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• What Are Website KPIs (10 KPIs and Best Ways to Track Them)

  3 mai 2024, par Erin

  Trying to improve your website’s performance ?

  Have you ever heard the phrase, “What gets measured gets managed ?”

  To improve, you need to start crunching your numbers.

  The question is, what numbers are you supposed to track ?

  If you want to improve your conversions, then you need to track your website KPIs.

  In this guide, we’ll break down the top website KPIs you need to be tracking and how you can track them so you can double down on what’s working with your website (and ditch what’s not).

  Let’s begin.

  What are website KPIs ?

  Before we dive into website KPIs, let’s define “KPI.”

  A KPI is a key performance indicator.

  You can use this measurable metric to track progress toward a specific objective.

  A website KPI is a metric to track progress towards a specific website performance objective.

  

  Website KPIs help your business identify strengths and weaknesses on your website, activities you’re doing well (and those you’re struggling with).

  Web KPIs can give you and your team a target to reach with simple checkpoints to show you whether you’re on the right track toward your goals.

  By tracking website KPIs regularly, you can ensure your organisation performs consistently at a high level.

  Whether you’re looking to improve your traffic, leads or revenue, keeping a close eye on your website KPIs can help you reach your goals.

  10 Website KPIs to track

  If you want to improve your site’s performance, you need to track the right KPIs.

  While there are plenty of web analytics solutions on the market today, below we’ll cover KPIs that are automatically tracked in Matomo (and don’t require any configuration).

  Here are the top 10 website KPIs you need to track to improve site performance and grow your brand :

  1. Pageviews

  Website pageviews are one of the most important KPIs to track.

  What is it exactly ?

  It’s simply the number of times a specific web page has been viewed on your site in a specific time period.

  For example, your homepage might have had 327 pageviews last month, and only 252 this month. 

  This is a drop of 23%. 

  A drop in pageviews could mean your search engine optimisation or traffic campaigns are weakening. Alternatively, if you see pageviews rise, it could mean your marketing initiatives are performing well.

  High or low pageviews could also indicate potential issues on specific pages. For example, your visitors might have trouble finding specific pages if you have poor website structure.

  

  2. Average time on page

  Now that you understand pageviews, let’s talk about average time on page.

  This is simple : it’s the average amount of time your visitors spend on a particular web page on your site.

  This isn’t the average time they spend on your website but on a specific page.

  If you’re finding that you’re getting steady traffic to a specific web page, but the average time on the page is low, it may mean the content on the page needs to be updated or optimised.

  Tracking your average time on page is important, as the longer someone stays on a page, the better the experience.

  This isn’t a hard and fast rule, though. For specific types of content like knowledge base articles, you may want a shorter period of time on page to ensure someone gets their answer quickly.

  3. Bounce rate

  Bounce rate sounds fun, right ?

  Well, it’s not usually a good thing for your website.

  A bounce rate is how many users entered your website but “bounced” away without clicking through to another page.

  Your bounce rate is a key KPI that helps you determine the quality of your content and the user experience on individual pages.

  You could be getting plenty of traffic to your site, but if the majority are bouncing out before heading to new pages, it could mean that your content isn’t engaging enough for your visitors.

  Remember, like average time on page, your bounce rate isn’t a black-and-white KPI.

  A higher bounce rate may mean your site visitors got exactly what they needed and are pleased.

  But, if you have a high bounce rate on a product page or a landing page, that is a sign you need to optimise the page.

  4. Exit rate

  Bounce rate is the percentage of people who left the website after visiting one page.

  Exit rate, on the other hand, is the percentage of website visits that ended on a specific page.

  For example, you may find that a blog post you wrote has a 19% exit rate and received 1,000 visits that month. This means out of the 1,000 people who viewed this page, 190 exited after visiting it.

  On the other hand, you may find that a second blog post has 1,000 pageviews, but a 10% exit rate, with only 100 people leaving the site after visiting this page.

  What could this mean ?

  This means the second page did a better job keeping the person on your website longer. This could be because :

  • It had more engaging content, keeping the visitors’ interest high
  • It had better internal links to other relevant pieces of content
  • It had a better call to action, taking someone to another web page

  If you’re an e-commerce store and notice that your exit rate is higher on your product, cart or checkout pages, you may need to adjust those pages for better conversions.

  

  5. Average page load time

  Want to know another reason you may have a high exit rate or bounce rate on a page ?

  Your page load time.

  The average page load time is the average time it takes (in seconds) from the moment you click through to a page until it has fully rendered within your browser.

  In other words, it’s the time it takes after you click on a page for it to be fully functional.

  Your average load time is a crucial website KPI because it significantly impacts page performance and the user experience.

  How important is your page load time ?

  Nearly 53% of website visitors expect e-commerce pages to load in 3 seconds or less.

  You will likely lose visitors if your pages take too long to load.

  You could have the best content on a web page, but if it takes too long to load, your visitors will bounce, exit, or simply be frustrated.

  6. Conversions

  

  Conversions.

  It’s one of the most popular words in digital marketing circles.

  But what does it mean ?

  A conversion is simply the number of times someone takes a specific action on your website.

  For example, it could be wanting someone to :

  • Read a blog post
  • Click an external link
  • Download a PDF guide
  • Sign up to your email list
  • Comment on your blog post
  • Watch a new video you uploaded
  • Purchase a limited-edition product
  • Sign up for a free trial of your software

  To start tracking conversions, you need to first decide what your business goals are for your website.

  With Matomo, you can set up conversions easily through the Goals feature. Simply set up your website goals, and Matomo will automatically track the conversions towards that objective (as a goal completion).

  Simply choose what conversion you want to track, and you can analyse when conversions occur through the Matomo platform.

  7. Conversion rate

  

  Now that you know what a conversion is, it’s time to talk about conversion rate.

  This key website KPI will help you analyse your performance towards your goals.

  Conversion rate is simply the percentage of visitors who take a desired action, like completing a purchase, signing up for a newsletter, or filling out a form, out of the total number of visitors to your website or landing page.

  Understanding this percentage can help you plan your marketing strategy to improve your website and business performance.

  For instance, let’s say that 2% of your website visitors purchase a product on your digital storefront.

  Knowing this, you could tweak different levers to increase your sales.

  If your average order value is $50 and you get 100,000 visits monthly, you make about $100,000.

  Let’s say you want to increase your revenue.

  One option is to increase your traffic by implementing campaigns to increase different traffic sources, such as social media ads, search ads, organic social traffic, and SEO.

  If you can get your traffic to 120,000 visitors monthly, you can increase your revenue to $120,000 — an additional $20,000 monthly for the extra 20,000 visits.

  Or, if you wanted to increase revenue, you could ignore traffic growth and simply improve your website with conversion rate optimisation (CRO).

  CRO is the practice of making changes to your website or landing page to encourage more visitors to take the desired action.

  If you can get your conversion rate up to 2.5%, the calculation looks like this :

  100,000 visits x $50 average order value x 2.5% = $125,000/month.

  8. Average time spent on forms

  If you want more conversions, you need to analyse forms.

  Why ?

  Form analysis is crucial because it helps you pinpoint where users might be facing obstacles. 

  By identifying these pain points, you can refine the form’s layout and fields to enhance the user experience, leading to higher conversion rates.

  In particular, you should track the average time spent on your forms to understand which ones might be causing frustration or confusion. 

  The average time a visitor spends on a form is calculated by measuring the duration between their first interaction with a form field (such as when they focus on it) and their final interaction.

  Find out how Concrete CMS tripled their leads using Form Analytics.

  9. Play rate

  One often overlooked website KPI you need to be tracking is play rate.

  What is it exactly ?

  The percentage of visitors who click “play” on a video or audio media format on a specific web page.

  For example, if you have a video on your homepage, and 50 people watched it out of the 1,000 people who visited your website today, you have a play rate of 5%.

  Play rate lets you track whenever someone consumes a particular piece of audio or video content on your website, like a video, podcast, or audiobook.

  Not all web analytics solutions offer media analytics. However, Matomo lets you track your media like audio and video without the need for configuration, saving you time and upkeep.

  10. Actions per visit

  Another crucial website KPI is actions per visit.

  This is the average number of interactions a visitor has with your website during a single visit.

  For example, someone may visit your website, resulting in a variety of actions :

  • Downloading content
  • Clicking external links
  • Visiting a number of pages
  • Conducting specific site searches

  Actions per visit is a core KPI that indicates how engaging your website and content are.

  The higher the actions per visit, the more engaged your visitors typically are, which can help them stay longer and eventually convert to paying customers.

  Track your website KPIs with Matomo today

  Running a website is no easy task.

  There are dozens of factors to consider and manage :

  • Copy
  • Design
  • Performance
  • Tech integrations
  • And more

  But, to improve your website and grow your business, you must also dive into your web analytics by tracking key website KPIs.

  Managing these metrics can be challenging, but Matomo simplifies the process by consolidating all your core KPIs into one easy-to-use platform.

  As a privacy-friendly and GDPR-compliant web analytics solution, Matomo tracks 20-40% more data than other solutions. So you gain access to 100% accurate, unsampled insights, enabling confident decision-making.

  Join over 1 million websites that trust Matomo as their web analytics solution. Try it free for 21 days — no credit card required.

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• ffmpeg output with label 'v' does not exist in any defined filtler graph

  15 avril 2024, par David Royale

  Been trying to make this Python code work but it fails up until rendering the final part :

  


  import subprocess
import re
import os

def detect_idle_sections(video_path):
    command = ['ffmpeg', '-i', video_path, '-vf', 'select=\'gt(scene,0.1)\'', '-vsync', 'vfr', '-f', 'null', '-']
    output = subprocess.check_output(command, stderr=subprocess.STDOUT).decode('utf-8')
    
    idle_sections = []
    duration = 0.0
    for line in output.split('\n'):
        match = re.search(r'scene:(\d+)', line)
        if match:
            scene = int(match.group(1))
            if scene == 0:
                duration += 1.0 / 30  # Assuming 30 fps
            else:
                if duration > 0:
                    idle_sections.append((duration, duration - (1.0 / 30)))  # Duration and start time
                    duration = 0.0
    
    return idle_sections

def cut_idle_sections(video_path, idle_sections, output_path, total_duration):
    print("starting to cut things")
    filters = []
    start_time = 0.0
    for duration, _ in idle_sections:
        filters.append(f'[0:v]trim=start={start_time}:end={start_time + duration},setpts=PTS-STARTPTS[v{len(filters)}]')
        start_time += duration
    
    if start_time < total_duration:
        filters.append(f'[0:v]trim=start={start_time},setpts=PTS-STARTPTS[v{len(filters)}]')

    filter_str = ';'.join(filters)
    print("finished chopping")
    command = ['ffmpeg', '-i', video_path, '-filter_complex', filter_str, '-map', '[v]', output_path]
    subprocess.call(command)

def get_total_duration(video_path):
    print("prior getting time")
    command = ['ffprobe', '-v', 'error', '-show_entries', 'format=duration', '-of', 'default=noprint_wrappers=1:nokey=1', video_path]
    output = subprocess.check_output(command).decode('utf-8').strip()
    print("after getting time")
    return float(output)

input_file = r"C:\Users\D\Videos\2024-04-15 08-42-53.mkv"
output_file = r"C:\Users\D\Videos\output_video.mp4"

# Get full paths
input_path = os.path.abspath(input_file)
output_path = os.path.abspath(output_file)

total_duration = get_total_duration(input_path)
idle_sections = detect_idle_sections(input_path)
cut_idle_sections(input_path, idle_sections, output_path, total_duration)


  


  The error I am getting is :

  


  [out#0/mp4 @ 000001af60f9d3c0] Output with label 'v' does not exist in any defined filter graph, or was already used elsewhere.
Error opening output file C:\Users\D_era\Videos\output_video.mp4.
Error opening output files: Invalid argument


  


  The code is intended to cut "iddle" frames where frame a = frame b. I want to point out that I don't really care about audio, so it's just comparing if frames "A" through "H" are the same and keeping a and continuing with the rest of the video.

  


  Putting some comments to determine which part was successful and which broke, I found that the line failig is this :

  


  command = ['ffmpeg', '-i', video_path, '-filter_complex', filter_str, '-map', '[v]', output_path]


  


  and apparently is the -map part.

  


• Consent Mode v2 : Everything You Need to Know

  7 mai 2024, par Alex — Analytics Tips

  Confused about Consent Mode v2 and its impact on your website analytics ? You’re not the only one. 

  Google’s latest update has left many scratching their heads about data privacy and tracking. 

  In this blog, we’re getting straight to the point. We’ll break down what Consent Mode v2 is, how it works, and the impact it has.

  What is Consent Mode ?

  What exaclty is Google Consent Mode and why is there so much buzz surrounding it ? This question has been frustrating analysts and marketers worldwide since the beginning of this year. 

  Consent Mode is the solution from Google designed to manage data collection on websites in accordance with user privacy requirements.

  This mode enables website owners to customise how Google tags respond to users’ consent status for cookie usage. At its core, Consent Mode adheres to privacy regulations such as GDPR in Europe and CCPA in California, without significant loss of analytical data.

  

  How does Consent Mode work ?

  Consent Mode operates by adjusting the behaviour of tags on a website depending on whether consent for cookie usage is provided or not. If a user does not consent to the use of analytical or advertising cookies, Google tags automatically switch to collecting a limited amount of data, ensuring privacy compliance.

  This approach allows for continued valuable insights into website traffic and user behavior, even if users opt out of most tracking cookies.

  What types of consent are available in Consent Mode ?

  As of 6 March 2024, Consent Mode v2 has become the current standard (and in terms of utilising Google Advertising Services, practically mandatory), indicating the incorporation of four consent types :

  1. ad_storage : allows for the collection and storage of data necessary for delivering personalised ads based on user actions.
  2. ad_user_data : pertains to the collection and usage of data that can be associated with the user for ad customisation and optimisation.
  3. ad_personalization : permits the use of user data for ad personalisation and providing more relevant content.
  4. analytics_storage : relates to the collection and storage of data for analytics, enabling websites to analyse user behaviour and enhance user experience.

  Additionally, in Consent Mode v2, there are two modes :

  1. Basic Consent Mode : in which Google tags are not used for personalised advertising and measurements if consent is not obtained.
  2. Advanced Consent Mode : allows Google tags to utilise anonymised data for personalised advertising campaigns and measurements, even if consent is not obtained.

  What is Consent Mode v2 ? (And how does it differ from Consent Mode v1 ?)

  Consent Mode v2 is an improved version of the original Consent Mode, offering enhanced customisation capabilities and better compliance with privacy requirements. 

  The new version introduces additional consent configuration parameters, allowing for even more precise control over which data is collected and how it’s used. The key difference between Consent Mode v2 and Consent Mode v1 lies in more granular consent management, making this tool even more flexible and powerful in safeguarding personal data.

  In Consent Mode v2, the existing markers (ad_storage and analytics_storage) are accompanied by two new markers :

  1. ad_user_data – does the user agree to their personal data being utilized for advertising purposes ?
  2. ad_personalization – does the user agree to their data being employed for remarketing ?

  In contrast to ad_storage and analytics_storage, these markers don’t directly affect how the tags operate on the site itself. 

  They serve as additional directives sent alongside the pings to Google services, indicating how user data can be utilised for advertising purposes.

  While ad_storage and analytics_storage serve as upstream qualifiers for data (determining which identifiers are sent with the pings), ad_user_data and ad_personalization serve as downstream instructions for Google services regarding data processing.

  How is the implementation of Consent Mode v2 going ?

  The implementation of Consent Mode v2 is encountering some issues and bugs (as expected). The most important thing to understand :

  1. Advanced Consent Mode v2 is essential if you have traffic and campaigns with Google Ads in the European Union.
  2. If you don’t have substantially large traffic, enabling Advanced Consent Mode v2 will likely result in a traffic drop in GA4 – because this version of consent mode (unlike the basic one) applies behavioural modelling to users who haven’t accepted the use of cookies. And modelling the behaviour requires time.

  The aspect of behavioural modelling in Consent Mode v2 implies the following : the data of users who have declined tracking options begin to be modelled using machine learning. 

  However, training the model requires a suitable data volume. As the Google’s documentation states :

  The property should collect at least 1,000 events per day with analytics_storage=’denied’ for at least 7 days. The property should have at least 1,000 daily users submitting events with analytics_storage=’granted’ for at least 7 of the previous 28 days.

  Largely due to this, the market’s response to the Consent Mode v2 implementation was mixed : many reported a significant drop in traffic in their GA4 and Google Ads reports upon enabling the Advanced mode. Essentially, a portion of the data was lost because Google’s models lacked enough data for training. 

  And from the very beginning of implementation, users regularly report about a few examples of that scenario. If your website doesn’t have enough traffic for behaviour modelling, after Consent Mode v2 switching you will face significant drop in your traffic in Google Ads and GA4 reports. There are a lot of cases of observing 90-95% drop in metrics of users and sessions.

  In a nutshell, you should be prepared for significant data losses if you are planning to switch to Google Consent Mode v2.

  How does Consent Mode v2 impact web analytics ? 

  The transition to Consent Mode v2 alters the methods of user data collection and processing. The main concerns arise from the potential loss of accuracy and completeness of analytical data due to restrictions on the use of cookies and other identifiers when user consent is absent. 

  With Google Consent Mode v2, the data of visitors who have not agreed to tracking will be modelled and may not accurately reflect your actual visitors’ behaviours and actions. So as an analyst or marketer, you will not have true insights into these visitors and the data acquired will be more generalised and less accurate.

  Google Consent Mode v2 appears to be a kind of compromise band-aid solution. 

  It tries to solve these issues by using data modelling and anonymised data collection. However, it’s critical to note that there are specific limitations inherent to the modelling mechanism.

  This complicates the analysis of visitor behavior, advertising campaigns, and website optimisation, ultimately impacting decision-making and resulting in poor website performance and marketing outcomes.

  Wrap up

  Consent Mode v2 is a mechanism of managing Google tag operations based on user consent settings. 

  It’s mandatory if you’re using Google’s advertising services, and optional (at least for Advanced mode) if you don’t advertise on Google Ads. 

  There are particular indications that this technology is unreliable from a GDPR perspective. 

  Using Google Consent Mode will inevitably lead to data losses and inaccuracies in its analysis. 

  In other words, it in some sense jeopardises your business.