This chapter explores a traceability strategy designed specifically to capture, log, and relate every action a visitor takes in a WooCommerce store—whether they complete a purchase or abandon their cart.
The solution is based on a dual identification model that clearly distinguishes between:
- the lifecycle of a cart
- and the persistent identity of the visitor
…even when the visitor remains anonymous.
Two Dimensions of User Identity
The architecture hinges on recognizing two distinct entities that must be tracked independently yet relationally:
1. The shopping cart as a unit of intent:
Every time a user begins a purchase process—whether by adding a product for the first time or returning after abandonment—it’s treated as a separate purchase attempt.
Each attempt has:
- a start
- a sequence of actions
- and an end (completed purchase or prolonged abandonment)
This attempt is assigned a unique identifier that lives only during its lifecycle.
Once the purchase is completed or the cart is emptied, this identifier is discarded, and any new interaction generates a new one.
2. The visitor as a persistent entity:
Regardless of how many carts they initiate, the same anonymous user may return days later.
To recognize them, a persistent identifier is assigned via a cookie with a defined duration (e.g., 12 hours).
This identifier:
- does not reset upon purchase
- is not deleted when the browser closes
- remains active while the cookie is valid
This allows multiple purchase attempts to be linked to the same individual.
It enables key business questions:
- How many carts has this visitor initiated recently?
- What products were explored in previous attempts and which were purchased?
- Is there a pattern of abandonment at a specific step?
Event Capture Along the Conversion Funnel
The system integrates non-intrusively at key points in the user journey, leveraging WooCommerce’s native hooks.
Each meaningful interaction is logged as a structured event, tied to both the current cart and the persistent visitor.
The typical flow begins when the user views a product—this is logged as a “view” event, marking the potential start of a new purchase attempt.
If the user adds the product to the cart, a new event captures:
- product ID
- quantity
- cart context
Subsequent actions—changing quantities, removing items, or navigating to the cart—are logged as additional events.
Entering the checkout page marks a key milestone.
If the user begins payment, a “checkout start” event is logged.
If the transaction succeeds, the purchase is recorded and the cart cycle is closed.
Crucially, if the user abandons at any point—even before adding anything—all prior actions are logged and tied to that failed attempt.
Thanks to the persistent visitor ID, if they return later, new events are linked to their previous history, revealing a fuller behavioral pattern.
Behavior-Oriented Data Model
Captured events are stored in a relational structure with two main entities:
- Session headers: Each row represents a unique purchase attempt (i.e., a cart), with metadata like:
- cart ID
- persistent visitor ID
- authentication status (logged in or anonymous)
- device used
By business rule, each new cart—even from the same visitor—generates a new header.
- Interaction events: Each row represents a specific action within a purchase attempt, linked to its header and detailing:
- action type (view, add, remove, etc.)
- product involved
- quantities
- other relevant attributes
This structure supports analysis:
- Micro-level: what a user did in a specific cart
- Macro-level: how many carts an anonymous visitor initiated, their conversion rate, recurring products
Controlled Cart Cycle Reset
A core design aspect is handling the end of a cart cycle.
When a purchase is completed, the system removes the current cart ID.
This ensures any subsequent interaction—even minutes later—is treated as a new purchase attempt, avoiding data contamination between transactions.
This reset also applies to scenarios like manual cart emptying, ensuring consistency in defining what constitutes an “attempt.”
Decision-Making Benefits
With this traceability in place, marketing, product, and UX teams can:
- Identify bottlenecks in the funnel (e.g., high drop-off at shipping step)
- Segment audiences by behavioral history (e.g., users who viewed a product in three different carts but never purchased)
- Measure effectiveness of abandoned cart recovery campaigns
- Personalize recommendations based on the visitor’s full history, not just the current session
> In short, this strategy transforms the shopping cart from a simple product container into a rich behavioral data unit, revealing the user’s intentions, hesitations, and decisions over time—even when they choose to remain anonymous.
