# Calculating Ecommerce Customer Lifetime Value

When evaluating an ecommerce investment opportunity, I consider seven key variables: Average Order Value (AOV), Cost of Goods Sold (COGS), Fulfillment Costs, Conversion Rate, Lifetime Purchases, Virality, and Return Visits. These variables have multiplicative effects and their relationships to profit and growth can be explained by a straightforward math equation that even my 6th-grade daughter can comprehend. Although the variables and equation may seem simple, the tactics and strategies used to enhance these metrics can be quite challenging.

A data-driven approach, when perfected, can lead to remarkable growth in a relatively short time. Ecommerce business success or failure revolves around unit economics – the amount of revenue and profit generated from a single, average visitor. Minor changes in unit economics can result in significant changes in overall financial statements.

Traditional financial statements offer a high-level view of a company, but a top-down approach has limited actionability. Profit and loss, cash flow statements, and balance sheets are just aggregations of the unit economics of each individual visitor. To truly understand an ecommerce business’s health, it’s crucial to examine the underlying unit economics. This bottom-up approach is far more actionable than traditional top-down financial management.

Though this isn’t a book on math, you’ll need to grasp a simple math equation using the seven variables:

\$\$ / Visitor = (Average Order Value – COGS – Fulfilment Costs) x Conversion Rate x Lifetime Purchases x Virality x Repeat Visits

This simplified ecommerce equation is not intended to be flawlessly accurate but rather directional. Our pay-per-click marketing team employs a more sophisticated version of the equation, managing tens of millions of dollars in ad spend, while our financial models also utilize more precise math. For the purposes of this book, this basic equation will effectively demonstrate the growth strategies.

Despite its elegant simplicity, this framework has served me well over the past two decades. It’s allowed me to make swift, critical operational decisions in the ecommerce businesses I’ve run and evaluate the health of those I’ve invested in.

When it comes to promoting growth, the seven variables are not equal. A ruthless, data-driven focus on the most important, high-leverage variables is crucial. For instance, two operational variables (cost of goods and fulfillment costs) quickly reach their limits. Beyond a certain level of optimization, additional work yields rapidly diminishing returns. Reducing actual shipping costs or the actual cost of goods by more than 10 or 20% is nearly impossible, unless your baseline is abysmal. While operational efficiency is vital, maximizing these variables alone won’t create an ecommerce giant.

Numerous case studies will demonstrate how non-operational variables have the potential for exponential improvement. For example, in Chapter XX, you’ll learn how my team increased a large channel’s conversion rate from 0.1% to 8.0%, an 8,000% improvement that generated nearly a hundred million dollars in additional annual revenue.

Since each variable contributes equally to determining profit per visitor, an 80-fold increase in conversion rate will obviously create more incremental value than negotiating marginally better deals with FedEx or your manufacturer.

These insights into maximizing variables led to the development of Engine, the first ecommerce platform designed to optimize unit economic variables.

By understanding the financial unit economics of a single visitor, you can make informed decisions about marketing spend, particularly ad spend for new customer acquisition. In essence, if you can acquire customers profitably and at scale, your company will grow.

In the customer acquisition chapters, this book will concentrate on key guidelines, specifically the Customer Acquisition Cost (CAC) to Customer Lifetime Value (LTV) ratio and payback period. A customer’s LTV must significantly exceed the CAC, and the faster the CAC is recouped, the better. Generally, your LTV to CAC ratio should be at least 3 to 1, and marketing spend should be recovered within a year or less. Many top ecommerce unicorns boast LTV:CAC ratios of at least 8:1 and payback periods of just one day.

Once you’ve determined a single, average visitor’s value using this equation, the figures can be refined indefinitely. Knowing that an average user is worth, say, 50 cents is informative but not actionable. To obtain actionable data, you must delve deeper into the data. For example, you can compare the relative value of a Facebook visitor versus a Google visitor. More specifically, you can calculate the value of an average visitor for each individual keyword. In my previous business, a visitor who searched for “cowboy boots” was worth 46 cents, while one who searched for “Lucchese cowboy boots” was worth 9 dollars. This data segmentation can be achieved using a method called cohort tracking, which will be discussed in a later chapter.