> For the complete documentation index, see [llms.txt](https://documentation.crossengage.io/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://documentation.crossengage.io/predictions-platform/glossary-predictions.md).

# Glossary - Predictions

#### Predictions Platform

CrossEngage's Customer Prediction Platform. The Predictions Platform has the URL: app.crossengage.io

#### Data & Engagement Platform

CrossEngage's Customer Data and Engagement Platform. The Data & Engagement Platform has the URL: [app.crossengage.io](https://app.crossengage.io)

#### Customer

A Customer is defined as any User whose data is present in the Predictions Platform. This includes Users who have not yet made a purchase, or who made a purchase but later returned it.

#### Transaction

A transaction is generally any activity initiated by the User, which involves exchange of products. Purchases and Returns are common examples of transactions.

#### Activity

Activity is defined as any Customer Interaction recorded on the Predictions Platform, that does not generally include a financial transaction. Some examples might be a Customer logging into an Online Store, calling your helpline or receiving a Marketing Newsletter.

#### Data Package

A Data Package is the collection of all Data Files that are used together to create a Model. This helps organize data effectively - every file only needs to be uploaded once, but can be used many times (by adding it to multiple Data Packages).

#### Model

A Model, commonly known as a Machine Learning Model is a complex file that has been trained to recognize certain patterns. CrossEngage use Machine Learning to train these Models to understand Customer behavior, allowing them to Predict Conversion in the future.

#### Conversion / Response

Conversion is defined as the point at which a recipient of a marketing message makes a Purchase.

#### Selection

A Selection is a group of Users on the Predictions Platform that have been bundled together for the purpose of a Campaign. This is similar to a User Segment in the Data & Engagement Platform.

#### CLV

Customer Lifetime Value is the estimated value of a Customer during their entire relationship with a business.

#### IPT

Individual Purchase Interval is an estimate of the time Interval that a Customer takes from one Purchase to the next. IPT is defined Individually - each customer has their own IPT.

#### Scores

In Machine Learning, a Score is a value calculated by the model, that is indicative of the predictive power of the model.

#### Features

In Machine Learning, a feature is an individual measurable property that serves as an input to the Model. It can be a single field, or a combined and transformed form of several fields. 

#### Target Variable

The target variable is the variable that is being predicted by the model.

#### Backtest

A backtest offers the possibility to train a Model on past data and get a forecast for a past point in time (as opposed to a live Model). As the forecast is based on a point in time in the past, the Model performance can be evaluated immediately