More clarity. Step by step.

Adstock, also known as the carryover effect, describes the phenomenon where advertising impact is not limited to the day of exposure but has a delayed effect that fades over time. In Marketing Mix Modeling, this lingering effect is modeled to ensure the true impact of a channel is not underestimated.

AI matching uses artificial intelligence to consolidate individual, fragmented signals into a seamless customer journey. This is crucial because no single tracking method is complete, and device-specific identifiers like fingerprints only ever relate to a single device.

App Tracking Transparency (ATT) is Apple's policy requiring active user consent for cross-app tracking on iOS since 2021. A large portion of users opt out, which makes platform-based attribution on iOS incomplete.

An attribution model is the rule used to assign value to the various touchpoints in a conversion path. Common models include last-click, first-click, linear, time-decay, position-based, and data-driven models—each of which weights touchpoints differently.

The attribution window (lookback window) is the period during which a conversion is credited to a preceding click or view. Different windows are a primary reason why platforms report varying conversion numbers.

The Average Order Value (AOV) is the average revenue generated per order over a given period. A higher order value allows you to recoup acquisition costs more quickly, making it a key lever for profitability.

Behavior-based AI attribution evaluates every customer journey based on actual user behavior and measures the true causal contribution—the incrementality—of each touchpoint, rather than simply distributing learned correlation weights. This is Tracify's approach and a distinct category compared to data-driven attribution.

The break-even ROAS is the ROAS value at which a campaign becomes cost-effective; it is the reciprocal of the contribution margin. It must be calculated based on the contribution margin rather than the gross margin, otherwise a campaign will appear more profitable than it actually is.

CAC (Customer Acquisition Cost) refers to the cost of acquiring a new customer. The denominator is new customers, not orders. A reliable CAC calculation includes not only the advertising budget but also proportional personnel, tool, and agency costs.

The cart abandonment rate is the percentage of created shopping carts that do not result in a purchase. Across many studies, the average is around 70 percent, and it is higher on mobile devices than on desktop.

Client-side tracking means that an event is sent directly from the user's browser to the platform, usually via a pixel. It is easy to implement, but it is susceptible to JavaScript errors and, as a recognizable third-party call, vulnerable to ad and cookie blockers.

Customer Lifetime Value (CLV/LTV) is the contribution margin generated by a customer over the entire course of the business relationship. A margin-based calculation is crucial; a revenue-based LTV overestimates customer value and cannot be compared with CAC.

Consent-free tracking refers to methods that do not require active user consent because they do not use device access or personal data for marketing purposes that would otherwise necessitate such consent. This is a narrow, legally sensitive configuration and not an automatic side effect of server-side tracking.

Consent Mode v2 transmits the user's consent status to Google tags via four signals. When consent is denied, cookieless pings are sent in Advanced mode, which Google uses to model conversions—however, these estimates are no substitute for a complete data set.

Contribution margin is the amount remaining after variable costs are deducted to cover fixed costs and generate profit. In e-commerce, it is calculated in stages (CM1 after cost of goods sold, CM2 after order costs, CM3 after marketing) – these stages are not standardized.

The conversion rate (CVR) is the percentage of sessions that result in an order. The denominator—whether sessions or unique users—must be consistent; otherwise, the values cannot be compared.

The Conversions API (CAPI) transmits events from server to server to an advertising platform, in parallel with the browser pixel. It recovers events that are lost on the client side, but it does not replace the pixel and requires clean deduplication.

Conversion tracking is the technical process of capturing a valuable user action—such as a purchase, lead, or sign-up—transmitting it to a measurement or advertising system, and attributing it to a specific source. It serves as the data foundation for all attribution and budget-related decisions.

Cookieless tracking refers to methods that capture user actions without traditional cookies, such as server-side first-party tracking, hashed first-party data, or aggregated models. It is a response to browser restrictions and the short lifespans of cookies.

The CPO (Cost per Order) represents the marketing costs per generated order, regardless of whether the customer is new or returning. For businesses with many repeat customers, the CPO is significantly lower than the CAC; confusing the two leads to an underestimation of acquisition costs.

The customer journey is the sequence of all touchpoints a user goes through from initial awareness to purchase and beyond. Mapping this journey is essential for accurately evaluating the contribution of individual channels.

Data-Driven Attribution (DDA) distributes conversion values based on weights learned from aggregated conversion paths—this is how the standard models of major advertising and analytics platforms work. It learns correlations, but it does not measure the causal incrementality of individual touchpoints.

Deduplication prevents the same event from being counted twice when using both the pixel and the Conversions API. Meta deduplicates based on event_id and event_name, keeping the event that arrives first. If the event_id is missing, the conversion will be counted twice.

Google Enhanced Conversions supplement existing conversion tags with hashed first-party data, such as email addresses, which are matched against logged-in Google accounts. This allows for the recovery of conversions that would otherwise be lost due to failed cookie matching.

Meta's Event Match Quality (EMQ) measures how effectively a transmitted event can be attributed to a specific person, ranging from Poor to Great. It increases with the number of hashed customer parameters provided. An event that cannot be matched will not result in an attributed conversion.

Fingerprinting identifies a device based on a combination of technical characteristics such as browser, operating system, and resolution, without setting a cookie. A fingerprint is always unique to a single device – without effective matching, cross-device journeys remain incomplete.

A first-party cookie is set by the website owner's own domain and is used to recognize the user on that site. Because it is not identified as a third-party tracker, it is less restricted by browsers and ad blockers than a third-party cookie.

A geo-lift test is an incrementality experiment where ads are shown in specific regions and withheld in comparable ones. The difference allows you to determine the causal contribution of a channel—even without granular user tracking.

Hybrid tracking combines multiple data collection methods to capture as many customer journey touchpoints as possible, both client-side and server-side. According to Tracify, their patented, consent-free hybrid tracking achieves a tracking rate of nearly 100 percent over 30 days.

Incrementality measures the additional revenue or conversions truly generated by a marketing campaign—in other words, results that would not have occurred without that specific initiative. It distinguishes genuine causal contribution from mere correlation.

Incrementality testing measures the causal effect of a marketing campaign by comparing a test group exposed to ads against a comparable control group that is not. It is considered the gold standard for proving true incrementality.

Intelligent Tracking Prevention (ITP) is Apple's tracking protection in Safari, which, among other things, limits client-side cookies to seven days and blocks third-party cookies. Conversions that occur after this window often appear as direct or unattributed.

Last-click attribution assigns the entire conversion to the final touchpoint before a purchase. While simple, it overvalues bottom-of-funnel channels and ignores all the preceding touchpoints in the customer journey.

The LTV:CAC ratio compares customer lifetime value to acquisition costs and measures the viability of your business model. A ratio of around 3:1 is generally considered healthy, while anything below 1:1 means you are losing money on every new customer.

Marketing attribution assigns the contribution of preceding touchpoints to a conversion and answers the question of which channel actually drove a sale. It is the weighting of captured tracking data – without complete data, it only provides an incomplete picture.

Marketing Mix Modeling (MMM) is a statistical method that estimates the revenue contribution of each marketing channel using aggregated historical data—without the need for cookies or personal data. It optimizes the allocation of a given budget and is resilient to the loss of tracking signals, though it is slower and operates at an aggregate level.

The MER (Marketing Efficiency Ratio), also known as Blended ROAS, measures total revenue against total marketing costs across all channels, without attribution. While it is robust against measurement errors, it does not indicate which specific channel is driving performance.

Multi-touch attribution (MTA) distributes the value of a conversion across multiple touchpoints in the customer journey rather than assigning it to just one. It is user-based and therefore relies on trackable, digital touchpoints.

ROAS (Return on Ad Spend) measures the revenue generated for every euro spent on advertising. It is typically calculated at the channel or campaign level and is attribution-dependent. Whether a ROAS is profitable depends on the contribution margin, not the value alone.

Section 25 of the TDDDG stipulates that in Germany, storing and accessing information on an end-user's device—such as cookies—requires prior, active consent, unless it is strictly technically necessary. Marketing and analytics tracking do not fall under this category and therefore require consent.

Server-side tracking means that events are sent to the platform API from your own server rather than from the browser. When combined with a first-party integration, it is less likely to be blocked and is more resilient against short-lived cookies—though it does not replace the need for user consent.

A third-party cookie is set via a third-party domain and is identifiable as a third-party tracker. Safari and Firefox block them by default, but they continue to function in Chrome – the long-heralded end of the cookie has yet to arrive.

A touchpoint is any interaction a user has with a brand along the path to purchase—such as an ad, a click, opening an email, or visiting a website. A touchpoint that is not tracked cannot be attributed to a journey.