A client-side multivariate testing (A/B tests) plugin for jQuery.
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jquery.labrats.js

README.md

This lab-rats project provides a jQuery plugin for doing multi-variate testing (A/B Tests) on the client's browser... in other words, treating your customers like lab rats in order to engineer the best web application.

Quick Start

To give an example of how to use this plugin, let's pretend you want to measure the results of changing the look of the sign up button. This experiment will split your visitors into two equal "groups". Group 0 will be shown the Shiny Red button, and Group 1 will be shown the Flashy Blue button. We call this test, the Big Button experiment.

Taking advantage of the labrats plugin is a simple two step process:

Step 1. Create a Function for each Group

Each group will have a special function that will change or render the page slightly differently:

function shinyRed() {
    $('#big-button').addClass('shiny-red');
}

function flashyBlue() {
    $('#big-button').addClass('flashy-blue');
}

Normally, you would put an element to report which element is shown to which user, but we'll get to that in a minute.

Step 2. Call the labrats() for Test Subject

The final step amounts to having the labrats() function call one of the callback functions for the user based on their group.

$.labrats( { name: 'Big Button',
             callbacks: [ shinyRed, flashyBlue ] } );

That is all that is needed to get a barebones test showing different button styles to different users.

On Reporting

Of course, a multivariate test is not really an experiement without the scientific principles of observations and reporting.

While I really can't help you in this regard, let me assume that you've written a tracking() function that can send a message to something like Google Analytics.

Now, we just need to change our functions a wee bit:

function shinyRed() {
    tracking('shown', 'shiny-red', guid);
    $('#big-button').addClass('shiny-red').click( function(){
        tracking('clicked', 'shiny-red', guid);
    });
}

function flashyBlue() {
    tracking('shown', 'flashy-blue', guid);
    $('#big-button').addClass('flashy-blue').click( function(){
        tracking('clicked', 'flashy-blue', guid);
    });
}

Notice each function tracks if the button was shown and whether it was clicked. You really should track both to get a clear coorelation between the size of your test and its success.

On Identifying Users

The plugin keeps track of a user account by storing a unique ID in the browser's stash of cookies, however, you can specify the ID you want it to use. For instance, assuming that you had a guid variable like:

var guid = 'bcfb3529-0fed-4b05-8414-db3e1d2b11da';

You can pass in this value as a key to the $.labrats() function:

$.labrats( { key: guid, name: 'Big Button',
             callbacks: [ shinyRed, flashyBlue ] } );

Note: If a key is not specified, the user ID calculated is simply a large random number. It is NOT a GUID and may not be unique among all your users. Since it is internal to this plugin and meaningless, it is obviously not very useful for tracking and reporting results. This is why we recommend specifying your own ID key.

On Hashing Issues

The hashing algorithm that comes with this plugin is pretty... uh, simplistic. Actually, it is downright stupid, and the resulting distribution isn't great. However, you can specify a hashing algorithm.

The function you give the hash must be able to accept a string and return an integer number, for instance:

  $.labrats.configure( {
       hash: function(key) {
                 return murmurhash3_32_gc(key, 73);
             }
  });

It seems the MurmurHash is quite good at distribution, and initial experiments show it a good algorithm for this plugin.

Recipes and Examples

This section contains a series of contrived examples. Each assume that you've created some functions that render the tests for a user.

Two Parallel 50/50 Tests

You want to run two tests, "Big Button" (shows either a red or blue sign up button) and "New Logo" (which shows the "new" or "old" logo). Each test will involve all web site visitors split down the middle. This gives four possibilities:

Overlapping Tests Illustration

The code to run these tests involves two calls to the plugin:

$.labrats( { name: 'Big Button',
             callbacks: [ shinyRed, flashyBlue ] } );

$.labrats( { name: 'New Logo',
             callbacks: [ oldlogo, newlogo ] } );

Having a Control Group

A control group contains the users who see the "old stuff". This is still a "group". For instance, in the previous example, the "control" is the group that see the old logo.

However, if the logo is particularly daring, and you only wanted to show it to 10% of your users, you would use the subset option:

$.labrats( { name: 'New Logo', subset: 10,
             callbacks: [ newlogo ], control: oldlogo } );

Note: The callbacks can accept one function (creating a single test group), but it still only accepts and array.

The control parameter is optional. This could be useful if the old logo is already being displayed and the newlogo() function simply replaced it.

Having a Control plus Test Groups

Suppose your graphics department churned up two hot new company logos you want to A/B test, but you still wanted the bulk of your users to see the old one until after the testing is complete.

$.labrats( { name: 'New Logo', subset: 50,
             callbacks: [ newlogoA, newlogoB ],
             control: oldlogo } );

In the above code, 50% of the users will see the old logo (by calling the oldlogo() function). The remaining users are split, so 25% of your users will see the "A" logo version (by calling newlogoA()) and 25% will see the "B" logo version (by calling newlogoB()).

Keep in mind that since we are dealing with random numbers, 25% means about 25%. For instance, in some of the tests that we ran with 1,000 user IDs, we got the following actual distribution:

Distribution for Test 1

Multiple Non-Overlapping Tests

In our first recipe, we wanted to test both our sign up button (which could be red or blue) as well as our logo change. What if these tests were so major that you didn't want a customer to see either the red or blue buttons if they also saw the new logo?

Here we introduce the concept of a slice. Each slice can contain a non-overlapping test... but only from tests in other slices. A slice must be named. Here is the example code:

$.labrats( { name: 'Major Tests', slices: 2, slice: 0,
             callbacks: [ shinyRed, flashyBlue ] } );

$.labrats( { name: 'Major Tests', slices: 2, slice: 1,
             callbacks: [ oldlogo, newlogo ] } );

In this code, the name refers to the slice collection and the test is specified with the slice parameter. This divides our users into 4 groups of 25% each:

Discreet Tests Illustration

Note: Some companies create this "major" slicing division with lots of small slices (like 20 slices of 5% each), and then allocate them to tests over time.

Function API

This section details all available functions in this plugin. While the primary function is $.labrats(), fine-grain control may be had with the other functions described below.

$.labrats()

Calls a function based on an assigned test group for a user. The identification key for the user (as well as the name(s) of the test) can be passed in as function arguments along with two or more callback functions (note that their order matters).

For instance, for a test that splits the user accounts into three groups, you could do:

$.labrats.configure( { groups: 3 } ); // Optional
$.labrats(userid, "Some Test", fn1, fn2, fn3);

The other approach to calling this function is with named parameters. For instance, the same example could be written:

$.labrats({ key: userid, name: "Some Test", groups:3,
            callbacks: [ fn1, fn2, fn3 ] });

This function returns the results of calling one of the callback functions.

Note: The size of available pool for tests can be limited (effectively creating a a pool of people in test groups and another control group). For instance:

$.labrats.group( { key: userId, name: "Another Test", subset: 10, callbacks: [ fn1, fn2, fn3 ], control: fn4 });

Will call the control function if the user is part of the 90% control group, otherwise, it calls the appropriate function in the callbacks array.

(See the group() function for details as to the other acceptable values for named parameters)

$.fn.labrats()

Behaves like the utility function, $.labrats(), but the callback function is given the jQuery selector results. This allows the callback function to behave as part of a jQuery chain. For instance:

function fn1() {
   return this.addClass("shiny-red");
};
function fn2() {
   return this.addClass("flashy-blue");
};

var testCfg = { key: id1, callbacks: [ fn1, fn2 ] };
$("#logo-test").labrats(testCfg).click(...);

Note: Only named parameters work as arguments.

$.labrats.group()

Determines the group number assigned to a given user. The number of groups can be specified using the configure() function (see below). The user's key is passed in as the parameter, but this should also take the name of the test as well.

This can either be specified as parameters, as in:

$.labrats.group( userID, "Large Logo Test" );

Or as a series of keys in an array. The following is equivalent:

$.labrats.group( [ userID, "Large Logo Test" ] );

Or as a collection of named parameters:

$.labrats.group( { key: userID, name: "Large Logo Test",
                   groups: 2 } );

This last approach allows you to specify the number of groups (instead of calling the configure() function).

Accepted Parameters:

  • key - The identification of the user account
  • name - The name of the test. The name is appended to the key in order to compute the hash value. This guarantees that each test has a different distribution of user accounts.
  • groups - The number of active groups a non-controlled test account can be in. If not given, this defaults to 0.
  • subset - A percentage (from 0 to 100) that Specifies the size of the test pool. User accounts that hash outside this value are part of the control. If not specified, this defaults to 100 (meaning, no control group).
  • slices - Divides the test pool into discreet slices where a user account can be in only one slice. This allows distinct test groups that don't overlap essentially guaranteeing that a user account could be in at most, one test group. The slices works for a given name parameter.
  • slice - The name of the slice this user should belong in order to qualify for being part of a test group.
  • hash - The function used to convert the string into a number than can be divided into the different test buckets.

Limit Test Pool with subset

You can limit the size of available pool (effectively creating a a pool of people in test groups and another control group). For instance:

$.labrats.group( { key: userId, name: "Another Test",
                   groups: 2, subset: 10 });

Will return -1 if the user is part of the 90% control group, otherwise, it returns either 0 or 1 if it is in one of the 5% sized test groups.

Slicing Test Pool

With multiple tests, a random distribution algorithm means that some users will end up in more than one test group. The slices option divides the test pool into discreet subgroups, and the slice option specifies which slice to use for a particular test.

For instance, suppose you have some experiments that are quite invasive, (perhaps even conflicting if a person ended up as a lab rat in more than one), we could define the first experiment to use the first slice:

$.labrats.group( { key: userId, name: "serious tests",
                   slices: 3, slice: 0, groups: 2 });

The second experiment would use the next slice:

$.labrats.group( { key: userId, name: "serious tests",
                   slices: 3, slice: 1, groups: 2 });

Notice the test name for the group of slices must be the same.

This slicing feature can be combined with the subset feature to keep a control group out. Also, when using the subset and the slicing features, the groups option can be unspecified in order to default to 1 (a single test group).

With five experiments where each experiment is in a slice with two test groups, we might have a distribution illustrated in this diagram:

Distribution for Test 1

$.labrats.inGroup()

A test to see if a particular 'key' is part of the given group. Returns true if a given key is in the group number, false otherwise.

This function can be called either with named parameters, as in:

$.labrats.inGroup( 2, { key: userid, name: testname }

Where the first argument is the group number to check, and the second argument is an object similar to what is passed to the $.labrats.group() function, including:

  • key is the identification of the user
  • name is the test's name
  • subset is the size of the pool, where 100 - subset is the size of the control group

This function can also be called as a series of parameters:

$.labrats.inGroup( 2, userid, testname )

$.labrats.key()

Converts a series of arguments into a key to use in a hash. Each function may call this using a few formats. For instance, as a series of string arguments:

var key = $.labrats.key("test", "abc", id);

or as an array:

var key = $.labrats.key([id, "test", "abc"]);

or even as the function's arguments:

var key = $.labrats.key(arguments);

$.labrats.getId()

Returns an unique identification for the current user's browser. If this is the first time a user has seen the application, we generate a new ID (as a random number), otherwise, we return the ID stored in a cookie.

$.labrats.configure()

This function allows a single object to overwrite some, but not all configuration values. Acceptable values include:

  • hash: A function used to convert a user ID key and test name into a number
  • groups: The number of test groups to divide the user pool