Category: Analytics

Average Engagement Time is a modern way to quantify how much *active attention* people give your website or app. In **Conversion & Measurement**, it helps answer a question that basic traffic metrics can’t: *Are visitors actually interacting with your experience, or just passing through?* In **Analytics**, Average Engagement Time sits between surface-level volume (sessions, page views) and outcomes (leads, purchases), making it a valuable diagnostic metric for content quality, UX, and funnel performance.

Audiences are how modern marketing turns data into action. In **Conversion & Measurement**, the term **Audiences** refers to defined groups of people (or devices/accounts) created from data signals—such as behaviors, attributes, or intent—so you can measure performance accurately and activate tailored experiences across channels.

An **Audience Trigger Event** is a measurable signal—behavioral, transactional, or contextual—that indicates a person now meets the criteria for a specific audience and should enter (or exit) an experience such as a campaign, journey, personalization rule, or sales sequence. In **Conversion & Measurement**, it functions as the “moment of truth” where audience definition becomes action: you stop treating segmentation as a static list and start treating it as a dynamic system tied to outcomes. In **Analytics**, it becomes a trackable milestone that can be validated, audited, and optimized over time.

An **Audience Trigger** is a defined signal—an event, condition, or threshold—that automatically places people into an audience (or moves them between audiences) so marketing, product, or sales actions can happen at the right moment. In **Conversion & Measurement**, an Audience Trigger is the bridge between what users do and what your organization does next: target an ad, send an email, personalize a page, notify sales, or record a milestone for reporting.

Attribution Settings are the rules and configurations that determine **how credit for a conversion is assigned across marketing touchpoints**—such as ads, emails, organic search, referrals, and direct visits. In **Conversion & Measurement**, these settings influence what your team considers “working,” what gets budget, and which channels appear to drive revenue. In **Analytics**, Attribution Settings turn raw interaction data into decisions by defining how conversions are counted, attributed, and reported.

Anomaly Detection is the practice of identifying data patterns that don’t behave as expected—sudden spikes, drops, or unusual relationships between metrics. In **Conversion & Measurement**, it helps teams catch tracking breakages, campaign issues, site problems, fraud, and genuine performance shifts before they distort decisions. In **Analytics**, it’s the guardrail that separates “a real change” from “noise,” especially when you’re monitoring dozens of channels, events, and KPIs at once.

Analytics is the discipline of collecting, organizing, and interpreting data so you can understand performance and make better decisions. In digital marketing, Analytics sits at the center of Conversion & Measurement because it connects what people do (impressions, clicks, visits, sign-ups, purchases) with why it matters (revenue, retention, profitability, and customer experience).

Amplitude is best known in digital marketing and product teams as an event-based product Analytics approach and platform used to understand user behavior across websites, apps, and connected experiences. In the context of **Conversion & Measurement**, Amplitude helps teams move beyond surface-level traffic reporting and into behavior-driven insights: what people do, where they drop off, and what actions predict retention and revenue.

Alerting is the practice of automatically notifying the right people (or systems) when marketing and measurement conditions change—such as a sudden drop in conversions, a tracking outage, or an unusual spike in spend. In **Conversion & Measurement**, Alerting bridges the gap between “data exists” and “someone acts on it,” reducing the time between a problem (or opportunity) and a response.

Modern marketing runs on two forces that often pull in different directions: personalization and privacy. **Ads Personalization Setting** sits right in the middle. It describes the controls—typically at the user, device, account, or platform level—that determine whether and how a person’s data and behavior can be used to tailor advertising.

Adobe Customer Journey Analytics is a customer-centric approach to **Conversion & Measurement** that connects cross-channel behavior into a unified view of how people discover, evaluate, and convert. Instead of treating website, app, CRM, and offline touchpoints as separate reporting silos, it helps teams analyze journeys end-to-end so they can understand what truly influences outcomes.

Adobe Analytics is a digital analytics platform used to collect, process, and interpret customer interaction data across websites, apps, and other digital touchpoints. In the context of **Conversion & Measurement**, it helps teams understand what drives outcomes—leads, purchases, subscriptions, retention—by turning behavioral data into decisions. Within the broader discipline of **Analytics**, it’s often used when organizations need deep segmentation, flexible reporting, and enterprise-grade governance.

Add_shipping_info is a checkout milestone used in **Conversion & Measurement** to track when a shopper submits or selects shipping details (such as shipping address, delivery method, or shipping tier) during an eCommerce journey. In modern **Analytics**, it functions as a high-intent signal that sits between “starting checkout” and “adding payment” or “purchasing,” making it invaluable for diagnosing funnel friction and improving revenue outcomes.

Add_payment_info is a conversion-step signal that indicates a user has reached the point in a checkout or subscription flow where they submit or select payment details. In **Conversion & Measurement**, it represents a critical mid-to-late funnel milestone: the user is no longer just browsing—they are actively preparing to pay. In **Analytics**, Add_payment_info is commonly implemented as an event (or tracked step) so teams can quantify checkout progression, identify friction, and improve revenue outcomes.

Ad-hoc Query is the practice of asking a new, specific question of your data and retrieving an answer on demand—without waiting for a scheduled report or a prebuilt dashboard. In **Conversion & Measurement**, that matters because the most valuable questions are often unexpected: a sudden dip in sign-ups, an unusual spike in checkout errors, or a new audience segment converting far better than average. In **Analytics**, Ad-hoc Query is how teams move from “What happened?” to “Why did it happen?” and “What should we do next?” fast enough to make a difference.

An **Active User** is one of the most important concepts in **Conversion & Measurement** because it connects marketing activity to real product usage, repeat engagement, and long-term value. While clicks and impressions show attention, an Active User shows *behavior*—someone who meaningfully uses your website, app, or platform within a defined time window.

An **Activation Metric** is the measurement that tells you whether a new user, lead, or account has reached the first meaningful moment of value—often the step that separates “interest” from “real usage.” In **Conversion & Measurement**, it’s the bridge between acquisition (getting attention) and retention (keeping customers). In **Analytics**, it becomes the operational definition of “a good start,” enabling teams to optimize onboarding, campaigns, and product experiences with evidence instead of assumptions.

Weekly Active Users (WAU) is one of the most useful “reality check” metrics in **Conversion & Measurement** because it answers a simple question: how many distinct people actually used your product, app, or digital service in the past week? In **Analytics**, WAU sits between daily volatility and monthly lag, making it a powerful way to understand engagement, retention, and momentum without overreacting to single-day spikes.

Objectives and Key Results—often shortened to **OKR**—is a goal-setting framework that connects what you want to achieve (objectives) with how you’ll measure progress (key results). In digital marketing, it becomes especially powerful when applied to **Conversion & Measurement** because it forces clarity: which outcomes matter, how success will be quantified, and how teams will use **Analytics** to make decisions.

Monthly Active Users (MAU) is one of the most widely used growth and engagement metrics in modern **Conversion & Measurement**. It answers a deceptively simple question: “How many unique people actively used our product, website, or app in the last 30 days?” In **Analytics**, MAU becomes a high-level “heartbeat” metric that helps teams track adoption, retention, and the real size of an engaged audience—not just traffic or installs.

Daily Active Users (DAU) is one of the most referenced product and marketing metrics because it answers a simple, high-impact question: how many unique people actively used your product today? In **Conversion & Measurement**, that “today” matters—DAU is sensitive to campaigns, onboarding changes, pricing tests, push notifications, seasonality, outages, and competitive moves. In **Analytics**, DAU becomes a daily health check that connects acquisition, activation, engagement, and retention into a single, trackable signal.

Business Intelligence (BI) is the discipline of turning raw business data into decision-ready insight—then making that insight actionable across teams. In digital marketing, BI becomes most visible in **Conversion & Measurement**, where you’re constantly trying to connect spend and activity to outcomes like leads, purchases, retention, and revenue.

Average Revenue Per User is one of the most practical ways to translate marketing and product performance into business value. In **Conversion & Measurement**, it helps teams move beyond “did we get clicks or signups?” to “did we generate meaningful revenue per customer?” In **Analytics**, it becomes a cornerstone metric for understanding monetization efficiency, segment performance, and the real impact of acquisition and retention strategies.

Average Revenue Per Paying User (ARPPU) is a core metric in **Conversion & Measurement** that tells you, on average, how much revenue each paying customer generates over a defined time period. Unlike broader revenue-per-user metrics that include free users, ARPPU focuses only on customers who actually paid—making it especially useful for freemium products, subscriptions, marketplaces, and any business where “not all users are buyers.”