Last Non-direct Click is a commonly used concept in Conversion & Measurement that assigns a conversion’s credit to the most recent *non-direct* marketing interaction before the conversion happens. In other words, if a person visits your site directly right before buying (by typing the URL, using a bookmark, or clicking an untagged link), Last Non-direct Click ignores that final direct visit and credits the last identifiable channel such as organic search, paid search, email, referral, or social.

Incrementality Measurement is the discipline of quantifying what marketing *actually causes*—the conversions, revenue, and behaviors that would **not** have happened without a specific campaign, channel, or tactic. In modern **Conversion & Measurement**, it answers the hardest question: “Did this marketing activity create new value, or did it just claim credit for demand that already existed?”

Incremental Revenue is the portion of revenue you can credibly say happened *because of* a marketing action—not merely alongside it. In modern Conversion & Measurement programs, it’s the difference between reporting what got “credit” and proving what actually *caused* additional sales.

Incremental Conversions are the conversions that happen *because* of a marketing action—not just alongside it. In modern **Conversion & Measurement**, this concept is crucial because many conversions would have occurred anyway through organic demand, returning customers, brand loyalty, or other channels. **Attribution** systems can assign credit to touchpoints, but they don’t automatically prove causality. Incremental Conversions aim to answer the harder question: “Did this campaign create *additional* outcomes, or did it merely capture demand that was already there?”

Identity Resolution for Attribution is the discipline of connecting customer identifiers across sessions, devices, channels, and systems so that marketing credit can be assigned to the right touchpoints. In modern Conversion & Measurement, this is the difference between “we think this campaign worked” and “we can prove which interactions drove revenue.”

Marketing performance rarely behaves like a neat spreadsheet. A campaign can lift conversions in places you’re not directly tracking, and a strong brand moment can boost results across multiple channels days or weeks later. That “spillover” is often described as the **Halo Effect**—a real and measurable phenomenon that sits at the center of **Conversion & Measurement** and directly complicates **Attribution**.

Ghost Ads are a measurement technique used to understand what advertising *actually* causes—rather than what advertising merely *correlates with*. In modern **Conversion & Measurement**, where privacy constraints, cross-device behavior, and walled-garden platforms complicate tracking, Ghost Ads help teams estimate incremental impact with less bias than many traditional approaches.

Engaged-view Attribution is a measurement approach that gives credit for conversions to ads or content that a person **actively watched or meaningfully engaged with**, even if they didn’t click right away. In modern **Conversion & Measurement**, this matters because many high-impact touchpoints—especially video—shape intent without producing immediate clicks.

Modern marketing stacks create multiple “echoes” of the same customer action: a purchase might be recorded by an ad platform, your analytics tool, your server-side endpoint, and your CRM—sometimes more than once. **Deduplicated Conversions** is the practice of identifying and removing those duplicate records so performance reporting reflects the *true* number of unique conversion events.

De-duplication is the disciplined practice of ensuring the same customer action (like a purchase, lead, or signup) is counted once—even if multiple systems, tags, or channels report it. In **Conversion & Measurement**, it is one of the most important “data hygiene” concepts because it directly determines whether your numbers reflect reality or inflated activity. In **Attribution**, De-duplication prevents multiple touchpoints from claiming the same conversion as if each created a separate result.

A **Custom Attribution Model** is a tailored way to assign credit for conversions across the marketing touchpoints that influenced them. In **Conversion & Measurement**, it sits at the intersection of data, strategy, and decision-making: you’re defining how performance is judged so budgets, bids, and creative decisions align with how customers actually buy. In **Attribution**, a Custom Attribution Model matters because the “default” model in many analytics tools often reflects convenience—not your business reality.

Cross-device Attribution is the practice of connecting marketing touchpoints that happen on different devices (phone, tablet, desktop, connected TV) to the same person or household, so you can credit the right channels and campaigns for a conversion. In modern Conversion & Measurement, this matters because customers rarely follow a single-device path: they might discover you on mobile, research on a laptop, and purchase later in an app or on desktop.

Cross-channel Cannibalization is what happens when one marketing channel “steals” conversions, credit, or budget from another channel—not because it created incremental demand, but because your tracking, targeting, or bidding caused overlap in who gets reached and who gets counted. In **Conversion & Measurement**, this is a critical concept because it can make performance look better in one place while total business outcomes stay flat.

Cross-browser Attribution is the practice of connecting marketing touchpoints to conversions when a person uses more than one web browser during their journey—such as researching in Safari, clicking an ad in Chrome, and purchasing in Firefox. In **Conversion & Measurement**, it addresses a common source of blind spots: the same user can look like multiple “different people” when browser identifiers don’t carry over.

A **Conversion Path** is the sequence of marketing and product interactions a person goes through before completing a desired action—such as a purchase, demo request, app install, subscription, or qualified lead. In **Conversion & Measurement**, it’s one of the most useful ways to connect activity to outcomes, because it shows *how* people actually arrive at conversions rather than assuming a single cause.

Click-through Attribution is a measurement approach that assigns credit for a conversion to marketing touchpoints a person *clicked* on before converting. In Conversion & Measurement, it’s a foundational way to connect spend and effort (ads, emails, affiliates, social posts, search listings) to outcomes like purchases, leads, or sign-ups. In Attribution work, click-based credit is often the default starting point because clicks are observable signals of intent and are easier to track than “view-only” exposure.

Clean Room Attribution is a privacy-forward way to measure marketing impact when user-level tracking is limited. In today’s **Conversion & Measurement** environment—shaped by consent rules, platform restrictions, and rising privacy expectations—teams still need credible **Attribution** to understand which channels and campaigns drive outcomes. Clean Room Attribution addresses that tension by enabling analysis inside controlled “clean room” environments where data can be matched and modeled with strict safeguards and aggregated outputs.

Causal Impact is the discipline of estimating what *actually changed* because of a marketing action—separating true incremental lift from changes that would have happened anyway. In **Conversion & Measurement**, it answers the question every team eventually faces: “Did this campaign cause more conversions, or did we just observe them?” In **Attribution**, it provides the missing ingredient that correlation-based reporting often can’t: a credible counterfactual, or “what would have happened without the marketing.”

Phone calls are still one of the highest-intent actions a customer can take—especially in industries like home services, healthcare, real estate, automotive, and B2B. Yet many teams struggle to measure which marketing efforts actually drove those calls. **Call Attribution** solves that gap by connecting phone leads to the campaigns, channels, keywords, and touchpoints that influenced them.

Attribution is the discipline of assigning credit for a conversion (or other meaningful outcome) to the marketing touchpoints that influenced it. In **Conversion & Measurement**, Attribution is the bridge between “what happened” (a sale, signup, lead, retention event) and “why it happened” (which channels, campaigns, creatives, and messages contributed).

Attributed Pipeline is the portion of sales pipeline (often expressed in currency) that your organization assigns to specific marketing activities, channels, campaigns, or touchpoints based on defined Attribution rules. In **Conversion & Measurement**, it answers a critical question that sits between “What happened?” and “What should we do next?”: *Which marketing efforts are contributing to opportunities being created and progressing through the funnel?*

Assisted Conversions describe the conversions that happen after a user interacts with one or more marketing touchpoints that *helped* the eventual purchase, lead, signup, or other goal—without necessarily being the final click or last interaction. In modern **Conversion & Measurement**, this concept is essential because most customer journeys are multi-step and multi-channel, and last-click views can undervalue the marketing that creates demand earlier in the path.

Amazon Marketing Cloud Analysis is the practice of using privacy-safe, query-based analysis inside Amazon’s clean room environment to understand how advertising exposures relate to downstream outcomes. In modern **Conversion & Measurement**, it fills a crucial gap between surface-level campaign reporting and true customer-journey understanding—especially when teams need better **Attribution** across multiple Amazon ad products and touchpoints.

Algorithmic Attribution is a data-driven approach to assigning credit for conversions across the marketing touchpoints a customer encounters before they buy, sign up, or take another valuable action. In the context of **Conversion & Measurement**, it helps teams move beyond simplistic assumptions and measure how channels, campaigns, and messages contribute to real business outcomes. Within **Attribution**, it represents a shift from fixed “rule” models (like last-click) to models that learn from observed behavior and adapt as marketing changes.

Marketing Mix Modeling (MMM) is a measurement approach that helps businesses understand how different marketing activities—like paid search, TV, promotions, pricing, and seasonality—contribute to outcomes such as revenue, leads, or subscriptions. In **Conversion & Measurement**, it’s one of the most important methods for connecting marketing inputs to business results when user-level tracking is incomplete or biased.

Last-Touch Attribution is one of the most common approaches to Attribution in digital marketing because it’s simple: it gives 100% of the credit for a conversion to the final marketing touchpoint a customer interacted with before converting. In Conversion & Measurement, that simplicity can be a strength—especially when you need fast, consistent reporting—but it can also hide the earlier interactions that created demand.

First-Touch Attribution (FTA) is an Attribution approach used in Conversion & Measurement to credit the very first marketing interaction that introduced a person to your brand before they eventually convert. In other words, it answers: “What brought this customer into our world in the first place?”