Author: wizbrand

Single-touch Attribution is a measurement approach in **Conversion & Measurement** that assigns 100% of the credit for a conversion to one marketing touchpoint. That “touch” might be the first interaction (first-touch) or the last interaction (last-touch), depending on the model you choose. In the broader discipline of **Attribution**, Single-touch Attribution is often the starting point because it is easy to implement, easy to explain, and produces clear (if simplified) answers.

Reattribution is a core idea in **Conversion & Measurement** that answers a deceptively simple question: *when a known user converts again (or returns after time away), should credit stay with the original marketing touchpoint—or move to a newer one?* Within **Attribution**, Reattribution governs how you update conversion credit when customer journeys don’t follow a single, linear path.

Re-engagement Attribution is the practice of determining which marketing touchpoints, messages, and channels deserve credit for bringing existing users back into an active journey—and ultimately driving conversions after a period of inactivity. In modern **Conversion & Measurement**, this matters because growth is increasingly driven by retention, repeat purchases, renewals, and reactivated users rather than only first-time acquisition.

Privacy expectations, regulations, and platform changes have reshaped how marketers track performance. **Privacy-safe Measurement** is the discipline of measuring marketing impact while reducing the use of identifiable personal data and respecting user choices. In **Conversion & Measurement**, it helps teams answer “what worked?” without relying on fragile identifiers or invasive tracking. In **Attribution**, it supports more trustworthy decision-making by leaning on aggregated, consented, and modeled signals instead of stitching together individual-level journeys at any cost.

Post-view Attribution is a measurement approach that gives credit for a conversion to an ad impression that was *seen* (served and viewable) even if the user did not click the ad. In Conversion & Measurement, it helps teams understand the influence of upper-funnel media—especially display, social, and video—where impact often happens without an immediate click. Within Attribution, it fills a real blind spot: many customers convert later through other channels after being exposed to ads that shape awareness, preference, and intent.

Post-install Attribution is the discipline of connecting what happens *after* a user installs an app—such as registration, purchases, subscriptions, retention, or ad revenue—back to the marketing touchpoints that acquired that user. In modern **Conversion & Measurement**, it closes the gap between “we got an install” and “we grew the business,” making **Attribution** more than a top-of-funnel scoreboard.

Post-click Attribution is the practice of assigning credit for a conversion to the marketing touchpoints a person engaged with **after they clicked** an ad, email, or link and entered your owned experience (site, app, landing page). In **Conversion & Measurement**, it answers a very specific question: *what happened after the click that led to a measurable outcome?* Within **Attribution**, it helps teams understand which campaigns, creatives, audiences, and landing experiences truly drive sign-ups, purchases, demos, or other conversion events.

Path Length is a core concept in **Conversion & Measurement** that describes how many interactions (or “touchpoints”) a customer typically has before they complete a desired action—such as a purchase, demo request, signup, or lead submission. In practice, it helps you understand whether conversions happen quickly or require repeated exposure across channels, content, and campaigns.

Offline Attribution is the practice of connecting marketing touchpoints (often digital) to conversions that happen outside the browser—like in-store purchases, phone orders, signed contracts, or kiosk transactions. In **Conversion & Measurement**, it closes a common gap: marketing activity is measurable, but revenue often materializes in offline systems. Within **Attribution**, Offline Attribution helps teams understand *which channels, campaigns, and messages actually drive business outcomes* when the final conversion doesn’t happen online.

Multi-touch Attribution is a measurement approach that assigns credit for a conversion across multiple marketing interactions instead of giving all credit to a single “last click” or “first click.” In **Conversion & Measurement**, it helps teams understand how channels and campaigns work together across a buyer journey that may include ads, email, SEO content, social, webinars, and sales outreach. In **Attribution**, Multi-touch Attribution is the bridge between what customers *do* (touchpoints) and how the business *decides* (budgeting, targeting, creative, and funnel optimization).

Modeled Lift is an approach to estimating the incremental impact of marketing when you can’t directly observe it with perfect tracking or clean experiments. In **Conversion & Measurement**, it fills the gap between what your analytics can *prove* and what the business still needs to *decide*—budget allocation, channel strategy, and campaign optimization.

Media Mix Modeling is a statistical approach that helps marketers understand how different marketing activities (and external factors) contribute to business outcomes like sales, leads, or revenue. In **Conversion & Measurement**, it’s a way to connect spend and exposure across channels to real results—especially when user-level tracking is limited.

A **Matched Market Test** is one of the most reliable ways to answer a hard marketing question: *did this campaign truly cause incremental results, or would the business have achieved similar outcomes anyway?* In modern **Conversion & Measurement**, that question sits at the center of budget decisions, channel strategy, and performance reporting—especially as tracking becomes noisier and user-level signals become less accessible.

Marketing teams generate demand across many touchpoints—ads, email, social, SEO, partners, and sales outreach. **Marketing Attribution** is the discipline of assigning credit for conversions and revenue to those touchpoints so you can understand what actually influences outcomes. In the context of **Conversion & Measurement**, it connects customer journeys to business results using consistent rules, data, and analysis.

Linear Attribution is a conversion measurement approach that distributes equal credit to every known marketing touchpoint in a customer’s path to conversion. In the context of Conversion & Measurement, it offers a straightforward way to acknowledge that most purchases are influenced by multiple interactions, not just the first click or the last click. Within Attribution, it’s often used as a “baseline” model because it’s easy to explain and implement while still being more balanced than single-touch methods.

A **Lift Study** is one of the most reliable ways to answer a question that matters in every growth team: *Did this marketing activity cause incremental results, or would they have happened anyway?* In **Conversion & Measurement**, that “incrementality” question is critical because modern tracking is fragmented across devices, platforms, and privacy constraints. A Lift Study helps separate correlation from causation.

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.