The process below does not support GoogleUniversal Analytics.
The ability to segment your data by user acquisition source is critical to effectively managing your marketing plan. Knowing the acquisition source of new users shows which channels yield the top returns, and allows your team to allocate marketing dollars with confidence.
If you are not already tracking user acquisition sources in your database, MBI can help you get started:
We recommend two methods to track referral source data based on your setup:
If you leverage Google Analytics E-Commerce to track your order and sales data, you can leverage our Google Analytics E-Commerce Connector to sync each order’s referral source data. This will allow you to segment revenue and orders by referral source (for example,
utm_medium) and also get a sense of customer acquisition sources via MBI custom dimensions such as
User's first order source.
For Shopify users**: Turn on [Google Analytics E-Commerce] tracking in Shopify(http://docs.shopify.com/manual/settings/general/google-analytics#ecommerce-tracking) before connecting your Google Analytics E-Commerce account to MBI.
In this article we will explain how to save Google Analytics acquisition channel information into your own database - namely the
gclid parameters that were present on a user’s first visit to your website. For an explanation of these parameters, check out the [Google Analytics] documentation(http://support.google.com/analytics/bin/answer.py?hl=en&answer=1191184). Then, we will explore some of the powerful marketing analyses that can be performed with this information in MBI.
If you are just looking at the default Google Analytics conversion and acquisition metrics, you are not getting the whole picture. While seeing the number of conversions from organic search versus paid search is interesting, what can you do with that information? Should you spend more money on paid search? That depends on the value of customers coming from that channel, which is not something Google Analytics provides.
Google Analytics eCommerce Tracking does mitigate this problem by storing transaction data in Google Analytics, but this solution does not work for non-eCommerce sites, and certain tools like cohort analysis are not easy to do in the Google Analytics interface.
What if you want to email a follow-up deal to all customers acquired from a certain e-mail campaign? Or integrate acquisition data with your CRM system? This is impossible in Google Analytics - in fact, it is against the Terms of Service for Google Analytics to store any data that identifies an individual. But that does not mean you cannot store this data yourself.
Google Analytics stores visitor referral information in a cookie called
__utmz. After this cookie is set (by the Google Analytics tracking code), its contents will be sent with every subsequent request to your domain from that user. So in PHP, for example, you could check out the contents of
$_COOKIE['__utmz'] and you would see a string that looks something like this:
We took this code and translated it into a PHP library hosted on github. To use the library,
include a reference to
ReferralGrabber.php and then call
$data = ReferralGrabber::parseGoogleCookie($_COOKIE['__utmz']);
$data array will be a map of the keys
gclid and their respective values.
We recommend adding a new table to your database called, for example,
user_referral, with the columns like:
id INT PRIMARY KEY, user_id INT NOT NULL, source VARCHAR(255), medium VARCHAR(255), term VARCHAR(255), content VARCHAR(255), campaign VARCHAR(255), gclid VARCHAR(255). Whenever a user signs up, grab the referral information and store it to this table.
Now that we are saving user acquisition source, how can we use it?
Lets suppose we are using a SQL database and have a
users table with the following structure:
For starters, we can count the number of users coming from each referral channel by running the following query against your database:
SELECT acq_source, COUNT(id) as user_count FROM users GROUP BY acq_source;
The result will look something like this:
This is interesting, but of limited use. What we would really like to know is the growth rate of these numbers over time, the amount of revenue generated by each acquisition source, a cohort analysis of users coming from each source, and the probability that a user from one of these channels will return as a customer in the future. The queries required to do these analyses are complex - which is why we built MBI. Armed with this information we can determine our most profitable acquisition channels and focus our marketing time and money accordingly.