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A referral leaderboard should award credit for a verified outcome, not for a page view or link opening. A click can show reach; it cannot prove that a person signed up, bought something, or otherwise met the program’s goal. The defensible approach is to define two separate events—one that connects a person to a referrer and another that qualifies the referral—then specify precedence, eligibility, deduplication, and audit rules.
What counts as a referral?
Keep three events distinct: a link interaction, attribution, and qualification. A link interaction indicates that someone reached or opened a referral destination. Attribution binds that person to a referrer. Qualification is the business outcome that earns leaderboard credit.
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For example, a program might attribute a person when they submit a form on a referral page, then qualify the referral only after a verified purchase. Referral Factory documents link-page form submission and Stripe promo-code redemption as possible attribution routes, while treating qualification as a separate setup choice (Referral Factory’s attribution documentation, updated September 29, 2026). Those are implementation examples, not universal rules.
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Define the credit rules before ranking people
A leaderboard is only as meaningful as its rules. Publish the rules in terms of events that can be observed and checked, rather than relying on a vague label such as “successful referral.”
1. Choose the attribution event
Specify the action that associates a referred person with a referrer: for instance, submitting a signup form after arriving through a referral link, or using a referral code during signup. Record the referrer identifier and the referred person’s identifier at that point.
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2. Choose the qualification event
Define the outcome that earns credit—such as a completed, verified purchase or another business event—and state whether reversals, cancellations, or invalid signups disqualify it. A recorded signup may establish who referred someone without yet earning a leaderboard point.
3. Set precedence when there are multiple referrers
Decide what happens if a person clicks more than one referral link or enters a different code later. Possible policies include first touch, last touch before signup, or a persistent mapping that cannot be replaced. There is no universal default: Invitebase documents last-click precedence before signup and one referral per user per campaign after attribution; WooCommerce documents a cookie-based last-click rule, with lifetime mapping taking precedence when enabled (Invitebase attribution documentation; WooCommerce referral-tracking documentation).
4. Set eligibility and an attribution window
State whether the referred person must be new to the service, what makes a referral valid, and how long after a click or code use the qualifying event may occur. The window is a policy choice, not a standard. WooCommerce’s documented plugin uses a configurable cookie lifetime and defaults to 30 days when the field is left empty; that is specific to that plugin’s setup, not a general referral-program rule (WooCommerce referral-tracking documentation).
5. Decide how many credits a conversion can earn
For a simple leaderboard, one qualified person can map to one referrer for a given campaign. Multi-touch models can distribute credit across several interactions instead, but that changes what a leaderboard rank means. Shopify’s marketing-report documentation describes first-click, last-click, last non-direct click, any-click, and linear attribution; these models differ in which touchpoints receive credit and whether credit is limited to one touchpoint (Shopify marketing reports).
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6. Deduplicate qualification events
Use a stable referral or conversion key so a retry, webhook redelivery, or repeated completion-page load cannot award the same credit twice. Google’s Privacy Sandbox documentation describes ignoring reports already received to prevent duplicate conversions (Google Privacy Sandbox: Prevent duplication in reports). The practical principle is the same: process a qualifying event idempotently, so processing it again has no additional effect.
Keep enough evidence to explain a leaderboard entry
For each credited referral, retain the minimum event trail needed to investigate a question: the referrer and referred-person identifiers, attribution event and timestamp, qualification event and timestamp, campaign, applied precedence rule, and a unique conversion key. Do not confuse raw events with modeled reporting totals; Attribution’s documentation distinguishes raw events from modeled totals and notes that some conversions may lack an attributable prior visit (Attribution: Default traffic sources).
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An audit trail should let an administrator answer why a person received credit, whether another referrer had a stronger claim under the published rule, and whether a repeat event was rejected as a duplicate. WooCommerce, for example, documents a traffic view for examining registered visits by an affiliate; the precise fields and retention options depend on the implementation (WooCommerce referral-tracking documentation).
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Plan for tracking failures and ambiguous journeys
Browser-based click tracking is not guaranteed. WooCommerce notes that scripts disabled or blocked by a browser may prevent a click from being recorded. A missing click therefore does not necessarily mean no referral happened; decide in advance whether a code, account-level association, or documented support process can resolve a legitimate case.
Checkout can also move across domains. Google Analytics warns that payment processors may appear as unwanted referral sources, and that cross-domain journeys need appropriate measurement configuration. Its guidance notes that excluding a referral domain can affect attribution (Google Analytics Help: Identify unwanted referrals). Treat an analytics referral source as evidence to inspect, not as automatic proof of who deserves referral credit.
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Write the rule in a way participants can verify
A compact policy should answer these questions without requiring participants to infer the logic from a changing rank:
- What action associates a person with a referrer?
- What exact event earns credit, and what events cancel or reverse it?
- How are multiple links or codes resolved?
- What eligibility requirements and attribution window apply?
- Can a conversion credit one referrer or more than one?
- How are retries and duplicate events handled?
- What evidence can an administrator review when credit is disputed?
Only claim a particular credit model if the implementation actually enforces it. The documentation examples above show that attribution choices vary by product; they do not establish the rules or results of any particular leaderboard.
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