Organization. The controlling entity that links projects, users, and a plan together. Each organization has a single Intempt plan associated with it, which determines the data volume limits and features available across all projects in the organization.

Project . A container for your data, including saved entities like sources, events, segments, and others. A single organization can contain multiple projects, and each project’s data tallies are summed together to give the organization-level usage.

User. Any visitor who interacts with your mobile app, website, and other platforms.

Account. A group of users that belong to a single entity, such as a company. Accounts need a common identifier that groups users, e.g., a domain name.

Event. Any action associated with a user (coming from an event collection) created via Intempt event builder as a group of conditions and filters.

Event attribute . A property of a particular event. The values it contains are current for the moment the event was triggered.

User attribute. Data about the user, such as name, email, etc.

Account attribute . Data about the group of users (like companies), such as company name, domain, etc.

Calculated attribute. User attribute whose value is calculated based on one of the scoring models' algorithms (Fit & Activity, RFM, Likelihood, Next best action models).

Source. A website, server library, mobile SDK, or cloud application which can send data into Intempt.

Catalog. A list of products synced from your inventory that you can use for advanced filtering and personalizing your messages.

Segment. A group of users or accounts whose actions or properties match a set of criteria you have defined. Once a user or an account is in a segment, you can target them with a journey or create a report to analyze them.

Destination. Downstream integration that allows you to send your data to external tools or message customers (e.g., Sendgrid for sending emails).

List.A group of users or accounts that can be filtered and grouped in a customized table view.
Report. A basic unit of performing analysis in Intempt.

Dashboard. An analytics board that allows you to view all your most important metrics at a glance.

Journey. A set of conditions created via workflow builder to engage your target customers.

Messaging templates. SMS or email templates you can reuse in the Journey builder to engage your customers.

Fit & Activity models. Set of rules with scoring conditions attached to evaluate your users and accounts based on fit & activity criteria.

RFM models. A scoring model that evaluates users' recency, frequency, and monetary behavior towards a conversion event.

Likelihood model. An AI-based scoring algorithm that creates a likelihood prediction if the user or account will reach a specific goal (e.g., purchase or churn).

Next best action model.An AI-based scoring algorithm that predict the optimal action you should perform/display to increase the likelihood of the user performing the conversion event.

Personalizations. Real-time visual changes on your website or application based on specified targeting conditions.

Experiences. A set of website changes applied to a selected audience within the personalization.

Experiments. An A/B testing tool that allows you to test hypotheses (e.g., "Promotion banner will increase the conversion rate by 20%) via dynamic website element changes rendered on the client or server-side.

Variant. A test group in an experiment. A variant denotes a particular treatment option (like banner change) being tested on a group of users that are selected by specified traffic allocation.

Confidence interval. A range of values that is likely to contain the true value of an estimated parameter.

Statistical significance. A measure of the probability that the observed difference between groups is due to a real effect rather than random chance.

CUPED. A technique that leverages user information from before an experiment to reduce the variance and increase confidence in experimental metrics. This can help to determine experiments that have a meaningful pre-exposure bias (e.g., the groups were randomly different before any treatment was applied)