Explore what different options in the filtering section. We'll cover 4 standard filtering conditions and have a look at data type-specific ones. All this with corresponding examples.
As you may have already noticed there are different condition types that I can select while creating a filter. The options available are strictly related to different data types, like for example string, boolean, datetime etc. Let’s then discuss the variety of options available in filtering section. There are 4 main filtering condition types available for each attribute, regardless of its type - those are “exactly”, “is not”, “unknown” and “has any value”. Other conditions depend on the attribute type. For example when we look at a string attribute First name, we can choose: contains, does not contain, exactly, is not, is unknown, has any value, starts or not starts with, ends or not ends with, depending on what exact users we need to filter out. Let’s say I want all with the name John. We can see the filtered results here. And know all first names but John. So I see here all users whose first name is not John. If you want to make conditions less strict for string attributes, you can go for contains, starts, ends ones. For instance, I want all users with email address that do not end with “.com”. As we know, condition types are different for different data types, so let’s have a look at other ones. I’m going to custom attributes as we’ve already covered all possible attribute types here. So we have 4 basic conditions for a boolean attributes like “Subscribed to newsletter”. The most commonly used is “exactly” as you then specify whether you need users for whom it’s exactly true or exactly false. Similarly for “fixed choices”, which in our app is “Preferred language”, we see there are 4 basic condition types available. Then let’s go to a date attribute which is “Sign-up day”. We have a few additional conditions here: greater, lesser, less or more than x days ago, today, today anniversary, yesterday, current month. The one I really like is “today annivarsery” as thanks to it you can for example create an automation that sends personalized celebration emails to users having been with you exactly for a year. If you choose current month, you can filter out users than have signed up month-to-date. Important note: if you use “greater” / “lesser” or exactly you will need to pick a specific date as a reference point. If you however go for “less or more than x days ago” you type in a given number of days and it’s a relative date range, which is relative to the current date. When we look at datetime - which is “Last time browsing downloads”, we have two extra options when compared to the “date” one. Those are less or more than x minutes ago, which is possible thanks to the fact that we track exact time for this attribute. Let’s go to an integer now and it is “Number of purchases made”. Except for 4 basic possibilities there are “greater” and “lesser” available so I can for example filter out users that have made more than 10 purchases in the last 3 months. And the last type left is “floating point number”, and for that one there are exactly the same options as for an integer. So… We now know what possibilities of filtering there are for different data types. Thanks for your time!
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