# 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 --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://documentation.crossengage.io/predictions-platform/glossary-predictions.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.