---
layout: page
title: Metrics
---

<div id="toc">
# "Defining a Metric":#define
# "Metrics From Your Database":#ar
# "Google Analytics":#ga
# "Creating Your Own Metric":#custom
# "Digging Deeper":#deeper
</div>


A good starting point for improving -- on anything -- is measuring.  Vanity allows you to measure multiple metrics, best way to tell how well your experiments are doing.

<blockquote>
"Startup metrics for pirates: AARRR!":http://500hats.typepad.com/500blogs/2007/09/startup-metrics.html
# Acquisition
# Activation
# Retention
# Referral
# Revenue
</blockquote>



h3(#define).  Defining a Metric

Vanity always loads metrics defined in the @experiments/metrics@ directory.  A metric definition is a Ruby file that looks like this:

<pre>
metric "Signup (Activation)" do
  description "Measures how many people signed up for our awesome service."
end
</pre>

That's a basic metric and you feed it data by calling the @track!@ method.  For example:

<pre>
class AccountsController < ApplicationController

  def create
    @person = Person.new(params[:person])
    if @person.save
      Vanity.track!(:signup)      # track successful sign up
      UserSession.create person
      redirect_to root_url
    else
      render :action=>:new
    end
  end

end
</pre>

The metric identifier is the same as the file name.  The above example defines the metric @:signup@ in the file @experiments/metrics/signup.rb@.

You can call @track!@ with a value to track.  This example tracks how many items were bought during the day:

<pre>
def checkout
  Vanity.track!(:items, @cart.items.count)
  . . .
end
</pre>

Calling @track!@ with no value is the same as calling with one, and for convenience you can pass zero and negative numbers, both will be ignored.

!images/signup_metric.png!

Define, track, and you're ready to roll.


h3(#ar).  Metrics From Your Database

If you already have the data, why not use it?

This example defines a metric for signups, based on the number of @Account@ records created each day:

<pre>
metric "Signup (Activation)" do
  description "Measures how many people signed up for our awesome service."
  model Account
end
</pre>

You don't need to call @track!@ with this metric, all the data already exists.  It's a simple query to count the number of records created, grouped by their timestamp (@created_at@).  And since it's querying the database, you'll immediately see historical data for the last 90 days.

Even though the metric itself doesn't store any information, it needs to update experiments whenever new records are created.  To do that, it registers itself as an @after_create@ callback on the model.

Some metrics measure values, not occurrences.  For example, this metric measures user satisfaction by calculating average value from the column @rating@:

<pre>
metric "Satisfaction Survey" do
  description "Measures how satisfied people are with our service."
  model Survey, :average=>:rating
end
</pre>

The aggregates you can use this way are: @:average@, @:minimum@, @:maximum@ and @:sum@.

You can use a condition when the metric only applies to some records.  Here's a metric that only measures unlimited accounts:

<pre>
metric "Signups to Unlimited" do
  description "Signups to our All You Can Eat and Burp Unlimited plan."
  model Account, :conditions=>{ :plan_type=>'unlimited' }
end
</pre>

If you have named scopes, you'll want to use them instead:

<pre>
metric "Signups to Unlimited" do
  description "Signups to our All You Can Eat and Burp Unlimited plan."
  model Account.unlimited
end
</pre>

When you view this metric, it calculates the number of accounts created on any given day that are currently unlimited plans.  So, if ten accounts were created over the past week, and today five of them upgraded to unlimited plan, the metric will show five unlimited accounts (current state) but spread over the past week (their @created_at@ timestamp).

If your metric uses aggregates or conditions, and the aggregate/conditional attributes change over time, and you need to know when the change took place, consider tracking the event.

This example tracks when accounts were created or upgraded to unlimited plan:

<pre>
metric "Signups (Unlimited)" do
  description "Signups to our All You Can Eat and Burp Unlimited plan (including upgrades)."
  Account.after_save do |account|
    Vanity.track!(:signup) if account.plan_type_changed? && account.plan_type == 'unlimited'
  end
end
</pre>

The @model@ specifier can also take an @:identity@ option.  @:identity:@ should be a @Proc@ that specifies how to fetch the identity of the experiment participant.  This is useful when constructing objects outside the ActionController context (perhaps in a background task).  However, note that your experiment participants may still be identified by their anonymous cookie identifier, if you started any experiments before the user was identifiable.

This example will record conversions for the Account to which the Subscription belongs:

<pre>
metric "Subscriptions finished on backend" do
  description "These signups were actually created in a background task."
  model Subscription, :identity => lambda { |record| record.account_id }
end
</pre>

h3(#ga).  Google Analytics

You can easily include Google Analytics metrics in your Vanity dashboard.  You'll need, in addition to Vanity, to use "Garb":http://github.com/vigetlabs/garb, a Ruby wrapper for the Google Analytics API.

Login to Google Analytics using either username and password, or OAuth authentication token.  Here's a sample @config/environment@ snippet:

<pre>
Rails::Initializer.run do |config|
  gems.config "vanity"
  gems.config "garb"

  . . .
  config.after_initialize do
    require "garb"
    ga = YAML.load_file(Rails.root + "config/ga.yml")
    Garb::Session.login(ga['email'], ga['password'], account_type: "GOOGLE")
  end
end
</pre>

To define a metric that corresponds to the Google Analytics daily visitors:

<pre>
metric "Acquisition: Visitors" do
  description "Unique visitors on any given page, as tracked by Google Analytics"
  google_analytics "UA-1828623-6", :visitors
end
</pre>

The first argument is the GA profile, the second argument the GA metric name (defaults to @pageviews@).

You can use the full Garb API by accessing the report directly, for example:

<pre>
metric "Activation: Signups" do
  google_analytics "UA-1828623-6"
  report.filters do
    eql(:page_path, 'welcome')
  end
end
</pre>

See "the Garb documentation":http://rdoc.info/projects/vigetlabs/garb and "Google Analytics API":http://code.google.com/apis/analytics/docs/gdata/gdataReferenceDimensionsMetrics.html#bounceRate for more details.


h3(#custom).  Creating Your Own Metric

Got other ideas for metrics?  Writing your own metric is fairly simple.

The easiest way to create your own metric is by adding your own @values@ method, for example:

<pre>
metric "Hours in a day" do
  description "Measures how many hours in each day."
  def values(from, to)
    (from..to).map { |i| 24 }
  end
end
</pre>

This example is based on @Vanity::Metric@.  You can, of course, base your metric on any other class.

For simplicity, a metric is any object that implements these two methods:

* @name@ -- Returns the metric's name, which will show up in the dashboard/report.
* @values@ -- Receives a start date and end date and returns an array of values for all dates in that range (inclusive).

A metric may also implement these methods:

* @description@ -- Returns human readable description.
* @bounds@ -- Returns acceptable upper and lower bounds (@nil@ if unknown).
* @hook@ -- "A/B tests":ab_testing.html use this to manage their own book keeping.

If you wrote your own metric implementation, please consider "contributing it to Vanity":contributing.html so we can all benefit from it.  Thanks.


h3(#deeper).  Digging Deeper

All metrics are listed in @Vanity.playground.metrics@, a hash that maps metric identifier to metric object.  Methods like @track!@ and @metrics@ (see "A/B tests":ab_testing.html) reference metrics using their identifier.

On startup, Vanity loads all the metrics it finds in the @experiments/metrics@ directory.  The metric identifier is the same as the file name, so @experiments/metrics/coolness.rb@ becomes @:coolness@.

You can always populate the hash with your own metrics.

When Vanity loads a metric, it evaluates the metric definition in a context that has two methods: @metric@ and @playground@.  The @metric@ method creates a new @Vanity::Metric@ object, and evaluates the block in the context of that object, so when you see the metric definition using methods like @description@ or @model@, these are all invoked on the metric object itself.

A @Vanity::Metric@ object responds to @track!@ and increments a record in the database (an _O(1)_ operation).  It creates one record for each day, accumulating that day's count.  When generating reports, the @values@ method fetches the values of all these keys (also _O(1)_).

You can call @track!@ with a value higher than one, and it will increment the day's count by that value.

Any time you track a metric, the metric passes its identifier, timestamp and count (if more than zero) to all its hooks.  "A/B tests":ab_testing.html use hooks to manage their own book keeping.  When you define an experiment and tell it which metric(s) to use, the experiment registers itself by calling the @hook@ method.

When you call @model@ on a metric, this method changes the metric definition by rewriting the @values@ method to perform a query, rewriting the @track!@ method to update hooks but not the database, and register an @after_create@ callback that updates the hooks.

How about some tips & tricks for getting the most out of metrics (you might call them "best practices")?  Got any to share?