Adobe Target Business Practitioner Expert key terms

The top-level container in Adobe Target that holds a test or personalization experience, its audience rules, success metrics, and schedule. Everything a marketer builds and reports on lives inside an activity.

A specific variation of content shown to visitors inside an activity, such as a different hero image or offer. Each experience is paired with traffic allocation and reporting data.

A rule-based group of visitors defined by attributes such as geography, device, browser, or custom profile parameters. Audiences decide who is eligible to enter an activity or see a specific experience.

The goal used to judge whether an activity worked, such as conversion rate, revenue per visitor, or click-through. The primary metric drives the statistical decision in a test.

An activity that splits traffic randomly among two or more experiences and compares them on a success metric. It measures which variation performs better rather than predicting individual preferences.

An activity that tests multiple elements on a page simultaneously, each with its own variations, to find the best combination. It also reports which individual elements most influence performance.

A Target activity type that uses machine learning to choose, per visitor, the experience most likely to drive the success metric. It blends testing and personalization and adapts as data accumulates.

An activity that combines offers into personalized experiences and uses machine learning to decide which offer combination each visitor sees. It optimizes both the offers and the algorithm over time.

An activity that delivers specific content to defined audiences based on rules rather than random splitting. It is used for geo-targeting, segment-specific messaging, and rule-based personalization.

A Target capability that suggests products, content, or articles to visitors using algorithms based on behavior, popularity, or item attributes. Recommendations appear as activities with their own design and criteria.

The algorithm and settings that determine which items a Recommendations activity surfaces, such as recently viewed or top sellers. Criteria also include item-count and backup behavior options.

The feed of products or content that Recommendations uses to generate suggestions, typically uploaded via CSV or a product feed. Item attributes in the catalog drive filtering and algorithm behavior.

A piece of content, such as an image, HTML block, or JSON payload, that Target can deliver to a page. Offers are reused across activities and are central to Automated Personalization and Recommendations.

Adobe's data management platform that builds and shares audience segments with Target. Segments created there can be used as Target audiences when the two solutions are integrated.

Adobe's JavaScript library that loads Target on a web page, requests offers, and applies them to the DOM. It replaced the older mbox.js library and supports flicker mitigation and single-page apps.

Adobe's unified client-side library that sends data to Experience Platform and Target, replacing at.js in modern implementations. It handles identity, personalization, and analytics through one tag.

The Target endpoint that receives a request and returns offers for a page location. The term also refers to the named location on a page where content can be swapped.

A brief flash of default page content before Target applies personalized offers. Flicker mitigation hides the page or element until Target responds, improving perceived performance.

The stored set of attributes Target knows about a visitor, such as browser, geography, and custom parameters. Profiles enable targeting and are updated as the visitor interacts with the site.

Server-side JavaScript that computes or updates a visitor profile attribute, such as a loyalty tier or visit count. Scripts run when a visitor qualifies and can be used in audience rules.

The percentage of eligible visitors assigned to each experience in an activity. Allocation controls how quickly data accumulates and affects statistical power.

The probability that an observed difference between experiences is real rather than random chance, usually expressed as a confidence level. Target reports this to help decide whether to declare a winner.

The reported likelihood that a test result is not due to chance, commonly shown as a percentage. Higher confidence supports stronger conclusions but does not guarantee business impact.

The percentage difference in a success metric between an experience and the control, such as a 12% lift in conversion rate. Lift is the headline number used to judge activity impact.

The baseline experience in a test, usually the current page or default content, against which other experiences are compared. Lift and significance are measured relative to the control.

The number of visitors or conversions needed to detect a meaningful difference with confidence. Planning sample size before launch prevents inconclusive tests and premature decisions.

A Target setting that lets practitioners preview activities without affecting reporting or live traffic. It is used to validate offers, audiences, and tracking before launch.

A way to view an activity as a specific visitor would, often using URL parameters or the Visual Experience Composer. Preview confirms that content and targeting render correctly.

The point-and-click editor used to create and modify experiences directly on a rendered page. It supports inserting, replacing, and rearranging content without writing code.

A non-visual editor for building activities when the VEC cannot access the page, such as in email or API scenarios. It uses mboxes and offers configured manually.

Target's role-based access control that assigns users to roles such as Approver, Editor, or Observer. Permissions limit who can create, edit, approve, or view activities and settings.

A logical partition in Target that separates activities, audiences, and assets by team or brand. Workspaces support governance and prevent cross-team interference.

The technical setup that connects a site or app to Target, including the library, mbox locations, and data parameters. A correct implementation is required for targeting and reporting to work.

A property of a catalog item, such as category or price, used by Recommendations to filter and rank suggestions. Attributes must be included in the catalog feed to be usable.

A rule that prevents certain visitors from entering an activity, such as customers who already purchased. Exclusions protect test integrity and avoid measuring already-converted users.

The share of eligible traffic assigned to an activity or experience, distinct from the audience rule itself. Allocation determines exposure and data volume.

The system that records and displays activity results, either Target or Adobe Analytics when A4T is configured. Choosing the source affects which metrics and segments are available.

An integration that uses Adobe Analytics as the reporting source for Target activities. It lets teams analyze test results alongside other Analytics metrics and segments.