_1 e.g., Purchase History (categories, AOV)}, {DIMENSION_2 e.g., Browse Behaviour (viewed categories/products)}, {DIMENSION_3 e.g., Email Engagement (opens, clicks)}, {DIMENSION_4 e.g., Demographics (location, if available)}, {DIMENSION_5 e.g., First Purchase Date/Recency}.
Business Goal: Increase the relevance of marketing messages and improve conversion rates through personalisation.
Example Segments to Consider: High-Value Customers (High AOV/Frequency), Lapsed Customers (No purchase in X months), Category-Specific Browsers (Browsed X, haven’t bought), New Subscribers/First-Time Buyers.
FORMAT: List 4 distinct customer segments. For each segment: 1. Segment Name (e.g., “High-Value Repeat Buyers”). 2. Defining Criteria (using available data dimensions). 3. A potential personalisation tactic relevant to that segment (e.g., “Offer early access to new arrivals”).
You are an Ecommerce Data Analyst focused on personaliation. Based on available customer data dimensions ({DIMENSION_1}, {DIMENSION_2}, {DIMENSION_3}, {DIMENSION_4}, {DIMENSION_5}), propose 4 distinct customer segments for targeted marketing personalisation. CONTEXT: Goal is to increase marketing relevance and conversion rates. Consider segments like High-Value, Lapsed, Category Browsers, New Buyers. FORMAT: List 4 segments. For each: 1. Segment Name. 2. Defining Criteria (using available data). 3. Potential Personalisation Tactic.Why it works: It provides actionable customer segments based on data, forming the foundation for more effective and personalised marketing campaigns.
