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• Google Optimize vs Matomo A/B Testing : Everything You Need to Know

  17 mars 2023, par Erin — Analytics Tips

  Google Optimize is a popular A/B testing tool marketers use to validate the performance of different marketing assets, website design elements and promotional offers. 

  But by September 2023, Google will sunset both free and paid versions of the Optimize product. 

  If you’re searching for an equally robust, but GDPR compliant, privacy-friendly alternative to Google Optimize, have a look at Matomo A/B Testing. 

  Integrated with our analytics platform and conversion rate optimisation (CRO) tools, Matomo allows you to run A/B and A/B/n tests without any usage caps or compromises in user privacy.

  Disclaimer : Please note that the information provided in this blog post is for general informational purposes only and is not intended to provide legal advice. Every situation is unique and requires a specific legal analysis. If you have any questions regarding the legal implications of any matter, please consult with your legal team or seek advice from a qualified legal professional.

  Google Optimize vs Matomo : Key Capabilities Compared 

  This guide shows how Matomo A/B testing stacks against Google Optimize in terms of features, reporting, integrations and pricing.

  Supported Platforms 

  Google Optimize supports experiments for dynamic websites and single-page mobile apps only. 

  If you want to run split tests in mobile apps, you’ll have to do so via Firebase — Google’s app development platform. It also has a free tier but paid usage-based subscription kicks in after your product(s) reaches a certain usage threshold. 

  Google Optimize also doesn’t support CRO experiments for web or desktop applications, email campaigns or paid ad campaigns.Matomo A/B Testing, in contrast, allows you to run experiments in virtually every channel. We have three installation options — using JavaScript, server-side technology, or our mobile tracking SDK. These allow you to run split tests in any type of web or mobile app (including games), a desktop product, or on your website. Also, you can do different email marketing tests (e.g., compare subject line variants).

  A/B Testing 

  A/B testing (split testing) is the core feature of both products. Marketers use A/B testing to determine which creative elements such as website microcopy, button placements and banner versions, resonate better with target audiences. 

  You can benchmark different versions against one another to determine which variation resonates more with users. Or you can test an A version against B, C, D and beyond. This is called A/B/n testing. 

  Both Matomo A/B testing and Google Optimize let you test either separate page elements or two completely different landing page designs, using redirect tests. You can show different variants to different user groups (aka apply targeting criteria). For example, activate tests only for certain device types, locations or types of on-site behaviour. 

  The advantage of Matomo is that we don’t limit the number of concurrent experiments you can run. With Google Optimize, you’re limited to 5 simultaneous experiments. Likewise, 

  Matomo lets you select an unlimited number of experiment objectives, whereas Google caps the maximum choice to 3 predefined options per experiment. 

  Objectives are criteria the underlying statistical model will use to determine the best-performing version. Typically, marketers use metrics such as page views, session duration, bounce rate or generated revenue as conversion goals. 

  

  Multivariate testing (MVT)

  Multivariate testing (MVT) allows you to “pack” several A/B tests into one active experiment. In other words : You create a stack of variants to determine which combination drives the best marketing outcomes. 

  For example, an MVT experiment can include five versions of a web page, where each has a different slogan, product image, call-to-action, etc. Visitors are then served with a different variation. The tracking code collects data on their behaviours and desired outcomes (objectives) and reports the results.

  MVT saves marketers time as it’s a great alternative to doing separate A/B tests for each variable. Both Matomo and Google Optimize support this feature. However, Google Optimize caps the number of possible combinations at 16, whereas Matomo has no limits. 

  Redirect Tests

  Redirect tests, also known as split URL tests, allow you to serve two entirely different web page versions to users and compare their performance. This option comes in handy when you’re redesigning your website or want to test a localised page version in a new market. 

  Also, redirect tests are a great way to validate the performance of bottom-of-the-funnel (BoFU) pages as a checkout page (for eCommerce websites), a pricing page (for SaaS apps) or a contact/booking form (for a B2B service businesses). 

  You can do split URL tests with Google Optimize and Matomo A/B Testing. 

  Experiment Design 

  Google Optimize provides a visual editor for making simple page changes to your website (e.g., changing button colour or adding several headline variations). You can then preview the changes before publishing an experiment. For more complex experiments (e.g., testing different page block sequences), you’ll have to codify experiments using custom JavaScript, HTML and CSS.

  In Matomo, all A/B tests are configured on the server-side (i.e., by editing your website’s raw HTML) or client-side via JavaScript. Afterwards, you use the Matomo interface to start or schedule an experiment, set objectives and view reports. 

  Experiment Configuration 

  Marketers know how complex customer journeys can be. Multiple factors — from location and device to time of the day and discount size — can impact your conversion rates. That’s why a great CRO app allows you to configure multiple tracking conditions. 

  Matomo A/B testing comes with granular controls. First of all, you can decide which percentage of total web visitors participate in any given experiment. By default, the number is set to 100%, but you can change it to any other option. 

  Likewise, you can change which percentage of traffic each variant gets in an experiment. For example, your original version can get 30% of traffic, while options A and B receive 40% each. We also allow users to specify custom parameters for experiment participation. You can only show your variants to people in specific geo-location or returning visitors only. 

  Finally, you can select any type of meaningful objective to evaluate each variant’s performance. With Matomo, you can either use standard website analytics metrics (e.g., total page views, bounce rate, CTR, visit direction, etc) or custom goals (e.g., form click, asset download, eCommerce order, etc). 

  In other words : You’re in charge of deciding on your campaign targeting criteria, duration and evaluation objectives.

  A free Google Optimize account comes with three main types of user targeting options : 

  • Geo-targeting at city, region, metro and country levels. 

  • Technology targeting  by browser, OS or device type, first-party cookie, etc. 
  • Behavioural targeting based on metrics like “time since first arrival” and “page referrer” (referral traffic source). 

  Users can also configure other types of tracking scenarios (for example to only serve tests to signed-in users), using condition-based rules. 

  Reporting 

  Both Matomo and Google Optimize use different statistical models to evaluate which variation performs best. 

  Matomo relies on statistical hypothesis testing, which we use to count unique visitors and report on conversion rates. We analyse all user data (with no data sampling applied), meaning you get accurate reporting, based on first-hand data, rather than deductions. For that reason, we ask users to avoid drawing conclusions before their experiment participation numbers reach a statistically significant result. Typically, we recommend running an experiment for at least several business cycles to get a comprehensive report. 

  Google Optimize, in turn, uses Bayesian inference — a statistical method, which relies on a random sample of users to compare the performance rates of each creative against one another. While a Bayesian model generates CRO reports faster and at a bigger scale, it’s based on inferences.

  Model developers need to have the necessary skills to translate subjective prior beliefs about the probability of a certain event into a mathematical formula. Since Google Optimize is a proprietary tool, you cannot audit the underlying model design and verify its accuracy. In other words, you trust that it was created with the right judgement. 

  In comparison, Matomo started as an open-source project, and our source code can be audited independently by anyone at any time. 

  Another reporting difference to mind is the reporting delays. Matomo Cloud generates A/B reports within 6 hours and in only 1 hour for Matomo On-Premise. Google Optimize, in turn, requires 12 hours from the first experiment setup to start reporting on results. 

  When you configure a test experiment and want to quickly verify that everything is set up correctly, this can be an inconvenience.

  User Privacy & GDPR Compliance 

  Google Optimize works in conjunction with Google Analytics, which isn’t GDPR compliant. 

  For all website traffic from the EU, you’re therefore obliged to show a cookie consent banner. The kicker, however, is that you can only show an Optimize experiment after the user gives consent to tracking. If the user doesn’t, they will only see an original page version. Considering that almost 40% of global consumers reject cookie consent banners, this can significantly affect your results.

  This renders Google Optimize mostly useless in the EU since it would only allow you to run tests with a fraction ( 60%) of EU traffic — and even less if you apply any extra targeting criteria. 

  In comparison, Matomo is fully GDPR compliant. Therefore, our users are legally exempt from displaying cookie-consent banners in most EU markets (with Germany and the UK being an exception). Since Matomo A/B testing is part of Matomo web analytics, you don’t have to worry about GDPR compliance or breaches in user privacy. 

  Digital Experience Intelligence 

  You can get comprehensive statistical data on variants’ performance with Google Optimize. But you don’t get further insights on why some tests are more successful than others. 

  Matomo enables you to collect more insights with two extra features :

  • User session recordings : Monitor how users behave on different page versions. Observe clicks, mouse movements, scrolls, page changes, and form interactions to better understand the users’ cumulative digital experience. 
  • Heatmaps : Determine which elements attract the most users’ attention to fine-tune your split tests. With a standard CRO tool, you only assume that a certain page element does matter for most users. A heatmap can help you determine for sure. 

  Both of these features are bundled into your Matomo Cloud subscription. 

  Integrations 

  Both Matomo and Google Optimize integrate with multiple other tools. 

  Google Optimize has native integrations with other products in the marketing family — GA, Google Ads, Google Tag Manager, Google BigQuery, Accelerated Mobile Pages (AMP), and Firebase. Separately, other popular marketing apps have created custom connectors for integrating Google Optimize data. 

  Matomo A/B Testing, in turn, can be combined with other web analytics and CRO features such as Funnels, Multi-Channel Attribution, Tag Manager, Form Analytics, Heatmaps, Session Recording, and more ! 

  You can also conveniently export your website analytics or CRO data using Matomo Analytics API to analyse it in another app. 

  Pricing 

  Google Optimize is a free tool but has usage caps. If you want to schedule more than 5 concurrent experiments or test more than 16 variants at once, you’ll have to upgrade to Optimize 360. Optimize 360 prices aren’t listed publicly but are said to be closer to six figures per year. 

  Matomo A/B Testing is available with every Cloud subscription (starting from €19) and Matomo On-Premise users can also get A/B Testing as a plugin (starting from €199/year). In each case, there are no caps or data limits. 

  Google Optimize vs Matomo A/B Testing : Comparison Table

  Features/capabilities	Google Optimize	Matomo A/B test
  Supported channels	Web	Web, mobile, email, digital campaigns
  A/B testing
  Multivariate testing (MVT)
  Split URL tests
  Web analytics integration	Native with UA/GA4	Native with Matomo You can also migrate historical UA (GA3) data to Matomo
  Audience segmentation	Basic	Advanced
  Geo-targeting
  Technology targeting
  Behavioural targeting	Basic	Advanced
  Reporting model	Bayesian analysis	Statistical hypothesis testing
  Report availability	Within 12 hours after setup	6 hours for Matomo Cloud 1 hour for Matomo On-Premise
  Heatmaps		Included with Matomo Cloud
  Session recordings		Included with Matomo Cloud
  GDPR compliance
  Support	Self-help desk on a free tier	Self-help guides, user forum, email
  Price	Free limited tier	From €19 for Cloud subscription From €199/year as plugin for On-Premise

  Final Thoughts : Who Benefits the Most From an A/B Testing Tool ?

  Split testing is an excellent method for validating various assumptions about your target customers. 

  With A/B testing tools you get a data-backed answer to research hypotheses such as “How different pricing affects purchases ?”, “What contact button placement generates more clicks ?”, “Which registration form performs best with new app subscribers ?” and more. 

  Such insights can be game-changing when you’re trying to improve your demand-generation efforts or conversion rates at the BoFu stage. But to get meaningful results from CRO tests, you need to select measurable, representative objectives.

  For example, split testing different pricing strategies for low-priced, frequently purchased products makes sense as you can run an experiment for a couple of weeks to get a statistically relevant sample. 

  But if you’re in a B2B SaaS product, where the average sales cycle takes weeks (or months) to finalise and things like “time-sensitive discounts” or “one-time promos” don’t really work, getting adequate CRO data will be harder. 

  To see tangible results from CRO, you’ll need to spend more time on test ideation than implementation. Your team needs to figure out : which elements to test, in what order, and why. 

  Effective CRO tests are designed for a specific part of the funnel and assume that you’re capable of effectively identifying and tracking conversions (goals) at the selected stage. This alone can be a complex task since not all customer journeys are alike. For SaaS websites, using a goal like “free trial account registration” can be a good starting point.

  A good test also produces a meaningful difference between the proposed variant and the original version. As Nima Yassini, Partner at Deloitte Digital, rightfully argues :

  “I see people experimenting with the goal of creating an uplift. There’s nothing wrong with that, but if you’re only looking to get wins you will be crushed when the first few tests fail. The industry average says that only one in five to seven tests win, so you need to be prepared to lose most of the time”.

  In many cases, CRO tests don’t provide the data you expected (e.g., people equally click the blue and green buttons). In this case, you need to start building your hypothesis from scratch. 

  At the same time, it’s easy to get caught up in optimising for “vanity metrics” — such that look good in the report, but don’t quite match your marketing objectives. For example, better email headline variations can improve your email open rates. But if users don’t proceed to engage with the email content (e.g. click-through to your website or use a provided discount code), your efforts are still falling short. 

  That’s why developing a baseline strategy is important before committing to an A/B testing tool. Google Optimize appealed to many users because it’s free and allows you to test your split test strategy cost-effectively. 

  With its upcoming depreciation, many marketers are very committed to a more expensive A/B tool (especially when they’re not fully sure about their CRO strategy and its results). 

  Matomo A/B testing is a cost-effective, GDPR-compliant alternative to Google Optimize with a low learning curve and extra competitive features. 

  Discover if Matomo A/B Testing is the ideal Google Optimize alternative for your organization with our free 21-day trial. No credit card required.

• Top 5 Web Analytics Tools for Your Site

  11 août 2023, par Erin — Analytics Tips

  At the start of July 2023, Universal Analytics (UA) users had to say goodbye to their preferred web analytics tool as Google discontinued it. While some find Google Analytics 4 (GA4) can do what they need, many GA4 users are starting to realise GA4 doesn’t meet all the needs UA once fulfilled. Consequently, they are actively seeking another web analytics tool to complement GA4 and address those unmet requirements effectively.

  In this article, we’ll break down five of the top web analytics tools on the market. You’ll find details about their core capabilities, pricing structures and some noteworthy pros and cons to help you decide which tool is the right fit for you. We’ve also included some key features a good web analytics tool should have to give you a baseline for comparison.

  Whether you’re a marketing manager focused on ROI of campaigns, a web analyst focused on conversions or simply interested in learning more about web analytics, there’s something for you on this list.

  What is a web analytics tool ?

  Web analytics tools collect and analyse information about your website’s visitors, their behaviour and the technical performance of your site. A web analytics tool compiles, measures and analyses website data to give you the information you need to improve site performance, boost conversions and increase your ROI.

  What makes a web analytics tool good ?

  Before we get into tool specifics, let’s go over some of the core features you can expect from a web analytics tool.

  For a web analytics tool to be worth your time (and money), it needs to cover the basics. For example :

  • Visitor reports : The number of visitors, whether they were unique or repeat visitors, the source of traffic (where they found your website), device information (if they’re using a desktop or mobile device) and demographic information like geographic location

  • Behaviour reports : What your visitors did while on your site, conversion rates (e.g., if they signed up for or purchased something), the pages they entered and exited from, average session duration, total time spent on a page and bounce rates (if they left without interacting with anything)

  • Technical information : Page loading speed and event tracking — where users are clicking, what they’re downloading or sharing from your site, if they’re engaging with the media on it and how far down the page they’re scrolling

  • Marketing campaign information : Breakdowns of ad campaigns by provider, showing if ads resulted in traffic to your site and lead to an eventual sale or conversion

  • Search Engine Optimisation (SEO) information : Which keywords on which pages are driving traffic to your site, and what search engines are they coming from

  • Real-time data tracking : Visitor, behaviour and technical information available in real-time, or close to it — allowing you to address to issues as they occur

  • Data visualisation : Charts and graphs illustrating the above information in an easily-readable format — helping identify opportunities and providing valuable insights you can leverage to improve site performance, conversion rates and the amount of time visitors spend on a page

  • Custom reporting : Create custom reports detailing the desired metrics and time frame you’re interested in

  • Security : User access controls and management tools to limit who can see and interact with user data

  • Resources : Official user guides, technical documentation, troubleshooting materials, customer support and community forums

  

  Pros and Cons of Google Analytics 4

  Despite many users’ dissatisfaction, GA4 isn’t going away anytime soon. It’s still a powerful tool with all the standard features you’d expect. It’s the most popular choice for web analytics for a few other reasons, too, including :

  • It’s free to use
  • It’s easy to set up
  • It has a convenient mobile app
  • It has a wealth of user documentation and technical resources online
  • Its machine-learning capabilities help predict user behaviour and offer insights on how to grow your site
  • It integrates easily with other Google tools, like Google Search Console, Google Ads and Google Cloud

  That said, it comes with some serious drawbacks. Many users accustomed to UA have reported being unhappy with the differences between it and GA4. Their reasons range from changes to the user interface and bounce rate calculations, as well as Google’s switch from pageview-focused metrics to event-based ones. 

  Let’s take a look at some of the other cons :

  • Lack of privacy, as Google uses data from Google Analytics for advertising purposes.
  • Cookie consent banners can frustrate visitors, and with 40-60% of web visitors rejecting consent, relying on them can lead to inaccurate data and an incomplete view of your web traffic and campaign outcomes.
  • Can’t import data from UA to GA4 
  • Missing features like heatmaps and session recording
  • Google Analytics 360, the GA for enterprises, costs $150,000/year

  Now that you know GA4’s strengths and weaknesses, it’s time to explore other tools that can help fill in GA4’s gaps.

  Top 5 web analytics tools (that aren’t Google)

  Below is a list of popular web analytics tools that, unless otherwise stated, have all the features a good tool should have.

  Adobe Analytics

  

  Adobe is a trusted name in software, with tools that have shaped the technological landscape for decades, like Photoshop and Illustrator. With web design and UX tools Dreamweaver and XD, it makes sense that they’d offer a web analytics platform as well.

  Adobe Analytics provides not just web analytics but marketing analytics that tell you about customer acquisition and retention, ROI and ad campaign performance metrics. Its machine learning (ML) and AI-powered analytics predict future customer behaviour based on previously collected data.

  Key features : 

  • Multichannel data collection that covers computers, mobile devices and IoT devices
  • Adobe Sensei (AI/ML) for marketing attribution and anomaly detection
  • Tag management through Adobe Experience Platform Launch simplifies the tag creation and maintenance process to help you track how users interact with your site

  Pros :

  • User-friendly and simple to learn with a drag-and-drop interface
  • When integrated with other Adobe software, it becomes a powerful solution for enterprises
  • Saves your team a lot of time with the recommendations and insights automatically generated by Adobe’s AI/ML

  Cons :

  • No free version
  • Adobe Sensei and tag manager limited to premium version
  • Expensive, especially when combined with the company’s other software
  • Steep learning curve for both setup and use

  Mobile app : Yes

  Integrations : Integrates with Adobe Experience Manager Sites, the company’s CMS. Adobe Target, a CRO tool and part of the Adobe Marketing Cloud subscription, integrates with Analytics.

  Pricing : Available upon request

  Matomo

  

  Matomo is the leading open-source web analytics solution designed to help you make more informed decisions and enhance your customer experience while ensuring GDPR compliance and user privacy. With Matomo Cloud, your data is stored in Europe, while Matomo On-Premise allows you to host your data on your own servers.

  Matomo is used on over 1 million websites, in over 190 countries, and in over 50 languages. Additionally, Matomo is an all-in-one solution, with traditional web analytics (visits, acquisition, etc.) alongside behavioural analytics (heatmaps, session recordings and more), plus a tag manager. No more inefficiently jumping back and forth between tabs in a huge tech stack. It’s all in Matomo, for one consistent, seamless and efficient experience. 

  Key features : 

  • Heatmaps and session recording to display what users are clicking on and how individual users interacted with your site 
  • A/B testing to compare different versions of the same content and see which gets better results
  • Robust API that lets you get insights by connecting your data to other platforms, like data visualisation or business intelligence tools

  Pros : 

  • Open-source, reviewed by experts to ensure that it’s secure
  • Offers On-Premise or Cloud-hosted options
  • Fully compliant with GDPR, so you can be data-driven without worrying. 
  • Option to run without cookies, meaning in most countries you can use Matomo without annoying cookie consent banners and while getting more accurate data
  • You retain complete ownership of your data, with no third parties using it for advertising or unspecified “own purposes”

  Cons : 

  • On-Premise is free, but that means an additional cost for advanced features (A/B testing, heatmaps, etc.) that are included by default on Matomo Cloud
  • Matomo On-Premise requires servers and technical expertise to setup and manage

  Mobile app : Matomo offers a free mobile app (iOS and Android) so you can access your analytics on the go. 

  Integrations : Matomo integrates easily with many other tools and platforms, including WordPress, Looker Studio, Magento, Jira, Drupal, Joomla and Cloudflare.

  Pricing : 

  • Varies based on monthly hits
  • Matomo On-Premise : free
  • Matomo Cloud : starting at €19/month

  Mixpanel

  

  Mixpanel’s features are heavily geared toward e-commerce companies. From the moment a visitor lands on your website to the moment they enter their payment details and complete a transaction, Mixpanel tracks these events.

  Similar to GA4, Mixpanel is an event-focused analytics platform. While you can still track pageviews with Mixpanel, its main focus is on the specific actions users take that lead them to purchases. Putting your attention on this information allows you to find out which events on your site are going through the sales funnel.

  They’re currently developing a Warehouse Events feature to simplify the process of importing data lakes and data warehouses.

  Key features :

  • Custom alerts and anomaly detection
  • Boards, which allow you to share multiple reports and insights with your team in a range of visual styles 
  • Detailed segmentation reporting that lets you break down your data to the individual user, specific event or geographic level

  Pros :

  • Boards allow for emojis, gifs, images and videos to make collaboration fun
  • Powerful mobile analytics for iOS and Android apps
  • Free promotional credits for eligible startups 

  Cons :

  • Limited features in free plan
  • Best features limited to the Enterprise-tier subscription
  • Complicated set up
  • Steep learning curve

  Mobile app : No

  Integrations : Mixpanel has a load of integrations, including Figma, Google Cloud, Slack, HappyFox, Snowflake, Microsoft Azure, Optimizely, Mailchimp and Tenjin. They also have a WordPress plugin.

  Pricing : 

  • Starter : free plan available
  • Growth : $20/month
  • Enterprise $833/month

  HubSpot Marketing

  

  HubSpot is a customer relationship management (CRM) platform with marketing, sales, customer service, content management system (CMS) and operations tools. This greater ecosystem of HubSpot software allows you to practically run your entire business in one place.

  Even though HubSpot Marketing isn’t a dedicated web analytics tool, it provides comparable standard metrics as the other tools on this list, albeit without the more advanced analytical metrics they offer. If you’re already using HubSpot to host your website, it’s definitely worth consideration.

  Key features :

  • Customer Journey Analytics presents the steps your customers went through in the sales process, step-by-step, in a visual way
  • Dashboards for your reports, including both fully customisable options for power users and pre-made templates for new users

  Pros :

  • Integration with other HubSpot tools, like HubSpot CRM’s free live chat widget 
  • User-friendly interface with many features being drag-and-drop, like the report dashboard
  • 24/7 customer support

  Cons :

  • Can get expensive with upgrades and other HubSpot tool add ons
  • Not a dedicated web analytics tool, so it’s missing some of the features other tools have, like heatmaps
  • Not really worth it as a standalone tool
  • Some users report customer support is unhelpful

  Mobile app : Yes

  Integrations : The larger HubSpot CRM platform can connect with nearly 1,500 other apps through the HubSpot App Marketplace. These include Slack, Microsoft Teams, Salesforce, Make, WordPress, SurveyMonkey, Shopify, monday.com, Stripe, WooCommerce and hundreds of others.

  Pricing : 

  • Starter : $20/month ($18/month with annual plan) 
  • Professional : $890/month ($800/month with annual plan) 
  • Enterprise : $3,600/month ($43,200 billed annually)

  Kissmetrics

  

  Kissmetrics is a web analytics tool that is marketed toward SaaS and ecommerce companies. They label themselves as “person-based” because they combine event-based tracking with detailed user profiles of the visitors to your site, which allows you to gain insights into customer behaviour. 

  With user profiles, you can drill down to see how many times someone has visited your site, if they’ve purchased from you and the steps they took before completing a sale. This allows you to cater more to these users and drive growth.

  Key features : 

  • Person Profiles that give granular information about individual users and their activities on your site
  • Campaigns, an engagement messenger application, allows you to set up email automations that are triggered by specific events
  • Detailed reporting tools 

  Pros : 

  • No third-party cookies
  • No data sampling
  • APIs for Ruby on Rails, JavaScript, Python and PHP

  Cons : 

  • Difficult installation
  • Strongest reporting features only available in the most expensive plan
  • Reports can be slow to generate
  • Requires custom JavaScript code to tack single-page applications
  • Doesn’t track demographic data, bounce rate, exits, session length or time on page

  Mobile app : No

  Integrations : Kissmetrics integrates with HubSpot, Appcues, Slack, Mailchimp, Shopify, WooCommerce, Recurly and a dozen others. There is also a Kissmetrics WordPress plugin.

  Pricing : 

  • Silver : $299/month (small businesses)
  • Gold : $499/month (medium) 
  • Platinum : custom pricing (enterprises)

  Conclusion

  In this article, you learned about popular tools for web analytics to better inform you of your options. Despite all of GA4’s shortcomings, by complementing it with another web analytics tool, teams can gain a more comprehensive understanding of their website traffic and enhance their overall analytics capabilities.

  If you want an option that delivers powerful insights while keeping privacy, security and compliance at the forefront, you should try Matomo. 

  Try Matomo alongside Google Analytics now to see how it compares.

  Start your 21-day free trial now – no credit card required.

• Open Media Developers Track at OVC 2011

  11 octobre 2011, par silvia

  The Open Video Conference that took place on 10-12 September was so overwhelming, I’ve still not been able to catch my breath ! It was a dense three days for me, even though I only focused on the technology sessions of the conference and utterly missed out on all the policy and content discussions.

  Roughly 60 people participated in the Open Media Software (OMS) developers track. This was an amazing group of people capable and willing to shape the future of video technology on the Web :

  • HTML5 video developers from Apple, Google, Opera, and Mozilla (though we missed the NZ folks),
  • codec developers from WebM, Xiph, and MPEG,
  • Web video developers from YouTube, JWPlayer, Kaltura, VideoJS, PopcornJS, etc.,
  • content publishers from Wikipedia, Internet Archive, YouTube, Netflix, etc.,
  • open source tool developers from FFmpeg, gstreamer, flumotion, VideoLAN, PiTiVi, etc,
  • and many more.

  To provide a summary of all the discussions would be impossible, so I just want to share the key take-aways that I had from the main sessions.

  WebRTC : Realtime Communications and HTML5

  Tim Terriberry (Mozilla), Serge Lachapelle (Google) and Ethan Hugg (CISCO) moderated this session together (slides). There are activities both at the W3C and at IETF – the ones at IETF are supposed to focus on protocols, while the W3C ones on HTML5 extensions.

  The current proposal of a PeerConnection API has been implemented in WebKit/Chrome as open source. It is expected that Firefox will have an add-on by Q1 next year. It enables video conferencing, including media capture, media encoding, signal processing (echo cancellation etc), secure transmission, and a data stream exchange.

  Current discussions are around the signalling protocol and whether SIP needs to be required by the standard. Further, the codec question is under discussion with a question whether to mandate VP8 and Opus, since transcoding gateways are not desirable. Another question is how to measure the quality of the connection and how to report errors so as to allow adaptation.

  What always amazes me around RTC is the sheer number of specialised protocols that seem to be required to implement this. WebRTC does not disappoint : in fact, the question was asked whether there could be a lighter alternative than to re-use dozens of years of protocol development – is it over-engineered ? Can desktop players connect to a WebRTC session ?

  We are already in a second or third revision of this part of the HTML5 specification and yet it seems the requirements are still being collected. I’m quietly confident that everything is done to make the lives of the Web developer easier, but it sure looks like a huge task.

  The Missing Link : Flash to HTML5

  Zohar Babin (Kaltura) and myself moderated this session and I must admit that this session was the biggest eye-opener for me amongst all the sessions. There was a large number of Flash developers present in the room and that was great, because sometimes we just don’t listen enough to lessons learnt in the past.

  This session gave me one of those aha-moments : it the form of the Flash appendBytes() API function.

  The appendBytes() function allows a Flash developer to take a byteArray out of a connected video resource and do something with it – such as feed it to a video for display. When I heard that Web developers want that functionality for JavaScript and the video element, too, I instinctively rejected the idea wondering why on earth would a Web developer want to touch encoded video bytes – why not leave that to the browser.

  But as it turns out, this is actually a really powerful enabler of functionality. For example, you can use it to :

  • display mid-roll video ads as part of the same video element,
  • sequence playlists of videos into the same video element,
  • implement DVR functionality (high-speed seeking),
  • do mash-ups,
  • do video editing,
  • adaptive streaming.

  This totally blew my mind and I am now completely supportive of having such a function in HTML5. Together with media fragment URIs you could even leave all the header download management for resources to the Web browser and just request time ranges from a video through an appendBytes() function. This would be easier on the Web developer than having to deal with byte ranges and making sure that appropriate decoding pipelines are set up.

  Standards for Video Accessibility

  Philip Jagenstedt (Opera) and myself moderated this session. We focused on the HTML5 track element and the WebVTT file format. Many issues were identified that will still require work.

  One particular topic was to find a standard means of rendering the UI for caption, subtitle, und description selection. For example, what icons should be used to indicate that subtitles or captions are available. While this is not part of the HTML5 specification, it’s still important to get this right across browsers since otherwise users will get confused with diverging interfaces.

  Chaptering was discussed and a particular need to allow URLs to directly point at chapters was expressed. I suggested the use of named Media Fragment URLs.

  The use of WebVTT for descriptions for the blind was also discussed. A suggestion was made to use the voice tag <v> to allow for “styling” (i.e. selection) of the screen reader voice.

  Finally, multitrack audio or video resources were also discussed and the @mediagroup attribute was explained. A question about how to identify the language used in different alternative dubs was asked. This is an issue because @srclang is not on audio or video, only on text, so it’s a missing feature for the multitrack API.

  Beyond this session, there was also a breakout session on WebVTT and the track element. As a consequence, a number of bugs were registered in the W3C bug tracker.

  WebM : Testing, Metrics and New features

  This session was moderated by John Luther and John Koleszar, both of the WebM Project. They started off with a presentation on current work on WebM, which includes quality testing and improvements, and encoder speed improvement. Then they moved on to questions about how to involve the community more.

  The community criticised that communication of what is happening around WebM is very scarce. More sharing of information was requested, including a move to using open Google+ hangouts instead of Google internal video conferences. More use of the public bug tracker can also help include the community better.

  Another pain point of the community was that code is introduced and removed without much feedback. It was requested to introduce a peer review process. Also it was requested that example code snippets are published when new features are announced so others can replicate the claims.

  This all indicates to me that the WebM project is increasingly more open, but that there is still a lot to learn.

  Standards for HTTP Adaptive Streaming

  This session was moderated by Frank Galligan and Aaron Colwell (Google), and Mark Watson (Netflix).

  Mark started off by giving us an introduction to MPEG DASH, the MPEG file format for HTTP adaptive streaming. MPEG has just finalized the format and he was able to show us some examples. DASH is XML-based and thus rather verbose. It is covering all eventualities of what parameters could be switched during transmissions, which makes it very broad. These include trick modes e.g. for fast forwarding, 3D, multi-view and multitrack content.

  MPEG have defined profiles – one for live streaming which requires chunking of the files on the server, and one for on-demand which requires keyframe alignment of the files. There are clear specifications for how to do these with MPEG. Such profiles would need to be created for WebM and Ogg Theora, too, to make DASH universally applicable.

  Further, the Web case needs a more restrictive adaptation approach, since the video element’s API is already accounting for some of the features that DASH provides for desktop applications. So, a Web-specific profile of DASH would be required.

  Then Aaron introduced us to the MediaSource API and in particular the webkitSourceAppend() extension that he has been experimenting with. It is essentially an implementation of the appendBytes() function of Flash, which the Web developers had been asking for just a few sessions earlier. This was likely the biggest announcement of OVC, alas a quiet and technically-focused one.

  Aaron explained that he had been trying to find a way to implement HTTP adaptive streaming into WebKit in a way in which it could be standardised. While doing so, he also came across other requirements around such chunked video handling, in particular around dynamic ad insertion, live streaming, DVR functionality (fast forward), constraint video editing, and mashups. While trying to sort out all these requirements, it became clear that it would be very difficult to implement strategies for stream switching, buffering and delivery of video chunks into the browser when so many different and likely contradictory requirements exist. Also, once an approach is implemented and specified for the browser, it becomes very difficult to innovate on it.

  Instead, the easiest way to solve it right now and learn about what would be necessary to implement into the browser would be to actually allow Web developers to queue up a chunk of encoded video into a video element for decoding and display. Thus, the webkitSourceAppend() function was born (specification).

  The proposed extension to the HTMLMediaElement is as follows :

  partial interface HTMLMediaElement 
    // URL passed to src attribute to enable the media source logic.
    readonly attribute [URL] DOMString webkitMediaSourceURL ;
    bool webkitSourceAppend(in Uint8Array data) ;
    // end of stream status codes.
  
    const unsigned short EOS_NO_ERROR = 0 ;
  
    const unsigned short EOS_NETWORK_ERR = 1 ;
  
    const unsigned short EOS_DECODE_ERR = 2 ;
    void webkitSourceEndOfStream(in unsigned short status) ;
    // states
  
    const unsigned short SOURCE_CLOSED = 0 ;
  
    const unsigned short SOURCE_OPEN = 1 ;
  
    const unsigned short SOURCE_ENDED = 2 ;
    readonly attribute unsigned short webkitSourceState ;
  
   ;
  

  The code is already checked into WebKit, but commented out behind a command-line compiler flag.

  Frank then stepped forward to show how webkitSourceAppend() can be used to implement HTTP adaptive streaming. His example uses WebM – there are no examples with MPEG or Ogg yet.

  The chunks that Frank’s demo used were 150 video frames long (6.25s) and 5s long audio. Stream switching only switched video, since audio data is much lower bandwidth and more important to retain at high quality. Switching was done on multiplexed files.

  Every chunk requires an XHR range request – this could be optimised if the connections were kept open per adaptation. Seeking works, too, but since decoding requires download of a whole chunk, seeking latency is determined by the time it takes to download and decode that chunk.

  Similar to DASH, when using this approach for live streaming, the server has to produce one file per chunk, since byte range requests are not possible on a continuously growing file.

  Frank did not use DASH as the manifest format for his HTTP adaptive streaming demo, but instead used a hacked-up custom XML format. It would be possible to use JSON or any other format, too.

  After this session, I was actually completely blown away by the possibilities that such a simple API extension allows. If I wasn’t sold on the idea of a appendBytes() function in the earlier session, this one completely changed my mind. While I still believe we need to standardise a HTTP adaptive streaming file format that all browsers will support for all codecs, and I still believe that a native implementation for support of such a file format is necessary, I also believe that this approach of webkitSourceAppend() is what HTML needs – and maybe it needs it faster than native HTTP adaptive streaming support.

  Standards for Browser Video Playback Metrics

  This session was moderated by Zachary Ozer and Pablo Schklowsky (JWPlayer). Their motivation for the topic was, in fact, also HTTP adaptive streaming. Once you leave the decisions about when to do stream switching to JavaScript (through a function such a wekitSourceAppend()), you have to expose stream metrics to the JS developer so they can make informed decisions. The other use cases is, of course, monitoring of the quality of video delivery for reporting to the provider, who may then decide to change their delivery environment.

  The discussion found that we really care about metrics on three different levels :

  • measuring the network performance (bandwidth)
  • measuring the decoding pipeline performance
  • measuring the display quality

  In the end, it seemed that work previously done by Steve Lacey on a proposal for video metrics was generally acceptable, except for the playbackJitter metric, which may be too aggregate to mean much.

  Device Inputs / A/V in the Browser

  I didn’t actually attend this session held by Anant Narayanan (Mozilla), but from what I heard, the discussion focused on how to manage permission of access to video camera, microphone and screen, e.g. when multiple applications (tabs) want access or when the same site wants access in a different session. This may apply to real-time communication with screen sharing, but also to photo sharing, video upload, or canvas access to devices e.g. for time lapse photography.

  Open Video Editors

  This was another session that I wasn’t able to attend, but I believe the creation of good open source video editing software and similar video creation software is really crucial to giving video a broader user appeal.

  Jeff Fortin (PiTiVi) moderated this session and I was fascinated to later see his analysis of the lifecycle of open source video editors. It is shocking to see how many people/projects have tried to create an open source video editor and how many have stopped their project. It is likely that the creation of a video editor is such a complex challenge that it requires a larger and more committed open source project – single people will just run out of steam too quickly. This may be comparable to the creation of a Web browser (see the size of the Mozilla project) or a text processing system (see the size of the OpenOffice project).

  Jeff also mentioned the need to create open video editor standards around playlist file formats etc. Possibly the Open Video Alliance could help. In any case, something has to be done in this space – maybe this would be a good topic to focus next year’s OVC on ?

  Monday’s Breakout Groups

  The conference ended officially on Sunday night, but we had a third day of discussions / hackday at the wonderful New York Lawschool venue. We had collected issues of interest during the two previous days and organised the breakout groups on the morning (Schedule).

  In the Content Protection/DRM session, Mark Watson from Netflix explained how their API works and that they believe that all we need in browsers is a secure way to exchange keys and an indicator of protection scheme is used – the actual protection scheme would not be implemented by the browser, but be provided by the underlying system (media framework/operating system). I think that until somebody actually implements something in a browser fork and shows how this can be done, we won’t have much progress. In my understanding, we may also need to disable part of the video API for encrypted content, because otherwise you can always e.g. grab frames from the video element into canvas and save them from there.

  In the Playlists and Gapless Playback session, there was massive brainstorming about what new cool things can be done with the video element in browsers if playback between snippets can be made seamless. Further discussions were about a standard playlist file formats (such as XSPF, MRSS or M3U), media fragment URIs in playlists for mashups, and the need to expose track metadata for HTML5 media elements.

  What more can I say ? It was an amazing three days and the complexity of problems that we’re dealing with is a tribute to how far HTML5 and open video has already come and exciting news for the kind of applications that will be possible (both professional and community) once we’ve solved the problems of today. It will be exciting to see what progress we will have made by next year’s conference.

  Thanks go to Google for sponsoring my trip to OVC.

  UPDATE : We actually have a mailing list for open media developers who are interested in these and similar topics – do join at http://lists.annodex.net/cgi-bin/mailman/listinfo/foms.