Role: Lifecycle Strategist & Conversion Copywriter

Execution Platform: Klaviyo

Data Engine: E-Commerce Customer Cohort Dataset (9,000+ Records, Kaggle)

Deliverables: 5 Master Flow Specifications, Dynamic Segment Definitions, RFM-Driven Conditional Logic, Native Coupon Mechanics

Supporting Materials

• Production Copy Deck (Awareness/Sophistication Framework): View the full 16-email copy deck — every email referenced below (1A, 2B, 3A, etc.) is written in full here, organized by awareness stage and sophistication level rather than by flow, so cross-reference by email ID.
• Workflow Screenshots (Klaviyo Build): View the flow-builder screenshots — visual proof of the conditional branches, delays, and trigger filters described in each Master Flow section below.
• Raw Dataset (Kaggle E-Commerce Customer Segmentation): View the source CSV — 9,000+ customer records used as the offline cohort-modeling foundation for the flow logic below. Note: this is spec/portfolio work. The dataset's native Segment_Label column was not used; Value_Tier and all other profile properties referenced in this architecture were derived from my own RFM-based segmentation logic, built independently of the source labels.

Credentials

• Digital Marketing Certificate — Klaviyo. Issued Aug 2026 · Expires Aug 2028. Credential ID 192796669. Verify credential
• Klaviyo Deliverability Certificate — Klaviyo. Issued Aug 2026 · Expires Aug 2028. Credential ID 192785976. Verify credential
• Klaviyo Practitioner Certificate — Klaviyo. Issued Aug 2026 · Expires Aug 2028. Credential ID 192795538. Verify credential

All three certifications are verifiable via LinkedIn.

Executive Overview

Many e-commerce brands struggle with lifecycle marketing because they rely on fragmented, overlapping automation setups. When a customer triggers a browse abandon flow, a post-purchase sequence, and a general promotional broadcast in the same 24-hour window, the result is subscriber fatigue, broken attribution, and margin leakage via unnecessary discounting.

To solve this, I engineered an enterprise-grade, event-driven lifecycle system in Klaviyo. Driven by exploratory data analysis (EDA) of a Kaggle e-commerce dataset, this architecture consolidates 8 overlapping customer segments into 5 Master Lifecycle Flows. By leveraging real-time event triggers and conditional branch logic, this system maximizes Customer Lifetime Value (LTV) while protecting gross margins.

               ┌─────────────────────────────────────────┐
               │         INCOMING EVENT / SIGNAL         │
               └────────────────────┬────────────────────┘
                                    │
    ┌──────────────────┬────────────┴───────────┬──────────────────┐
    │ Checkout/View    │ Order Placed           │ Inactivity       │ Unengaged 30d+
┌───▼────────────┐ ┌───▼────────────┐       ┌───▼────────────┐ ┌───▼────────────┐
│ Master Flow 1  │ │ Master Flow 2  │       │ Master Flow 3  │ │ Master Flow 4  │
│ Recovery       │ │ Post-Purchase  │       │ Win-Back       │ │ Nurture/Sunset │
└───┬────────────┘ └───┬────────────┘       └───┬────────────┘ └───┬────────────┘
    │                  │                        │                  │
    └──────────────────┴───────────┬────────────┴──────────────────┘
                                   │ (If Exited or Inactive in Flows 1–4)
                       ┌───────────▼────────────┐
                       │   Standard Promo      │
                       │   Cadence (Flow 5)     │
                       └────────────────────────┘

See workflow screenshots for the actual built version of this diagram inside Klaviyo's flow editor.

Data Foundation & Kaggle Dataset Framing

This architecture is built as a spec strategy and production blueprint based on a raw e-commerce customer behavior dataset sourced from Kaggle.