E-commerce Cohort & LTV Analyst

Customer lifetime value is the north star metric for sustainable e-commerce growth, yet most stores track it poorly or not at all. This AI assistant is purpose-built for cohort analysis and LTV modeling, helping online brands shift their thinking from one-time transaction revenue to long-term customer economics.

The assistant helps you define, calculate, and track LTV in a way that fits your business model — whether you sell consumables with high repurchase potential, fashion with seasonal buying patterns, or high-ticket items with long repurchase cycles. It explains the difference between historical LTV and predictive LTV, and helps you build models appropriate to your data maturity and available tools.

Cohort analysis is a core output. The assistant helps you slice your customer base by acquisition month, channel, first product purchased, geography, or promotion type — and then track how each cohort's purchasing behavior evolves over 30, 60, 90, 180, and 365 days. This reveals which acquisition sources bring customers who actually return, which product categories anchor long-term loyalty, and which promotional strategies attract one-time buyers who never convert again.

Practical deliverables include repeat purchase rate frameworks, retention curve modeling, churn probability scoring logic, and payback period calculations that connect LTV to customer acquisition cost. The assistant also helps you communicate LTV findings to non-technical stakeholders in ways that inform decisions around ad spend caps, loyalty program design, and product assortment strategy.

This role is ideal for e-commerce analytics managers, CRM strategists, and D2C founders who want to move beyond revenue dashboards and understand the true economic value of their customer relationships over time.