SQL Filter
Last updated
Last updated
CrossEngage's Predictions Platform provides various filtering options to add or remove Customers from certain Selections. This includes customer lists uploaded as files (blocklists and allowlists) or other Selections. These methods can be quite useful to restrict Selections, but can be time-intensive and limited in capability.
Hence the Platform allows you to enter a Custom SQL filter when creating Prediction Models.
While setting up the Model, you can find filter options In the Target Variable or Target Group settings. Here you can write a SQL filter with the help of our application, hence you do not need to know SQL.
You can also add filters in the "Row Filters" section under "Advanced Configuration". Row filters are applied to the data before preparation for the model; hence these filters can reduce the amount of the data going into the system and reduce the time they take to process. These filters are recommended if you want to optimize the Model's runtime or if certain data should be excluded from the analysis.
The fields can have the following data types:
Numeric: Whole numbers or floating point numbers
Strings: Any character such as digits, letters, special characters, etc. Strings are written in 'single quotes'.
Date & Time
Boolean: True or False
Operators can be used to compare or combine different values or expressions:
Arithmetic Operators: These can be applied to numeric values: +, -, *, /, etc.
Comparison Operators: These compare values between two expressions and return a boolean value: =, >, <, !=, IN, BETWEEN, LIKE etc.
Logical Operators: Boolean operators, which can combine different expressions and values together: AND, OR, etc.
Remember to use the correct case when using filters. A simple way to avoid case-sensitivity errors is to keep all values stored in the data tables in lowercase.
Wildcards are placeholder characters, which can be added to a string when a certain part of string is unknown or not fixed. Wildcards are used with the SQL LIKE operator to find similar strings.
Underscore: Underscore (_) can be used to replace a single unknown character in a string.
Percent: Percent (%) can be used to replace a series of unknown characters, of any length.
