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@appannie/react-ab-testing
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@appannie/react-ab-testing - npm Package Compare versions
Comparing version 1.2.2 to 1.3.0
@@ -6,2 +6,10 @@ # Change Log | ||
| ## 1.3.0 (2022-03-08) | ||
| **Note:** Version bump only for package @appannie/react-ab-testing | ||
| ### 1.2.2 (2021-08-23) | ||
@@ -8,0 +16,0 @@ |
@@ -7,3 +7,3 @@ { | ||
| }, | ||
| "version": "1.2.2", | ||
| "version": "1.3.0", | ||
| "description": "", | ||
@@ -21,3 +21,3 @@ "files": [ | ||
| "dependencies": { | ||
| "@appannie/ab-testing": "^1.2.2" | ||
| "@appannie/ab-testing": "^1.3.0" | ||
| }, | ||
@@ -27,3 +27,3 @@ "peerDependencies": { | ||
| }, | ||
| "gitHead": "4660c199fb4ea6b7f21ad84d401b0d33e067a818" | ||
| "gitHead": "491a631d53a8f8c1f38dd62c2ae145d057a477dc" | ||
| } |
@@ -6,22 +6,22 @@ <!-- TOC --> | ||
| - [Package API References](#package-api-references) | ||
| - [For React: `@appannie/react-ab-testing`](#for-react-appanniereact-ab-testing) | ||
| - [Installation](#installation) | ||
| - [Usage](#usage) | ||
| - [For Vanilla Javascript: `@appannie/ab-testing`](#for-vanilla-javascript-appannieab-testing) | ||
| - [Installation](#installation-1) | ||
| - [Usage](#usage-1) | ||
| - [Javascript Hashing Utils `@appannie/ab-testing-hash-object`](#javascript-hashing-utils-appannieab-testing-hash-object) | ||
| - [Installation](#installation-2) | ||
| - [Usage](#usage-2) | ||
| - [For Python: `py-ab-testing`](#for-python-py-ab-testing) | ||
| - [Installation](#installation-3) | ||
| - [Usage](#usage-3) | ||
| - [Configration Hashing](#configration-hashing) | ||
| - [Prepare config file BEFORE make it public](#prepare-config-file-before-make-it-public) | ||
| - [In runtime](#in-runtime) | ||
| - [For React: `@appannie/react-ab-testing`](#for-react-appanniereact-ab-testing) | ||
| - [Installation](#installation) | ||
| - [Usage](#usage) | ||
| - [For Vanilla Javascript: `@appannie/ab-testing`](#for-vanilla-javascript-appannieab-testing) | ||
| - [Installation](#installation-1) | ||
| - [Usage](#usage-1) | ||
| - [Javascript Hashing Utils `@appannie/ab-testing-hash-object`](#javascript-hashing-utils-appannieab-testing-hash-object) | ||
| - [Installation](#installation-2) | ||
| - [Usage](#usage-2) | ||
| - [For Python: `py-ab-testing`](#for-python-py-ab-testing) | ||
| - [Installation](#installation-3) | ||
| - [Usage](#usage-3) | ||
| - [Configration Hashing](#configration-hashing) | ||
| - [Prepare config file BEFORE make it public](#prepare-config-file-before-make-it-public) | ||
| - [In runtime](#in-runtime) | ||
| - [Configuration File Reference](#configuration-file-reference) | ||
| - [Configuration File Hashing and Protecting private information](#configuration-file-hashing-and-protecting-private-information) | ||
| - [Example configuration and common use case](#example-configuration-and-common-use-case) | ||
| - [Excluding a user from an experiment](#excluding-a-user-from-an-experiment) | ||
| - [Internal whitelist](#internal-whitelist) | ||
| - [Configuration File Hashing and Protecting private information](#configuration-file-hashing-and-protecting-private-information) | ||
| - [Example configuration and common use case](#example-configuration-and-common-use-case) | ||
| - [Excluding a user from an experiment](#excluding-a-user-from-an-experiment) | ||
| - [Internal whitelist](#internal-whitelist) | ||
| - [Credits](#credits) | ||
@@ -43,3 +43,3 @@ | ||
| 1. Percentage based segmentation: Randomly allocate certain percent of user to a "[cohort](#terms)". The allocation algorithm guarantees to produce stable results within a single [experiment](#terms), and produce a different set of users for another [experiment](#terms) in a random way. Take a look at this example: `first 25% users`, it will always result into the same group of users in [experiment](#terms) `A`, but results in another completely different group of users for [experiment](#terms) `B`. | ||
| 1. Percentage based segmentation: Randomly allocate certain percent of user to a "[cohort](#terms)". The allocation algorithm guarantees to produce stable results within a single [experiment](#terms), and produce a different set of users for another [experiment](#terms) in a random way. You can also further filter the user set using the key `allocation_criteria`. Take a look at this example: `first 25% users`, it will always result into the same group of users in [experiment](#terms) `A`, but results in another completely different group of users for [experiment](#terms) `B`. | ||
| 2. User profile based segmentation: Manually assign a specific group of users to a cohort. This can be used to achieve features like "expose a new feature to the company emplyees for internal testing". Note the profile based segmentation works independently from the percentage based method. They doesn't interfere each other. | ||
@@ -318,2 +318,8 @@ | ||
| ], | ||
| // The allocation_criteria key allows us to further filter the set of users by enforcing | ||
| // a criteria that must be valid before the cohort is approved. In this case, | ||
| // the user must have an email domain of data.ai or appannie.com. | ||
| allocation_criteria: { | ||
| email_domain: ['appannie.com', 'data.ai'] | ||
| } | ||
| }, | ||
@@ -331,3 +337,3 @@ { | ||
| At App Annie, we're maintaining this configuration in yaml format to reduce the syntax noise. We have an automated CI task to encode/encrypt and push the final configuration to a public S3 bucket from where our SDK retrieve the configuration. | ||
| At Data AI, we're maintaining this configuration in yaml format to reduce the syntax noise. We have an automated CI task to encode/encrypt and push the final configuration to a public S3 bucket from where our SDK retrieve the configuration. | ||
@@ -385,3 +391,3 @@ ## Configuration File Hashing and Protecting private information | ||
| force_include: | ||
| # Force all App Annie user under the given cohort | ||
| # Force all Data AI user under the given cohort | ||
| email_domain: | ||
@@ -393,4 +399,4 @@ - appannie.com | ||
| Made with ❤️ by [Zhang Tao](https://github.com/ZigZagT) and [Simon Boudrias](https://github.com/SBoudrias) from the App Annie Beijing office. | ||
| Made with ❤️ by [Zhang Tao](https://github.com/ZigZagT) and [Simon Boudrias](https://github.com/SBoudrias) from the Data AI Beijing office. | ||
| Available for public use under the MIT license. |
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Improved metrics
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- Number of lines in readme file
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Dependency changes
Updated@appannie/ab-testing@^1.3.0