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?”
A **Web Analyst** is the role responsible for turning digital behavior data into decisions that improve website performance, campaign outcomes, and customer experience. In **Conversion & Measurement**, the Web Analyst is the person who ensures you can reliably answer questions like: *Which channels drive qualified traffic? Where do users drop off? What changes increase conversions?* Within **Analytics**, they design measurement approaches, validate data quality, interpret performance, and communicate insights in a way that teams can act on.
An **Analytics Workflow** is the repeatable, end-to-end way an organization turns raw data into decisions and actions. In **Conversion & Measurement**, it connects what you’re trying to grow (leads, purchases, retention) to how you track it, interpret it, and improve it. In **Analytics**, it provides the structure that makes insights reliable rather than accidental.
Modern marketing decisions are only as good as the measurement behind them. An **Analytics Testing Framework** is the structured way teams validate that tracking, attribution signals, and reporting logic are accurate before they trust insights or optimize spend. In **Conversion & Measurement**, it acts like quality assurance for the entire measurement stack—ensuring the numbers you act on reflect real user behavior, not tracking gaps, duplicates, or broken tags.
An **Analytics Template** is a reusable, documented blueprint for how you collect, organize, analyze, and report marketing and product data. In **Conversion & Measurement**, it acts as the “standard operating procedure” that turns messy events, channels, and KPIs into consistent decision-making.
Modern marketing teams don’t just measure performance—they measure performance for specific groups of people. **Analytics Target Audience** is the practice of defining, analyzing, and using a clearly described audience segment inside your measurement stack so your reporting, optimization, and decisions reflect who is actually driving outcomes.
Analytics Strategy is the blueprint for how an organization uses data to understand performance, improve outcomes, and make decisions with confidence. In **Conversion & Measurement**, it connects what you’re trying to achieve (leads, sales, retention, pipeline) to what you track, how you interpret it, and what actions you take next.
Modern marketing teams don’t just invest in campaigns—they invest in the ability to *prove* which efforts work, why they work, and what to do next. **Analytics Spend** is the budget you allocate to that capability: the people, processes, and technology that turn customer and campaign data into decisions.
An **Analytics Scorecard** is a structured, repeatable way to track performance against the metrics that matter most—across channels, campaigns, and the full customer journey. In **Conversion & Measurement**, it turns scattered numbers into a clear view of what’s working, what’s not, and what to do next. Rather than “reporting for reporting’s sake,” an Analytics Scorecard connects day-to-day marketing activity to business outcomes like leads, revenue, retention, and efficiency.
Analytics ROI is the measurable return a business gets from investing in Analytics—tools, tracking, people, processes, and experimentation—within a broader Conversion & Measurement strategy. It answers a deceptively hard question: “Are we getting enough business impact from our measurement and analysis efforts to justify their cost?”
Analytics ROAS is the practice of measuring and improving return on ad spend using your analytics and measurement stack—not just the numbers shown inside an ad platform. In **Conversion & Measurement**, it acts as the bridge between advertising cost and real business outcomes like purchases, subscriptions, qualified leads, and downstream revenue. In **Analytics**, it forces clarity: what revenue is truly attributable to paid media, how confident you are in that attribution, and what you should do next to scale profitably.