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• Segmentation Analytics : How to Leverage It on Your Site

  27 octobre 2023, par Erin — Analytics Tips

  The deeper you go with your customer analytics, the better your insights will be.

  The result ? Your marketing performance soars to new heights.

  Customer segmentation is one of the best ways businesses can align their marketing strategies with an effective output to generate better results. Marketers know that targeting the right people is one of the most important aspects of connecting with and converting web visitors into customers.

  By diving into customer segmentation analytics, you’ll be able to transform your loosely defined and abstract audience into tangible, understandable segments, so you can serve them better.

  In this guide, we’ll break down customer segmentation analytics, the different types, and how you can delve into these analytics on your website to grow your business.

  What is customer segmentation ?

  Before we dive into customer segmentation analytics, let’s take a step back and look at customer segmentation in general. 

  Customer segmentation is the process of dividing your customers up into different groups based on specific characteristics.

  These groups could be based on demographics like age or location or behaviours like recent purchases or website visits. 

  By splitting your audience into different segments, your marketing team will be able to craft highly targeted and relevant marketing campaigns that are more likely to convert.

  Additionally, customer segmentation allows businesses to gain new insights into their audience. For example, by diving deep into different segments, marketers can uncover pain points and desires, leading to increased conversion rates and return on investment.

  But, to grasp the different customer segments, organisations need to know how to collect, digest and interpret the data for usable insights to improve their business. That’s where segmentation analytics comes in.

  What is customer segmentation analytics ?

  Customer segmentation analytics splits customers into different groups within your analytics software to create more detailed customer data and improve targeting.

  

  With customer segmentation, you’re splitting your customers into different groups. With customer segmentation analytics, you’re doing this all within your analytics platform so you can understand them better.

  One example of splitting your customers up is by country. For example, let’s say you have a global customer base. So, you go into your analytics software and find that 90% of your website visitors come from five countries : the UK, the US, Australia, Germany and Japan.

  In this area, you could then create customer segmentation subsets based on these five countries. Moving forward, you could then hop into your analytics tool at any point in time and analyse the segments by country. 

  For example, if you wanted to see how well your recent marketing campaign impacted your Japanese customers, you could look at your Japanese subset within your analytics and dive into the data.

  The primary goal of customer segmentation analytics is to gather actionable data points to give you an in-depth understanding of your customers. By gathering data on your different audience segments, you’ll discover insights on your customers that you can use to optimise your website, marketing campaigns, mobile apps, product offerings and overall customer experience.

  Rather than lumping your entire customer base into a single mass, customer segmentation analytics allows you to meet even more specific and relevant needs and pain points of your customers to serve them better.

  By allowing you to “zoom in” on your audience, segmentation analytics helps you offer more value to your customers, giving you a competitive advantage in the marketplace.

  5 types of segmentation

  There are dozens of different ways to split up your customers into segments. The one you choose depends on your goals and marketing efforts. Each type of segmentation offers a different view of your customers so you can better understand their specific needs to reach them more effectively.

  While you can segment your customers in almost endless ways, five common types the majority fall under are :

  

  Geographic

  Another way to segment is by geography.

  This is important because you could have drastically different interests, pain points and desires based on where you live.

  If you’re running a global e-commerce website that sells a variety of clothing products, geographic segmentation can play a crucial role in optimising your website.

  For instance, you may observe that a significant portion of your website visitors are from countries in the Southern Hemisphere, where it’s currently summer. On the other hand, visitors from the Northern Hemisphere are experiencing winter. Utilising this information, you can tailor your marketing strategy and website accordingly to increase sells.

  Where someone comes from can significantly impact how they will respond to your messaging, brand and offer.

  Geographic segmentation typically includes the following subtypes :

  • Cities (i.e., Austin, Paris, Berlin, etc.)
  • State (i.e., Massachusetts)
  • Country (i.e., Thailand)

  Psychographic

  Another key segmentation type of psychographic. This is where you split your customers into different groups based on their lifestyles.

  Psychographic segmentation is a method of dividing your customers based on their habits, attitudes, values and opinions. You can unlock key emotional elements that impact your customers’ purchasing behaviours through this segmentation type.

  Psychographic segmentation typically includes the following subtypes :

  • Values
  • Habits
  • Opinions

  Behavioural

  While psychographic segmentation looks at your customers’ overall lifestyle and habits, behavioural segmentation aims to dive into the specific individual actions they take daily, especially when interacting with your brand or your website.

  Your customers won’t all interact with your brand the same way. They’ll act differently when interacting with your products and services for several reasons. 

  Behavioural segmentation can help reveal certain use cases, like why customers buy a certain product, how often they buy it, where they buy it and how they use it.

  By unpacking these key details about your audience’s behaviour, you can optimise your campaigns and messaging to get the most out of your marketing efforts to reach new and existing customers.

  Behavioural segmentation typically includes the following subtypes :

  • Interactions
  • Interests
  • Desires

  Technographic

  Another common segmentation type is technographic segmentation. As the name suggests, this technologically driven segment seeks to understand how your customers use technology.

  While this is one of the newest segmentation types marketers use, it’s a powerful method to help you understand the types of tech your customers use, how often they use it and the specific ways they use it.

  Technographic segmentation typically includes the following subtypes :

  • Smartphone type
  • Device type : smartphone, desktop, tablet
  • Apps
  • Video games

  Demographic

  The most common approach to segmentation is to split your customers up by demographics. 

  Demographic segmentation typically includes subtypes like language, job title, age or education.

  This can be helpful for tailoring your content, products, and marketing efforts to specific audience segments. One way to capture this information is by using web analytics tools, where language is often available as a data point.

  However, for accurate insights into other demographic segments like job titles, which may not be available (or accurate) in analytics tools, you may need to implement surveys or add fields to forms on your website to gather this specific information directly from your visitors.

  How to build website segmentation analytics

  With Matomo, you can create a variety of segments to divide your website visitors into different groups. Matomo’s Segments allows you to view segmentation analytics on subsets of your audience, like :

  • The device they used while visiting your site
  • What channel they entered your site from
  • What country they are located
  • Whether or not they visited a key page of your website
  • And more

  While it’s important to collect general data on every visitor you have to your website, a key to website growth is understanding each type of visitor you have.

  For example, here’s a screenshot of how you can segment all of your website’s visitors from New Zealand :

  

  The criteria you use to define these segments are based on the data collected within your web analytics platform.

  Here are some popular ways you can create some common themes on Matomo that can be used to create segments :

  Visit based segments

  Create segments in Matomo based on visitors’ patterns. 

  For example :

  • Do returning visitors show different traits than first-time visitors ?
  • Do people who arrive on your blog experience your website differently than those arriving on a landing page ?

  This information can inform your content strategy, user interface design and marketing efforts.

  Demographic segments

  Create segments in Matomo based on people’s demographics. 

  For example :

  • User’s browser language
  • Location

  This can enable you to tailor your approach to specific demographics, improving the performance of your marketing campaigns.

  Technographic segments

  Create segments in Matomo based on people’s technographics. 

  For example :

  • Web browser being used (i.e., Chrome, Safari, Firefox, etc.)
  • Device type (i.e., smartphone, tablet, desktop)

  This can inform how to optimise your website based on users’ technology preferences, enhancing the effectiveness of your website.

  Interaction based segments

  Create segments in Matomo based on interactions. 

  For example :

  • Events (i.e., when someone clicks a specific URL on your website)
  • Goals (i.e., when someone stays on your site for a certain period)

  Insights from this can empower you to fine-tune your content and user experience for increasing conversion rates.

  

  Campaign-based segments

  Create segments in Matomo based on campaigns. 

  For example :

  • Visitors arriving from specific traffic sources
  • Visitors arriving from specific advertising campaigns

  With these insights, you can assess the performance of your marketing efforts, optimise your ad spend and make data-driven decisions to enhance your campaigns for better results.

  Ecommerce segments

  Create segments in Matomo based on ecommerce. 

  For example :

  • Visitors who purchased vs. those who didn’t
  • Visitors who purchased a specific product

  This allows you to refine your website and marketing strategy for increased conversions and revenue.

  Leverage Matomo for your segmentation analytics

  By now, you can see the power of segmentation analytics and how they can be used to understand your customers and website visitors better. By breaking down your audience into groups, you’ll be able to gain insights into those segments to know how to serve them better with improved messaging and relevant products.

  If you’re ready to begin using segmentation analytics on your website, try Matomo. Start your 21-day free trial now — no credit card required.

  Matomo is an ideal choice for marketers looking for an easy-to-use, out-of-the-box web analytics solution that delivers accurate insights while keeping privacy and compliance at the forefront.

• Date and segment comparison feature

  31 octobre 2019, par Matomo Core Team — Analytics Tips, Development

  Get a clearer picture with the date and segment comparison feature

  What can you do with it ? What are the benefits ?

  Make informed decisions faster by easily comparing different segments and dates with each other.

  Compare report data for multiple segments next to each other

  

  Directly compare the behaviour of visitors from different segments e.g. customers with accounts vs. customers without accounts. Segment comparisons are a powerful way to compare different audience ; learn which ones perform better ; and in what way their actions differ. 

  Compare report data for two time periods next to each other

  

  See how your website performs compared to the previous month/week/year. Including seeing trends over those periods. Say, your business always picks up at the same times within a year, or there’s a sag in business for every user segment over this year and the last except one.

  By being able to compare date ranges you are able to get a quick overview of trends and period to period performance. Has a campaign worked better in September than in October ? Get an instant look by having the side-by-side comparison in Matomo.

  What is it capable of ?

  It lets you ask the question, “What is different ?”

  If you look at reports you’ll only see how people behave overall and if you look at specific segments you’ll see how they behave at face value, however, if you compare data together you’ll be quickly informed on what makes them unique. This data is still there when you don’t use the comparison feature, it’s just buried. Comparing data highlights discrepancies and leads to important questions and answers.

  For example, perhaps some class of users have very low engagement on a specific day compared to the rest of your visitors, and perhaps those users are responsible for an outsized proportion of churn. 

  Who could benefit from it, and why ?

  Everyone can benefit from using it (and probably should use it). It’s yours to experiment with ! You shouldn’t feel restricted to only comparing between the current and last period, or having questions before you start comparing. Follow your instincts and see what pops out when data from different segments is laid out next to each other.

  Where can you find it in Matomo ?

  • Segment comparison is activated by the new icon in the segment selector

  

  • Date comparison can be found in the calendar section of Matomo

  

  • The list of active comparisons is visible at the top of the page for all pages that support comparison
  • Comparisons are visible in every report that supports comparing data, and reports that do not support it will display a message saying so

  How do you use it ?

  • To compare segments, click the icon in the segment selector
  • To compare periods, click the ‘compare’ checkbox in the period selector, then select what period you want to compare it against in the dropdown (previous period, previous year, or a custom range)
  • When comparisons are active, view your reports as normal

  Take it away !

  The comparison feature is a new tool from Matomo 3.12.0 that highlights discrepancies and differences in data that can lead to more clarity and understanding, so we’d encourage everyone to use it. 

  Try it out today in your Matomo and see the power behind this new data comparison mode !

• Investigating Steam for Linux

  1er mars 2013, par Multimedia Mike — Game Hacking

  Valve recently released the final, public version of their Steam client for Linux, and the Linux world rejoiced. At least, it probably did. The announcement was 2 weeks ago on Valentine’s Day and I had other things on my mind, so I missed any fanfare. When framed in this manner, the announcement timing becomes suspect– it’s as though Linux enthusiasts would have plenty of time that day or something.

  
  
  

  Taming the Frontier
  Speculation about a Linux Steam client had been kicking around for nearly as long as Steam has existed. However, sometime last year, the rumors became more substantive.

  I naturally wondered how to port something like Steam to Linux. I have some experience with trying to make a necessarily binary-only program that runs on Linux. I’m fairly well-versed in the assorted technical challenges that one might face when attempting such a feat. Because of this, whenever I hear rumors that a company might be entertaining the notion of porting a major piece of proprietary software to Linux, my instinctive reflex is, “What ?! Why, you fools ?! Save yourselves !”

  At least, that’s how it used to be. The proposal of developing a proprietary binary for Linux has been rendered considerably less insane by a few developments, for example :

  1. The rise of Ubuntu Linux as a quasi de facto standard for desktop Linux computing
  2. The increasing homogeneity in personal desktop computing technology

  What I would like to know is how the Steam client runs on Linux. Does it rely on any libraries being present on the system ? Or does it bring its own ? The latter is a trick that proprietary programs can use– transport all of the shared libraries that the main program binary depends upon, install them someplace out of the way on the filesystem, probably in /opt, and then make the main program a shell script which sets a preload path to rely on the known quantity libraries instead of the copies already on the system.

  Downloading and Installing the Client
  For this exercise, I installed x86_64 desktop Ubuntu 12.04 Linux on a l33t gaming rig that was totally top of the line about 5 years ago, and that someone didn’t want anymore and handed down to me recently. So it should be ideal for this project.

  At first, I was blown away– the Linux client is in a .deb package that is less than 2 MB large. I unpacked the steam.deb file and found a bunch of support libraries — mostly X11 and standard C/C++ runtimes. Just as I suspected. Still, I can’t believe how small the thing is. However, my amazement quickly abated when I actually ran Steam and saw this :

  
  
  

  So it turns out steam.db is just the installer program which immediately proceeds to download an additional 160+ MB of data. So there’s actually a lot more information to possibly sift through.
  
  Another component of the installation is to basically run a big ‘apt-get install’ command to make sure a bunch of required packages are installed :

  
  
  

  After all these installation steps, the client was ready to run. However, whenever I tried to do so, I got this dialog which would cause Steam to close when the dialog was dismissed.

  
  
  

  Not a huge deal ; later NVIDIA drivers are fairly straightforward to install on Ubuntu Linux. After a few minutes of downloading, installing and restarting X, Steam ran with minimal complaint (it still had some issue regarding the video drivers but didn’t seem to consider it a deal-breaker).

  Using Steam on Linux

  So here’s Steam running on Linux :

  
  
  

  If you have experience with using Steam on Windows or Mac, you might observe that it looks exactly the same. I don’t have a very expansive library of games (I only started using Steam because purchasing a few computer components a few years ago entitled me to some free Steam downloads of some of the games on the list in the screenshot). I didn’t really expect any of the games to have Linux versions yet, but it turns out that the indie darling FTL : Faster Than Light has been ported to Linux. FTL was a much-heralded Kickstarter success story and sounded like something I wanted to support. I purchased this from Steam shortly after its release last year and was able to download the Linux version at no additional cost with a single click.

  It runs natively on Linux (note the Ubuntu desktop window decorations) :

  
  
  

  You might notice from the main Steam client that, despite purchasing FTL about a 1/2 year ago and starting it up at least a 1/2 dozen times, I haven’t really invested a whole lot of time into it. I only managed to get about 2 minutes further this time :

  
  
  

  What can I say ? This game just bores me to tears. It’s frustrating because I know that this is one of the cool games that all real gamers are supposed to like, but I practically catch myself nodding off every time I try to run through the tutorial. It’s strange to think that I’ve invested far more time into games that offer considerably less stimulation. That’s probably because I had far more free time compared to gaming options during those times.

  But that’s neither here nor there. We’ll file this under “games that aren’t for me.” I’m glad that people like FTL and a little indie underdog has met with such success. And I’m pleased that Steam on Linux works. It’s native and the games are also native, which is all quite laudable (there was speculation that everything would just be running on top of a Wine layer).

  Deeper Analysis
  So I set out wondering how Steam was able to create a proprietary program that would satisfy a large enough cross-section of Linux users (i.e., on different platforms and distros). Answer : well, they didn’t, per the stated requirements. The installation is only tuned to work on Ubuntu 12.04. However, it works on both 32- and 64-bit platforms, the only 2 desktop CPU platforms that matter these days (unless ARM somehow makes inroads on the desktop). The Steam client is quite clearly an x86_32 binary– look at the terminal screenshot above and observe that it’s downloading all :i386 support libraries.

  The file /usr/bin/steam isn’t a binary but a launcher shell script (something you’ll also see if you investigate /usr/bin/firefox on a Linux system). Here’s an interesting tidbit :

  function detect_platform()
  
    # Maybe be smarter someday
    # Right now this is the only platform we have a bootstrap for, so hard-code it.
    echo ubuntu12_32
  
  

  I wager that it’s possible to get Steam running on other distributions, it probably just takes a little more effort (assuming that Steam doesn’t put too much effort into thwarting such attempts).

  As for the FTL game, it comes with binaries and libraries for both x86_32 and x86_64. So, good work to the dev team for creating and testing both versions. FTL also distributes versions of the libraries it expects to work with.

  I suspect that the Steam client overall is largely a WWW rendering engine underneath the covers. That would help explain how Valve is able to achieve such a consistent look and feel, not only across OS platforms, but also through a web browser. When I browse the Steam store through Google Chrome, it looks and feels exactly like the native desktop client. When I first thought of how someone could port Steam to Linux, I immediately wondered about how they would do the UI.

  A little Googling for “steam uses webkit” (just a hunch) confirms my hypothesis.