---
title: "Amplitude Product Release Notes: April 2020"
description: "Amplitude's new features and product updates through April 2020 include the ability apply date offsets and rolling windows to in-line cohorts, as well as updates to our iOS and Javascript SDKs."
canonical: "https://amplitude.com/en-us/blog/release-notes-april-2020"
---

# Amplitude Product Release Notes: April 2020

Amplitude's new features and product updates through April 2020 include the ability apply date offsets and rolling windows to in-line cohorts, as well as updates to our iOS and Javascript SDKs.

Source: https://amplitude.com/en-us/blog/release-notes-april-2020

---

[Data Monster](/blog/author/datamonster)

[Mascot, Amplitude](/blog/author/datamonster)

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### Behavior Offset: apply a date offset to your in-line cohorts

*Available for all Scholarship, Growth, and Enterprise customers*

Behavior Offset helps you track how and when a user transitioned from one cohort into another cohort, by defining the user’s state in a previous time period. This is useful if you want a deeper understanding of how your user base is changing over time. You can track when an active user became a power user, or when a novice user became an engaged user, and then try to inspire that behavior in a larger user base with strategic product investments.

In the chart below, the user definition for the previous period is *users who performed Play Song or Video >= 1 time any time in each day offset by 30 days*. The user definition for the current period is *users who performed Purchase Song or Video >= 1 time any time in each day*.

The highlighted data point tells you that on Mon, Mar 30, 23 128 users performed Play Song or Video 30 days prior to performing Purchase Song or Video. For more information on Behavior Offset, check out our [documentation](https://help.amplitude.com/hc/en-us/articles/360040965352).

*Example of Behavior Offset*

#### Apply a rolling window to your in-line cohorts

You can also add a rolling window to your offset in-line cohorts in order to increase the interval of the previous time period.

In the chart below, the user definition for the previous period is *users who performed Play Song or Video >= 1 time any time in each day offset by 1 day with a rolling window of 30 days*. The user definition for the current period is set as *users who performed Purchase Song or Video >= 1 time any time in each day*.

The data point in this chart tells you that on Mon, Mar 30, 94 262 users performed Play Song or Video any time during the 30 days prior to performing Purchase Song or Video (i.e., users who purchased a song or video within 30 days of playing a song or video.) For more information on in-line cohort rolling windows, check out our [documentation](https://help.amplitude.com/hc/en-us/articles/360040965352#in-line-cohort-rolling).

*Example of an in-line cohort rolling window*

### Instrument your macOS apps

*Available for all customers*

Using Amplitude’s iOS SDK version 5.0.0, you can now instrument your apps running on macOS. We support two modes: pure macOS apps and [Mac Catalyst](https://developer.apple.com/mac-catalyst/) apps. For more on our iOS SDK installation, check out our [documentation](https://help.amplitude.com/hc/en-us/articles/115002278527).

### Turn off cookies with Amplitude’s Javascript SDK

*Available for all customers*

Amplitude’s Javascript SDK version 6.0.0 includes major changes to reduce the amount of data stored in cookies, as well as the ability to turn off cookies completely. If this option is enabled, the SDK will fall back to using localStorage. For more information on our Javascript SDK, check out our [documentation](https://help.amplitude.com/hc/en-us/articles/115001361248#settings-configuration-options).

About the author

Data Monster

Mascot, Amplitude

[More from Data](/blog/author/datamonster)

Data Monster spends most of its time nom nomming data and fulfilling duties as a cultural icon and brand ambassador for Amplitude. Data Monster wants everyone to know that there's a little data monster in all of us.

[More from Data](/blog/author/datamonster)

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