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• Web Analytics : The Quick Start Guide

  25 janvier 2024, par Erin

  You’ve spent ages carefully designing your website, crafting copy to encourage as many users as possible to purchase your product. 

  But they aren’t. And you don’t know why. 

  The good news is you don’t have to remain in the dark. Collecting and analysing web analytics lets you understand how your users behave on your site and why they aren’t converting. 

  But before you can do that, you need to know what those metrics and KPIs mean. That’s why this article is taking things back to basics. Below, we’ll show you which metrics to track, what they mean and how to choose the best web analytics platform. 

  What is web analytics ?

  Web analytics is the process of collecting, analysing and reporting website data to understand how users behave on your website. Web analytics platforms like Matomo collect this data by adding a code line to every site page. 

  Why is it important to track web analytics ?

  There are plenty of reasons you should start tracking web analytics, including the following :

  

  Analyse user behaviour

  Being able to analyse user behaviour is the most important reason to track website analytics. After all, you can’t improve your website’s conversion rate if you don’t know what users do on your site.

  A web analytics platform can show you how users move around your site, the links they click on and the forms they fill in. 

  Improve site experience

  Web analytics is a fantastic way to identify issues and find areas where your site could improve. You could look at your site’s exit pages, for example, and see why so many users leave your site when viewing one of these pages and what you can do to fix it.

  It can also teach you about your user’s preferences so you can improve the user experience in the future. Maybe they always click a certain type of button or prefer one page’s design over another. Whatever the case, you can use the data to make your site more user-friendly and increase conversions.

  Boost marketing efforts

  Web analytics is one of the best ways to understand your marketing efforts and learn how to improve them.

  A good platform can collect valuable data about your marketing campaigns, including :

  • Where users came from
  • What actions these users take on your site
  • Which traffic sources create the most conversions

  This information can help you decide which marketing campaigns send the best users to your site and generate the highest ROI. 

  Make informed decisions

  Ultimately, web analytics simplifies decision-making for your website and marketing efforts by relying on concrete data instead of guesswork.

  Rather than wonder why users aren’t adding products to their shopping cart or signing up for your newsletter, you can analyse how they behave and use that information to hypothesise how you can improve conversions. Web analytics will even give you the data to confirm whether you were right or wrong. 

  What are the key metrics you should track ?

  Getting your head around web analytics means knowing the most important metrics to track. Below are seven key metrics and how to track them using Matomo. 

  Traffic

  Traffic is the number of people visiting your website over a period of time. It is the lifeblood of your website since the more visits your site receives, the more revenue it stands to generate.

  However, simply having a high volume of visitors does not guarantee substantial revenue. To maximise your success, focus on attracting your ideal customers and generating quality traffic from those who are most likely to engage with your offerings.

  Ideally, you should be seeing an upward trend in traffic over time though. The longer your website has been published and the more quality and targeted content you create, the more traffic you should receive. 

  Matomo offers multiple ways to check your website’s traffic :

  • Visits log report
  • Real-time visitor map
  • Visits in real-time report

  The visits log report in Matomo is perfect if you want a granular view of your visitors.

  

  It shows you each user session and get a detailed picture of each user, including :

  • Their geographic location
  • The number of actions they took
  • How they found your site
  • The length of time they stayed
  • Their device type
  • What browser they are using
  • The keyword they used to find your site

  Traffic sources

  Traffic sources show how users access your website. They can enter via a range of traffic sources, including search engines, email and direct visits, for instance.

  Matomo has five default traffic source types :

  • Search engine – visitors from search platforms (like Google, Bing, etc.)
  • Direct traffic – individuals who directly type your website’s URL into their browser or have it bookmarked, bypassing search engines or external links
  • Websites – visits from other external sites
  • Campaigns – traffic resulting from specific marketing initiatives (like a newsletter or ad campaign, for instance)
  • Social networks  – visitors who access your website through various social media platforms (such as Facebook, LinkedIn, Instagram. etc.)

  But each of these can be broken into more granular sources. Take organic traffic from search engines, for example :

  

  Matomo tracks visits from each search engine, showing you how many visits you had in total, how many actions those visitors took, and the average amount of time those visitors spent on your site. 

  You can even integrate Google, Bing and Yahoo search consoles to monitor keyword performance and enhance your search engine optimisation efforts.

  Pageviews

  Whenever a browser loads a page, your web analytics tool records a pageview. This term, pageview, represents the count of unique times a page on your website is loaded.

  You can track pageviews in Matomo by opening the Pages tab in the Behaviour section of the main navigation. 

  

  You can quickly see your site’s most visited pages in this report in Matomo. 

  Be careful of deriving too much meaning from pageviews. Just because a page has lots of views, doesn’t necessarily mean it’s quality or valuable. There are a couple of reasons for this. First, the page might be confusing, so users have to keep revisiting it to understand the content. Second, it could be the default page most visitors land on when they enter your site, like the homepage. 

  While pageviews offer insights, it’s important to dig deeper into user behaviour and other metrics to truly gauge a page’s importance and impact.

  Average time on page

  Time on page is the amount of time users spend on the page on average. You can see average time on page in Matomo’s page analytics report.

  A low time on page score isn’t necessarily a bad thing. Users will naturally spend less time on gateway pages and checkout pages. A short time spent on checkout pages, especially if users are successfully completing their transactions, indicates that the checkout process is easy and seamless.

  Conversely, a longer time on blog posts is a positive indicator. It suggests that readers are genuinely engaged with the content.

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  Returning visitors

  Returning visitors measures the number of people who visit your site more than once. It can be expressed as a number or a percentage. 

  While some analytics tools only show returning visitors as a percentage, Matomo lets you learn more about each of them in the Visitor profile report. 

  

  This report offers a full summary of a user’s previous actions, including :

  • How many times they’ve visited your site
  • The pages they viewed on each visit
  • Where they visited from
  • The devices they used
  • How quickly pages loaded

  When people keep coming back to a website, it’s usually a positive sign and means they like the service, content or products. But, it depends on the type of website. If it’s the kind of site where people make one-off purchases, the focus might not be on getting visitors to return. For a site like this, a high number of returning visitors could indicate that the website is confusing or difficult to use. 

  It’s all about the context – different websites have different goals, and it’s important to keep this in mind when analysing your site.

  Conversions

  A conversion is when a user takes a desired action on your website. This could be :

  • Making a purchase
  • Subscribing to your newsletter
  • Signing up for a webinar

  You can track virtually any action as a conversion in Matomo by setting goals and analysing the goals report.

  

  As you can see in the screenshot above, Matomo shows your conversions plotted over time. You can also see your conversion rate to get a complete picture and assign a value to each conversion to calculate how much revenue each conversion generates. 

  Bounce rate

  A visitor bounces when they leave your website without taking an action or visiting another page. 

  Typically, you want bounce rate to be low because it means people are engaged with your site and more likely to convert. However, in some cases, a high bounce rate isn’t necessarily bad. It might mean that visitors found what they needed on the first page and didn’t feel the need to look further. 

  The impact of bounce rate depends on your website’s purpose and goals.

  You can view your website’s bounce rate using Matomo’s page analytics report — the same report that shows pageviews.

  Try Matomo for Free

  Get the web insights you need, without compromising data accuracy.

  Start for free

  No credit card required

  Web analytics best practices

  You should follow several best practices to get the most from website analytics data. 

  Choose metrics that align with your goals

  Only some metrics your analytics platform tracks will be relevant to your business. So don’t waste time analysing all of them.

  Instead, focus on the ones that matter most to your business. A marketer for an e-commerce store, for example, might focus on conversion-related metrics like conversion rate and total number of transactions. They might also want to look at campaign-related metrics, like traffic sources and bounce rates, so they can optimise paid ad campaigns accordingly. 

  A marketer looking to improve their site’s SEO, on the other hand, will want to track SEO web analytics like bounce rate and broken links.

  Add context to your data

  Don’t take your data at face value. There could be dozens of factors that impact how visitors access and use your site — many of which are outside your control. 

  For example, you may think an update to your site has sent your conversions crashing when, in reality, a Google algorithm update has negatively impacted your search traffic.

  Adding annotations within Matomo can provide invaluable context to your data. These annotations can be used to highlight specific events, changes or external factors that might influence your website metrics.

  

  By documenting significant occurrences, such as website updates, marketing campaigns or algorithm changes, you create a timeline that helps explain fluctuations in your data.

  Go further with advanced web analytics features

  It’s clear that a web analytics platform is a necessary tool to understand your website’s performance.

  However, if you want greater confidence in decision-making, quicker insights and better use of budget and resources, you need an advanced solution with behavioural analytics features like heatmaps, A/B testing and session recordings. 

  Most web analytics solutions don’t offer these advanced features, but Matomo does, so we’ll be showcasing Matomo’s behavioural analytics features.

  Now, if you don’t have a Matomo account, you can try it free for 21-days to see if it’s the right tool for you.

  

  A heatmap, like the example above, makes it easy to discover where your users pay attention, which part of your site they have problems with, and how they convert. It adds a layer of qualitative data to the facts offered by your web analytics tool.

  Similarly, session recordings will offer you real-time playbacks of user interactions, helping you understand their navigation patterns, identify pain points and gain insights into the user experience.

  Then you can run experiments bu using A/B testing to compare different versions of your website or specific elements, allowing you to make informed decisions based on actual user preferences and behaviour. For instance, you can compare different headlines, images, page layouts or call-to-action buttons to see which resonates better with your audience. 

  Together, these advanced features will give you the confidence to optimise your website, improve user satisfaction and make data-driven decisions that positively impact your business.

  Try Matomo for Free

  Get the web insights you need, without compromising data accuracy.

  Start for free

  No credit card required

  How to choose a web analytics tool

  A web analytics tool is the best way to track the above metrics. Choose the best one for your company by following the steps below. 

  Look for the right features

  Most popular web analytics platforms, like Google Analytics, will offer the same core features like tracking website traffic, monitoring conversions and generating reports. 

  But it’s the added features that set great tools apart. Do you need specific tools to measure the performance of your e-commerce store, for example ? What about paid ad performance, A/B testing or form analytics ?

  By understanding exactly what you need from an analytics platform, you can make an informed choice. 

  Think about data accuracy

  Data accuracy is one of the biggest issues with analytics tools. Many users block cookies or opt out of tracking, making it difficult to get a clear picture of user behaviour — and meaning that you have to think about how your user data will be collected with your chosen platform.

  Google Analytics, for instance, uses data sampling to make assumptions about traffic levels rather than relying on accurate data. This can lead to inaccurate reports and false conclusions. 

  It’s why Matomo doesn’t use data sampling and provides 100% accurate data. 

  Understand how you’ll deal with data privacy

  Data privacy is another big concern for analytics users. Several major analytics platforms aren’t compatible with regional data privacy laws like GDPR, which can impact your ability to collect data in these regions. 

  It’s why many companies trust privacy-focused analytics tools that abide by regulations without impacting your ability to collect data. Matomo is a market leader in this respect and is one of the few web analytics tools that the Centre for Data Privacy Protection in France has said is exempt from tracking consent requirements.

  Many government agencies across Europe, Asia, Africa and North America, including organisations like the United Nations and European Commission, rely on Matomo for web analytics.

  Conclusion

  Web analytics is a powerful tool that helps you better understand your users, improve your site’s performance and boost your marketing efforts. 

  If you want a platform that offers advanced features, 100% accurate data and protects your users’ privacy, then look no further than Matomo. 

  Try Matomo free for 21 days, no credit card required. 

  Try Matomo for Free

  21 day free trial. No credit card required.

  
  

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• Marketing Cohort Analysis : How To Do It (With Examples)

  12 janvier 2024, par Erin

  The better you understand your customers, the more effective your marketing will become. 

  The good news is you don’t need to run expensive focus groups to learn much about how your customers behave. Instead, you can run a marketing cohort analysis using data from your website analytics.

  A marketing cohort groups your users by certain traits and allows you to drill down to discover why they take the actions on your website they do. 

  In this article, we’ll explain what a marketing cohort analysis is, show you what you can achieve with this analytical technique and provide a step-by-step guide to pulling it off. 

  What is cohort analysis in marketing ?

  A marketing cohort analysis is a form of behavioural analytics where you analyse the behavioural patterns of users who share a similar trait to better understand their actions. 

  These shared traits could be anything like the date they signed up for your product, users who bought your service through a paid ad or email subscribers from the United Kingdom.

  It’s a fantastic way to improve your marketing efforts, allowing you to better understand complex user behaviours, personalise campaigns accordingly and improve your ROI. 

  You can run marketing analysis using an analytics platform like Google Analytics or Matomo. With these platforms, you can measure how cohorts perform using traffic, engagement and conversion metrics.

  

  There are two types of cohort analysis : acquisition-based cohort analysis and behavioural-based cohort analysis.

  Acquisition-based cohort analysis

  An acquisition-based cohort divides users by the date they purchased your product or service and tracks their behaviour afterward. 

  For example, one cohort could be all the users who signed up for your product in November. Another could be the users who signed up for your product in October. 

  You could then run a cohort analysis to see how the behaviour of the two cohorts differed. 

  Did the November cohort show higher engagement rates, increased frequency of visits post-acquisition or quicker conversions compared to the October cohort ? Analysing these cohorts can help with refining marketing strategies, optimising user experiences and improving retention and conversion rates.

  As you can see from the example, acquisition-based cohorts are a great way to track the initial acquisition and how user behaviour evolves post-acquisition.

  Behavioural-based cohort analysis

  A behavioural-based cohort divides users by their actions on your site. That could be their bounce rate, the number of actions they took on your site, their average time on site and more.

  

  Behavioural cohort analysis gives you a much deeper understanding of user behaviour and how they interact with your website.

  What can you achieve with a marketing cohort analysis ?

  A marketing cohort analysis is a valuable tool that can help marketers and product teams achieve the following goals :

  

  Understand which customers churn and why

  Acquisition and behavioural cohort analyses help marketing teams understand when and why customers leave. This is one of the most common goals of a marketing cohort analysis. 

  Learn which customers are most valuable

  Want to find out which channels create the most valuable customers or what actions customers take that increase their loyalty ? You can use a cohort analysis to do just that. 

  For example, you may find out you retain users who signed up via direct traffic better than those that signed up from an ad campaign. 

  Discover how to improve your product

  You can even use cohort analysis to identify opportunities to improve your website and track the impact of your changes. For example, you could see how visitor behaviour changes after a website refresh or whether visitors who take a certain action make more purchases. 

  Find out how to improve your marketing campaign

  A marketing cohort analysis makes it easy to find out which campaigns generate the best and most profitable customers. For example, you can run a cohort analysis to determine which channel (PPC ads, organic search, social media, etc.) generates customers with the lowest churn rate. 

  If a certain ad campaign generates the low-churn customers, you can allocate a budget accordingly. Alternatively, if customers from another ad campaign churn quickly, you can look into why that may be the case and optimise your campaigns to improve them. 

  Measure the impact of changes

  You can use a behavioural cohort analysis to understand what impact changes to your website or product have on active users. 

  If you introduced a pricing page to your website, for instance, you could analyse the behaviour of visitors who interacted with that page compared to those who didn’t, using behavioural cohort analysis to gauge the impact of these website changes on engagemen or conversions.

  The problem with cohort analysis in Google Analytics

  Google Analytics is often the first platform marketers turn to when they want to run a cohort analysis. While it’s a free solution, it’s not the most accurate or easy to use and users often encounter various issues. 

  For starters, Google Analytics can’t process user visitor data if they reject cookies. This can lead to an inaccurate view of traffic and compromise the reliability of your insights.

  In addition, GA is also known for sampling data, meaning it provides a subset rather than the complete dataset. Without the complete view of your website’s performance, you might make the wrong decisions, leading to less effective campaigns, missed opportunities and difficulties in reaching marketing goals.

  How to analyse cohorts with Matomo

  Luckily, there is an alternative to Google Analytics. 

  As the leading open-source web analytics solution, Matomo offers a robust option for cohort analysis. With its 100% accurate data, thanks to the absence of sampling, and its privacy-friendly tracking, users can rely on the data without resorting to guesswork. It is a premium feature included with our Matomo Cloud or available to purchase on the Matomo Marketplace for Matomo On-Premise users.

  Below, we’ll show how you can run a marketing cohort analysis using Matomo.

  Set a goal

  Setting a goal is the first step in running a cohort analysis with any platform. Define what you want to achieve from your analysis and choose the metrics you want to measure. 

  For example, you may want to improve your customer retention rate over the first 90 days. 

  Define cohorts

  Next, create cohorts by defining segmentation criteria. As we’ve discussed above, this could be acquisition-based or behavioural. 

  Matomo makes it easy to define cohorts and create charts. 

  In the sidebar menu, click Visitors > Cohorts. You’ll immediately see Matomo’s standard cohort report (something like the one below).

  

  In the example above, we’ve created cohorts by bounce rate. 

  You can view cohorts by weekly, monthly or yearly periods using the date selector and change the metric using the dropdown. Other metrics you can analyse cohorts by include :

  • Unique visitors
  • Return visitors
  • Conversion rates
  • Revenue
  • Actions per visit

  Change the data selection to create your desired cohort, and Matomo will automatically generate the report. 

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  Analyse your cohort chart

  Cohort charts can be intimidating initially, but they are pretty easy to understand and packed with insights. 

  Here’s an example of an acquisition-based cohort chart from Matomo looking at the percentage of returning visitors :

  

  Cohorts run vertically. The oldest cohort (visitors between February 13 – 19) is at the top of the chart, with the newest cohort (April 17 – 23) at the bottom. 

  The period of time runs horizontally — daily in this case. The cells show the corresponding value for the metric we’re plotting (the percentage of returning visitors). 

  For example, 98.69% of visitors who landed on your site between February 13 – 19, returned two weeks later. 

  Usually, running one cohort analysis isn’t enough to identify a problem or find a solution. That’s why comparing several cohort analyses or digging deeper using segmentation is important.

  Segment your cohort chart

  Matomo lets you dig deeper by segmenting each cohort to examine their behaviour’s specifics. You can do this from the cohort report by clicking the segmented visitor log icon in the relevant row.

  

  

  Segmenting cohorts lets you understand why users behave the way they do. For example, suppose you find that users you purchased on Black Friday don’t return to your site often. In that case, you may want to rethink your offers for next year to target an audience with potentially better customer lifetime value. 

  Start using Matomo for marketing cohort analysis

  A marketing cohort analysis can teach you a lot about your customers and the health of your business. But you need the right tools to succeed. 

  Matomo provides an effective and privacy-first way to run your analysis. You can create custom customer segments based on almost anything, from demographics and geography to referral sources and user behaviour. 

  Our custom cohort analysis reports and colour-coded visualisations make it easy to analyse cohorts and spot patterns. Best of all, the data is 100% accurate. Unlike other web analytics solution or cohort analysis tools, we don’t sample data. 

  Find out how you can use Matomo to run marketing cohort analysis by trialling us free for 21 days. No credit card required.

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• Why does every encoded frame's size increase after I had use to set one frame to be key in intel qsv of ffmpeg

  22 avril 2021, par TONY

  I used intel's qsv to encode h264 video in ffmpeg. My av codec context settings is like as below :

  


   m_ctx->width = m_width;
    m_ctx->height = m_height;
    m_ctx->time_base = { 1, (int)fps };
    m_ctx->qmin = 10;
    m_ctx->qmax = 35;
    m_ctx->gop_size = 3000;
    m_ctx->max_b_frames = 0;
    m_ctx->has_b_frames = false;
    m_ctx->refs = 2;
    m_ctx->slices = 0;
    m_ctx->codec_id = m_encoder->id;
    m_ctx->codec_type = AVMEDIA_TYPE_VIDEO;
    m_ctx->pix_fmt = m_h264InputFormat;
    m_ctx->compression_level = 4;
    m_ctx->flags &= ~AV_CODEC_FLAG_CLOSED_GOP;
    AVDictionary *param = nullptr;
    av_dict_set(&param, "idr_interval", "0", 0);
    av_dict_set(&param, "async_depth", "1", 0);
    av_dict_set(&param, "forced_idr", "1", 0);


  


  and in the encoding, I set the AVFrame to be AV_PICTURE_TYPE_I when key frame is needed :

  


    if(key_frame){
        encodeFrame->pict_type = AV_PICTURE_TYPE_I;
    }else{
        encodeFrame->pict_type = AV_PICTURE_TYPE_NONE;
    }
    avcodec_send_frame(m_ctx, encodeFrame);
    avcodec_receive_packet(m_ctx, m_packet);
   std::cerr<<"packet size is "<size<<",is key frame "<code>

  


  The strange phenomenon is that if I had set one frame to AV_PICTURE_TYPE_I, then every encoded frame's size after the key frame would increase. If I change the h264 encoder to x264, then it's ok.

  


  The packet size is as below before I call "encodeFrame->pict_type = AV_PICTURE_TYPE_I" :

  


  packet size is 26839
packet size is 2766
packet size is 2794
packet size is 2193
packet size is 1820
packet size is 2542
packet size is 2024
packet size is 1692
packet size is 2095
packet size is 2550
packet size is 1685
packet size is 1800
packet size is 2276
packet size is 1813
packet size is 2206
packet size is 2745
packet size is 2334
packet size is 2623
packet size is 2055


  


  If I call "encodeFrame->pict_type = AV_PICTURE_TYPE_I", then the packet size is as below :

  


  packet size is 23720,is key frame 1
packet size is 23771,is key frame 0
packet size is 23738,is key frame 0
packet size is 23752,is key frame 0
packet size is 23771,is key frame 0
packet size is 23763,is key frame 0
packet size is 23715,is key frame 0
packet size is 23686,is key frame 0
packet size is 23829,is key frame 0
packet size is 23774,is key frame 0
packet size is 23850,is key frame 0