Google
Analytics, a brilliant digital marketing tool
Google Analytics
is, without any doubt, one of the most used piece of software for companies
active on Internet through a website or any web platform. Developed by the
successful Californian company, it aims at providing a better understanding of
user’s identity and behaviors in order to take actions fostering the digital
Holy Grail metric, also known as “conversion rate”.
In our multi-connected era, it is critical
for firms to collect and use those data to track their website’s traffic in
order to be as effective as possible. Therefore, Google Analytics helps
worldwide digital marketing teams to answer both simple and complex questions
such as:
- How many people visit my website?
- Where do my visitors live?
- Who are my customers?
- Which pages of my website are the most popular?
- What is my conversion rate?
There are hundreds of problems that can be
answered thanks to data collected in Google Analytics; but website owners
cannot only look at the overall numbers without properly analyzing them.
A/B
testing and segmentation, two different concepts linked for digging into data
In this article,
Shane Barker – a Digital Marketing Consultant specialized in SEO - explains two
different concepts used in digital marketing: “A/B testing” and “Segmentation”.
The first of those two concepts is
basically an experiment comparing two (or more) versions of a same web page (version
A and version B and so on). Thus, having those different versions will help decision
makers finding which one increases the traffic on and gives a higher rate of
conversion.
The second concept is about how to divide
the website audience. Segmentation is the process of grouping prospective
buyers that have common needs or characteristics into segments.
While taking its roots in traditional
marketing, this method is still particularly important in the digital world. Cookies,
Google accounts, IP addresses for instance are providing Google Analytics with
precise and relevant information about a website’s users. Those data can then
be used in order to lead precise A/B test operations by focusing on people who
are the more relevant for a given conversion and for offering them personalized
remarketing campaigns afterwards.
Here are some examples of potentially
relevant segment that can be made using Google’s tool:
- Visitor Type (New visitor vs Returning Visitor)
- Location
- Content Viewed
- Landing Page Type (combination of on-site and off-site customer behavior)
- Action taken (which customer have completed conversion goals)
- Value
- Demographics
- Engagement
- Technology platform (visitor’s devices used)
Comparing those segments’ performance is
the key for a strong analysis. Linking those two concepts is a must-do in order
to have a clear vision of the reaction of a category of user to the changes made.
One shouldn’t take data displayed for granted without digging into details: focusing
on a certain population is as important as focusing on certain pages of your
website. Creating segments for each test variation may seems time consuming but
the relevance of the result will, for sure be higher. While a lot of A/B tests
undertaken are leading to no changes in conversion rate, working with qualitative
samples is one of the paths toward better understanding of a company’s business
objectives.
Personal
analysis
This article wittily
highlights the importance of segmentation in order to enhance website (or app)
optimization. Nevertheless, the missing part is, based on our knowledge of
digital marketing, the statistical relation between A/B testing and
segmentation. Qualitative segmentation is important and relevant for solid
results. Nevertheless, quantitative relevance shouldn’t be forgotten. The
concept linking mathematics and accurate results leading to good operational
and strategic decision is called “statistical significance”. Defined by
Investopedia as “the likelihood that a relationship between two or more
variables is caused by something other than random chance”, statistical
significance is, in other words, reducing the risk of inaccurate results by
excluding potential false positive data. In order to get enough statistical
significance in the test results, the segments made must contain a solid number
of users. To do so, marketers can either use statistics calculations* or the
tool developed by Evan Miller and available here: http://bit.ly/1rUSvW2
Moreover, this year, Google is launching a
powerful suit called Google Analytics 360 (still in beta test version as this
article is written). One of the 6 tools
developed by the company will be “Google Optimize 360”, an interesting
optimization and A/B test tool that offer direct integration with Google
Analytics. With this new update comes two major news for our subject.
The first is a new metric named “Session
Quality score”. This indicator, by displaying signs of conversion intentions
based on quantitative information such as session duration or number of pages
visited, will be a relevant for A/B testing. By using Machine Learning to “predict
the likelihood of a visitor making a transaction on your site or app.” (Google),
it will enable new opportunities in user testing (and remarketing).
Secondly, segments already used in Google
Analytics will now directly be available as targets for tests. This integration
in the Google ecosystem will bring a huge time gain and productivity rise.
The
last piece of advice
Mixing
quantitative and qualitative relevancy while working with data is a key for
accurate results. While powerful tools as Google Analytics, Optimizely and
Optimize 360 are at the disposal of digital marketing teams; one shouldn’t only
rely on pre-made tools and by-default processes. The path leading to more
conversion is large and complex but an analysis mind and a great customer
understanding will be your best ally in order to catch with your business objectives!
* Statistic calculation is the following:
More details here
Sijelmassi Mehdi
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