Attribution Audit is the disciplined process of checking whether your marketing **Attribution** setup is producing trustworthy, decision-ready insights—based on how users actually discover, engage, and convert. In **Conversion & Measurement**, it acts as quality control for the entire chain of data collection, identity resolution, channel tracking, and reporting that informs budget allocation.
Attribution Attribution is the practice of evaluating, validating, and governing your marketing attribution approach so the credit you assign to channels and touchpoints is trustworthy and actionable. In other words, it’s “measuring the measurement”: checking whether your attribution method, data, and assumptions are producing decisions you can safely use in **Conversion & Measurement**.
Attribution Assisted Conversions is a measurement concept that shows which marketing interactions *helped* lead to a conversion—even when they weren’t the final touchpoint. In modern Conversion & Measurement, this is essential because real customer journeys are rarely linear: people discover a brand through content, compare options through retargeting, and convert later through branded search or email.
Attribution Analysis is the discipline of figuring out which marketing efforts meaningfully contribute to a conversion—such as a purchase, demo request, subscription, or qualified lead—so teams can invest with confidence. In modern **Conversion & Measurement**, it’s the bridge between “what happened” (a conversion) and “why it happened” (the touchpoints, channels, and messages that influenced it). Within **Attribution**, it provides the logic for assigning credit across a customer journey that rarely follows a straight line.
Weighted Pipeline Influence is a way to quantify how marketing and sales touchpoints contribute to pipeline creation by assigning a proportional “influence weight” to each touch. In **Conversion & Measurement**, it helps teams move beyond binary thinking (“this campaign sourced the deal” vs. “it didn’t”) and instead measure *how much* a channel, campaign, or interaction shaped pipeline outcomes. It’s especially valuable in **Attribution**, where the goal is to distribute credit across multiple touches rather than over-crediting only the first or last interaction.
W-shaped Attribution is a multi-touch **Attribution** model used in **Conversion & Measurement** to assign more credit to the interactions that typically matter most in a customer journey—not just the first click or the last click. It’s designed for teams that want to understand which marketing efforts create demand, which efforts convert that demand into a lead or opportunity, and which efforts ultimately drive the final conversion.
View-through Attribution is a measurement approach that gives some level of credit for a conversion to an ad impression that was **seen** (viewed) but **not clicked**, when the conversion happens later within a defined time window. In modern Conversion & Measurement programs, this matters because many campaigns—especially display, video, connected TV, and paid social—are designed to influence intent and preference without always generating immediate clicks.
U-shaped Attribution is a position-based Attribution model used in Conversion & Measurement to assign more credit to the touchpoints that start and “activate” a customer journey. Instead of giving all value to the final click or spreading value evenly, U-shaped Attribution typically emphasizes the first interaction (the discovery moment) and the moment a prospect becomes a known lead (the conversion moment), while still assigning some credit to the steps in between.
Test vs Control is one of the most reliable ways to answer a deceptively simple marketing question: “Did this change actually cause the improvement?” In **Conversion & Measurement**, it’s the backbone of credible experimentation—separating true incremental impact from normal fluctuations, seasonality, channel mix shifts, or biased reporting. In **Attribution**, Test vs Control provides the “ground truth” needed to validate whether a channel, campaign, or tactic truly created additional conversions or merely captured demand that would have happened anyway.
Synthetic Control is a causal measurement method that helps marketers answer a hard question in **Conversion & Measurement**: *What would have happened if we hadn’t run this campaign, changed this landing page, or launched this channel?* In real-world marketing, true randomized experiments aren’t always possible due to budget, operational constraints, or platform limitations. Synthetic Control provides a disciplined way to estimate the “counterfactual” outcome—often the missing piece behind credible **Attribution**.
Digital marketing doesn’t stop at the checkout page. For retailers, restaurants, automotive, healthcare, and any business with physical locations, a large share of revenue happens offline—after someone sees an ad, clicks a listing, or taps for directions. **Store Visit Attribution** is the set of methods used in **Conversion & Measurement** to connect digital marketing touchpoints to real-world store visits so teams can evaluate performance, optimize spend, and improve customer journeys.
The **Spillover Effect** is one of the most important “hidden forces” in modern digital marketing performance. In **Conversion & Measurement**, it describes how one marketing activity drives results **outside** the channel, campaign, audience, or time window you expected—often improving (or sometimes harming) conversions that get credited somewhere else. If you’ve ever paused a paid campaign and noticed organic traffic drop, or launched TV/creator campaigns and saw direct traffic surge, you’ve seen the Spillover Effect in action.
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?”