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• What is Multi-Touch Attribution ? (And How To Get Started)

  2 février 2023, par Erin — Analytics Tips

  Good marketing thrives on data. Or more precisely — its interpretation. Using modern analytics software, we can determine which marketing actions steer prospects towards the desired action (a conversion event). 

  An attribution model in marketing is a set of rules that determine how various marketing tactics and channels impact the visitor’s progress towards a conversion. 

  Yet, as customer journeys become more complicated and involve multiple “touches”, standard marketing reports no longer tell the full picture. 

  That’s when multi-touch attribution analysis comes to the fore. 

  What is Multi-Touch Attribution ?

  Multi-touch attribution (also known as multi-channel attribution or cross-channel attribution) measures the impact of all touchpoints on the consumer journey on conversion. 

  Unlike single-touch reporting, multi-touch attribution models give credit to each marketing element — a social media ad, an on-site banner, an email link click, etc. By seeing impacts from every touchpoint and channel, marketers can avoid false assumptions or subpar budget allocations.

  To better understand the concept, let’s interpret the same customer journey using a standard single-touch report vs a multi-touch attribution model. 

  Picture this : Jammie is shopping around for a privacy-centred web analytics solution. She saw a recommendation on Twitter and ended up on the Matomo website. After browsing a few product pages and checking comparisons with other web analytics tools, she signs up for a webinar. One week after attending, Jammie is convinced that Matomo is the right tool for her business and goes directly to the Matomo website a starts a free trial. 

  • A standard single-touch report would attribute 100% of the conversion to direct traffic, which doesn’t give an accurate view of the multiple touchpoints that led Jammie to start a free trial. 
  • A multi-channel attribution report would showcase all the channels involved in the free trial conversion — social media, website content, the webinar, and then the direct traffic source.

  In other words : Multi-touch attribution helps you understand how prospects move through the sales funnel and which elements tinder them towards the desired outcome. 

  Types of Attribution Models

  As marketers, we know that multiple factors play into a conversion — channel type, timing, user’s stage on the buyer journey and so on. Various attribution models exist to reflect this variability. 

  

  First Interaction attribution model (otherwise known as first touch) gives all credit for the conversion to the first channel (for example — a referral link) and doesn’t report on all the other interactions a user had with your company (e.g., clicked a newsletter link, engaged with a landing page, or browsed the blog campaign).

  First-touch helps optimise the top of your funnel and establish which channels bring the best leads. However, it doesn’t offer any insight into other factors that persuaded a user to convert. 

  Last Interaction attribution model (also known as last touch) allocates 100% credit to the last channel before conversion — be it direct traffic, paid ad, or an internal product page.

  The data is useful for optimising the bottom-of-the-funnel (BoFU) elements. But you have no visibility into assisted conversions — interactions a user had prior to conversion. 

  Last Non-Direct attribution model model excludes direct traffic and assigns 100% credit for a conversion to the last channel a user interacted with before converting. For instance, a social media post will receive 100% of credit if a shopper buys a product three days later. 

  This model is more telling about the other channels, involved in the sales process. Yet, you’re seeing only one step backwards, which may not be sufficient for companies with longer sales cycles.

  Linear attribution model distributes an equal credit for a conversion between all tracked touchpoints.

  For instance, with a four touchpoint conversion (e.g., an organic visit, then a direct visit, then a social visit, then a visit and conversion from an ad campaign) each touchpoint would receive 25% credit for that single conversion.

  This is the simplest multi-channel attribution modelling technique many tools support. The nuance is that linear models don’t reflect the true impact of various events. After all, a paid ad that introduced your brand to the shopper and a time-sensitive discount code at the checkout page probably did more than the blog content a shopper browsed in between. 

  Position Based attribution model allocates a 40% credit to the first and the last touchpoints and then spreads the remaining 20% across the touchpoints between the first and last. 

  This attribution model comes in handy for optimising conversions across the top and the bottom of the funnel. But it doesn’t provide much insight into the middle, which can skew your decision-making. For instance, you may overlook cases when a shopper landed via a social media post, then was re-engaged via email, and proceeded to checkout after an organic visit. Without email marketing, that sale may not have happened.

  Time decay attribution model adjusts the credit, based on the timing of the interactions. Touchpoints that preceded the conversion get the highest score, while the first ones get less weight (e.g., 5%-5%-10%-15%-25%-30%).

  This multi-channel attribution model works great for tracking the bottom of the funnel, but it underestimates the impact of brand awareness campaigns or assisted conversions at mid-stage. 

  Why Use Multi-Touch Attribution Modelling

  Multi-touch attribution provides you with the full picture of your funnel. With accurate data across all touchpoints, you can employ targeted conversion rate optimisation (CRO) strategies to maximise the impact of each campaign. 

  Most marketers and analysts prefer using multi-touch attribution modelling — and for some good reasons.

  Issues multi-touch attribution solves 

  • Funnel visibility. Understand which tactics play an important role at the top, middle and bottom of your funnel, instead of second-guessing what’s working or not. 
  • Budget allocations. Spend money on channels and tactics that bring a positive return on investment (ROI). 
  • Assisted conversions. Learn how different elements and touchpoints cumulatively contribute to the ultimate goal — a conversion event — to optimise accordingly. 
  • Channel segmentation. Determine which assets drive the most qualified and engaged leads to replicate them at scale.
  • Campaign benchmarking. Compare how different marketing activities from affiliate marketing to social media perform against the same metrics.

  How To Get Started With Multi-Touch Attribution 

  To make multi-touch attribution part of your analytics setup, follow the next steps :

  1. Define Your Marketing Objectives 

  Multi-touch attribution helps you better understand what led people to convert on your site. But to capture that, you need to first map the standard purchase journeys, which include a series of touchpoints — instances, when a prospect forms an opinion about your business.

  Touchpoints include :

  • On-site interactions (e.g., reading a blog post, browsing product pages, using an on-site calculator, etc.)
  • Off-site interactions (e.g., reading a review, clicking a social media link, interacting with an ad, etc.)

  Combined these interactions make up your sales funnel — a designated path you’ve set up to lead people toward the desired action (aka a conversion). 

  Depending on your business model, you can count any of the following as a conversion :

  • Purchase 
  • Account registration 
  • Free trial request 
  • Contact form submission 
  • Online reservation 
  • Demo call request 
  • Newsletter subscription

  So your first task is to create a set of conversion objectives for your business and add them as Goals or Conversions in your web analytics solution. Then brainstorm how various touchpoints contribute to these objectives. 

  Web analytics tools with multi-channel attribution, like Matomo, allow you to obtain an extra dimension of data on touchpoints via Tracked Events. Using Event Tracking, you can analyse how many people started doing a desired action (e.g., typing details into the form) but never completed the task. This way you can quickly identify “leaking” touchpoints in your funnel and fix them. 

  2. Select an Attribution Model 

  Multi-attribution models have inherent tradeoffs. Linear attribution model doesn’t always represent the role and importance of each channel. Position-based attribution model emphasises the role of the last and first channel while diminishing the importance of assisted conversions. Time-decay model, on the contrary, downplays the role awareness-related campaigns played.

  To select the right attribution model for your business consider your objectives. Is it more important for you to understand your best top of funnel channels to optimise customer acquisition costs (CAC) ? Or would you rather maximise your on-site conversion rates ? 

  Your industry and the average cycle length should also guide your choice. Position-based models can work best for eCommerce and SaaS businesses where both CAC and on-site conversion rates play an important role. Manufacturing companies or educational services providers, on the contrary, will benefit more from a time-decay model as it better represents the lengthy sales cycles. 

  3. Collect and Organise Data From All Touchpoints 

  Multi-touch attribution models are based on available funnel data. So to get started, you will need to determine which data sources you have and how to best leverage them for attribution modelling. 

  Types of data you should collect : 

  • General web analytics data : Insights on visitors’ on-site actions — visited pages, clicked links, form submissions and more.
  • Goals (Conversions) : Reports on successful conversions across different types of assets. 
  • Behavioural user data : Some tools also offer advanced features such as heatmaps, session recording and A/B tests. These too provide ample data into user behaviours, which you can use to map and optimise various touchpoints.

  You can also implement extra tracking, for instance for contact form submissions, live chat contacts or email marketing campaigns to identify repeat users in your system. Just remember to stay on the good side of data protection laws and respect your visitors’ privacy. 

  Separately, you can obtain top-of-the-funnel data by analysing referral traffic sources (channel, campaign type, used keyword, etc). A Tag Manager comes in handy as it allows you to zoom in on particular assets (e.g., a newsletter, an affiliate, a social campaign, etc). 

  Combined, these data points can be parsed by an app, supporting multi-touch attribution (or a custom algorithm) and reported back to you as specific findings. 

  Sounds easy, right ? Well, the devil is in the details. Getting ample, accurate data for multi-touch attribution modelling isn’t easy. 

  Marketing analytics has an accuracy problem, mainly for two reasons :

  • Cookie consent banner rejection 
  • Data sampling application

  Please note that we are not able to provide legal advice, so it’s important that you consult with your own DPO to ensure compliance with all relevant laws and regulations.

  If you’re collecting web analytics in the EU, you know that showing a cookie consent banner is a GDPR must-do. But many consumers don’t often rush to accept cookie consent banners. The average consent rate for cookies in 2021 stood at 54% in Italy, 45% in France, and 44% in Germany. The consent rates are likely lower in 2023, as Google was forced to roll out a “reject all” button for cookie tracking in Europe, while privacy organisations lodge complaints against individual businesses for deceptive banners. 

  For marketers, cookie rejection means substantial gaps in analytics data. The good news is that you can fill in those gaps by using a privacy-centred web analytics tool like Matomo. 

  Matomo takes extra safeguards to protect user privacy and supports fully cookieless tracking. Because of that, Matomo is legally exempt from tracking consent in France. Plus, you can configure to use our analytics tool without consent banners in other markets outside of Germany and the UK. This way you get to retain the data you need for audience modelling without breaching any privacy regulations. 

  Data sampling application partially stems from the above. When a web analytics or multi-channel attribution tool cannot secure first-hand data, the “guessing game” begins. Google Analytics, as well as other tools, often rely on synthetic AI-generated data to fill in the reporting gaps. Respectively, your multi-attribution model doesn’t depict the real state of affairs. Instead, it shows AI-produced guesstimates of what transpired whenever not enough real-world evidence is available.

  4. Evaluate and Select an Attribution Tool 

  Google Analytics (GA) offers several multi-touch attribution models for free (linear, time-decay and position-based). The disadvantage of GA multi-touch attribution is its lower accuracy due to cookie rejection and data sampling application.

  At the same time, you cannot create custom credit allocations for the proposed models, unless you have the paid version of GA, Google Analytics 360. This version of GA comes with a custom Attribution Modeling Tool (AMT). The price tag, however, starts at USD $50,000 per year. 

  Matomo Cloud offers multi-channel conversion attribution as a feature and it is available as a plug-in on the marketplace for Matomo On-Premise. We support linear, position-based, first-interaction, last-interaction, last non-direct and time-decay modelling, based fully on first-hand data. You also get more precise insights because cookie consent isn’t an issue with us. 

  Most multi-channel attribution tools, like Google Analytics and Matomo, provide out-of-the-box multi-touch attribution models. But other tools, like Matomo On-Premise, also provide full access to raw data so you can develop your own multi-touch attribution models and do custom attribution analysis. The ability to create custom attribution analysis is particularly beneficial for data analysts or organisations with complex and unique buyer journeys. 

  Conclusion

  Ultimately, multi-channel attribution gives marketers greater visibility into the customer journey. By analysing multiple touchpoints, you can establish how various marketing efforts contribute to conversions. Then use this information to inform your promotional strategy, budget allocations and CRO efforts. 

  The key to benefiting the most from multi-touch attribution is accurate data. If your analytics solution isn’t telling you the full story, your multi-touch model won’t either. 

  Collect accurate visitor data for multi-touch attribution modelling with Matomo. Start your free 21-day trial now. 

• 5 Top Google Optimize Alternatives to Consider

  17 mars 2023, par Erin — Analytics Tips

  Google Optimize is a popular conversion rate optimization (CRO) tool from Alphabet (parent company of Google). With it, you can run A/B, multivariate, and redirect tests to figure out which web page designs perform best. 

  Google Optimize seamlessly integrates with Google Analytics (GA). It also has a free tier. So many marketers chose it as their default A/B testing tool…until recently. 

  Google will sunset Google Optimize by 30 September 2023. 

  Starting from this date, Google will no longer support Optimize and Optimize 360 (premium edition). All experiments, active after this date, will be paused automatically and you’ll no longer have access to your historical records (unless these are exported in advance).

  The better news is that you still have time to find a Google Optimize alternative — and this post will help you with that. 

  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. 

  Best Google Optimize Alternatives 

  Google Optimize was among the first free A/B testing apps. But as with any product, it has some disadvantages. 

  Data updates happen every 24 hours, not in real-time. A free account has caps on the number of experiments. You cannot run more than 5 experiments at a time or implement over 16 combinations for multivariate testing (MVT). A premium version (Optimize 365) has fewer usage constraints, but it costs north of $150K per year. 

  Google Optimize has native integration with GA (of course), so you can review all the CRO data without switching apps. But Optimize doesn’t work well with Google Analytics alternatives, which many choose to use for privacy-friendly user tracking, higher data accuracy and GDPR compliance. 

  At the same time, many other conversion rate optimization (CRO) tools have emerged, often boasting better accuracy and more competitive features than Google Optimize.

  Here are 5 alternative A/B testing apps worth considering.

  Adobe Target 

  

  Adobe Target is an advanced personalization platform for optimising user and marketing experiences on digital properties. It uses machine learning algorithms to deliver dynamic content, personalised promotions and custom browsing experiences to visitors based on their behaviour and demographic data. 

  Adobe Target also provides A/B testing and multivariate testing (MVT) capabilities to help marketers test and refine their digital experiences.

  Key features : 

  • Visual experience builder for A/B tests setup and replication 
  • Full factorial multivariate tests and multi-armed bandit testing
  • Omnichannel personalisation across web properties 
  • Multiple audience segmentation and targeting options 
  • Personalised content, media and product recommendations 
  • Advanced customer intelligence (in conjunction with other Adobe products)

  Pros

  • Convenient A/B test design tool 
  • Acucate MVT and MAB results 
  • Powerful segmentation capabilities 
  • Access to extra behavioural analytics 
  • One-click personalisation activation 
  • Supports rules-based, location-based and contextual personalisation
  • Robust omnichannel analytics in conjunction with other Adobe products 

  Cons 

  • Requires an Adobe Marketing Cloud subscription 
  • No free trial or freemium tier 
  • More complex product setup and configuration 
  • Steep learning curve for new users 

  Price : On-demand. 

  Adobe Target is sold as part of Adobe Marketing Cloud. Licence costs vary, based on selected subscriptions and the number of users, but are typically above $10K.

  Google Optimize vs Adobe Target : The Verdict 

  Google Optimize comes with a free tier, unlike Adobe Target. It provides you with a basic builder for A/B and MVT tests, but none of the personalisation tools Adobe has. Because of ease-of-use and low price, other Google Optimize alternatives are better suited for small to medium-sized businesses, doing baseline CRO for funnel optimisation. 

  Adobe Target pulls you into the vast Adobe marketing ecosystem, offering omnipotent customer behaviour analytics, machine-learning-driven website optimisation, dynamic content recommendations, product personalisation and extensive reporting. The app is better suited for larger enterprises with a significant investment in digital marketing.

  Matomo A/B Testing

  

  Matomo A/B Testing is a CRO tool, integrated into Matomo. All Matomo Cloud users get instant access to it, while On-Premise (free) Matomo users can purchase A/B testing as a plugin. 

  With Matomo A/B Testing, you can create multiple variations of a web or mobile page and test them with different segments of their audience. Matomo also doesn’t have any strict experiment caps, unlike Google Optimize. 

  You can split-test multiple creative variants for on-site assets such as buttons, slogans, titles, call-to-actions, image positions and more. You can even benchmark the performance of two (or more !) completely different homepage designs, for instance. 

  With us, you can compliantly and ethically collect historical user data about any visitor, who’s entered any of the active tests — and monitor their entire customer journey. You can also leverage Matomo A/B Testing data as part of multi-touch attribution modelling to determine which channels bring the best leads and which assets drive them towards conversion. 

   

  Since Matomo A/B Testing is part of our analytics platform, it works well with other features such as goal tracking, heatmaps, user session recordings and more. 

  Key features

  • Run experiments for web, mobile, email and digital campaigns 
  • Convenient A/B test design interface 
  • One-click experiment scheduling 
  • Integration with historic visitor profiles
  • Near real-time conversion tracking 
  • Apply segmentation to Matomo reports 
  • Easy creative variation sharing via a URL 

  Pros

  • High data accuracy with no reporting gaps 
  • Monitor the evolution of your success metrics for each variation
  • Embed experiments across multiple digital channels 
  • Set a custom confidence threshold for winning variations 
  • No compromises on user privacy 
  • Free 21-day trial available (for Matomo Cloud) and free 30-day plugin trial (for Matomo On-Premise)

  Cons

  • No on-site personalisation tools available 
  • Configuration requires some coding experience 

  Price : Matomo A/B Testing is included in the monthly Cloud plan (starting at €19 per month). On-Premise users can buy this functionality as a plugin (starting at €199/year). 

  Google Optimize vs Matomo A/B Testing : The Verdict 

  Matomo offers the same types of A/B testing features as Google Optimize (and some extras !), but without any usage caps. Unlike Matomo, Google Optimize doesn’t support A/B tests for mobile apps. You can access some content testing features for Android Apps via Firebase, but this requires another subscription. 

  Matomo lets you run A/B experiments across the web and mobile properties, plus desktop apps, email campaigns and digital ads. Also, Matomo has higher conversion data accuracy, thanks to our privacy-focused method for collecting website analytics. 

  When using Matomo in most EU markets, you’re legally exempt from showing a cookie consent banner. Meaning you can collect richer insights for each experiment and make data-driven decisions. Nearly 40% of global consumers reject cookie consent banners. With most other tools, you won’t be getting the full picture of your traffic. 

  Optimizely 

  

  Optimizely is a conversion optimization platform that offers several competitive products for a separate subscription. These include a flexible content management system (CMS), a content marketing platform, a web A/B testing app, a mobile featuring testing product and two eCommerce-specific website management products.

  The Web Experimentation app allows you to optimise every customer touchpoint by scheduling unlimited split or multi-variant tests and conversions across all your projects from the same app. Apart from websites, this subscription also supports experiments for single-page applications. But if you want more advanced mobile app testing features, you’ll have to purchase another product — Feature Experimentation. 

  Key features :

  • Intuitive experiment design tool 
  • Cross-browser testing and experiment preview 
  • Multi-page funnel tests design 
  • Behavioural and geo-targeting 
  • Exit/bounce rate tracking
  • Custom audience builder for experiments
  • Comprehensive reporting 

  Pros

  • Unlimited number of concurrent experiments 
  • Upload your audience data for test optimisation 
  • Dynamic content personalisation available on a higher tier 
  • Pre-made integrations with popular heatmap and analytics tools 
  • Supports segmentation by device, campaign type, traffic sources or referrer 

  Cons

  • You need a separate subscription for mobile CRO 
  • Free trial not available, pricing on-demand 
  • Multiple licences and subscriptions may be required 
  • Doesn’t support A/B tests for emails 

  Price : Available on-demand. 

  Web Experimentation tool has three subscription tiers — Grow, Accelerate, and Scale with different features included. 

  Google Optimize vs Optimizely : The Verdict 

  Optimizely is a strong contender for Google Optimize alternative as it offers more advanced audience targeting and segmentation options. You can target users by IP address, cookies, traffic sources, device type, browser, language, location or a custom utm_campaign parameter.

  Similar to Matomo A/B testing, Optimizely doesn’t limit the number of projects or concurrent experiments you can do. But you have to immediately sign an annual contract (no monthly plans are available). Pricing also varies based on the number of processed impressions (more experiments = a higher annual bill). An annual licence can cost $63,700 for 10 million impressions on average, according to an independent estimate. 

  Visual Website Optimizer (VWO) 

  

  VWO is another popular experimentation platform, supporting web, mobile and server-side A/B testing and personalisation campaigns.

  Similar to others, VWO offers a drag-and-drop visual editor for creating campaign variants. You don’t need design or coding knowledge to create tests. Once you’re all set, the app will benchmark your experiment performance against expected conversion rates, report on differences in conversion rate and point towards the best-performing creative. 

  Similar to Optimizely, VWO also offers web/mobile app optimisation as a separate subscription. Apart from testing visual page elements, you can also run in-app experiments throughout the product stack to locate new revenue opportunities. For example, you can test in-app subscription flows, search algorithms or navigation flows to improve product UX. 

  Key features :

  • Multivariate and multi-arm bandit tests 
  • Multi-step (funnel) split tests 
  • Collaborative experiment tracking dashboard 
  • Target users by different attributes (URL, device, geo-data) 
  • Personal library of creative elements 
  • Funnel analytics, session records, and heatmaps available 

  Pros

  • Free starter plan is available (similar to Google Optimize)
  • Simple tracking code installation and easy code editor
  • Offers online reporting dashboards and report downloads 
  • Slice-and-dice reports by different audience dimensions
  • No impact on website/app loading speed and performance 

  Cons

  • Multivariate testing is only available on a higher-tier plan 
  • Annual contract required, despite monthly billing 
  • Mobile app A/B split tests require another licence 
  • Requires ongoing user training 

  Price : Free limited plan available. 

  Then from $356/month, billed annually. 

  Google Optimize vs VWO : The Verdict 

  The free plan on VWO is very similar to Google Optimize. You get access to A/B testing and split URL testing features for websites only. The visual editing tool is relatively simple — and you can use URL or device targeting. 

  Free VWO reports, however, lack the advertised depth in terms of behavioural or funnel-based reporting. In-depth insights are available only to premium users. Extra advertised features like heatmaps, form analytics and session recordings require yet another subscription. With Matomo Cloud, you get all three of these together with A/B testing. 

  ConvertFlow 

  

  ConvertFlow markets itself as a funnel optimisation app for eCommerce and SaaS companies. It meshes lead generation tools with some CRO workflows. 

  With ConvertFlow, you can effortlessly design opt-in forms, pop-ups, quizzes and even entire landing pages using pre-made web elements and a visual builder. Afterwards, you can put all of these assets to a “field test” via the ConvertFlow CRO platform. Select among pre-made templates or create custom variants for split or multivariate testing. You can customise tests based on URLs, cookie data and user geolocation among other factors. 

  Similar to Adobe Target, ConvertFlow also allows you to run tests targeted at specific customer segments in your CRM. The app has native integrations with HubSpot and Salesforce, so this feature is easy to enable. ConvertFlow also offers advanced targeting and segmentation options, based on user on-site behaviour, demographics data or known interests.

  Key features :

  • Create and test landing pages, surveys, quizzes, pop-ups, surveys and other lead-gen assets. 
  • All-in-one funnel builder for creating demand-generation campaigns 
  • Campaign personalisation, based on on-site activity 
  • Re-usable dynamic visitor segments for targeting 
  • Multi-step funnel design and customisation 
  • Embedded forms for split testing CTAs on existing pages 

  Pros

  • Allows controlling the traffic split for each variant to get objective results 
  • Pre-made integration with Google Analytics and Google Tag Manager 
  • Conversion and funnel reports, available for each variant 
  • Access to a library with 300+ conversion campaign templates
  • Apply progressive visitor profiling to dynamically adjust user experiences 

  Cons

  • Each plan covers only $10K views. Each extra 10k costs another $20/mo 
  • Only one website allowed per account (except for Teams plan) 
  • Doesn’t support experiments in mobile app 
  • Not all CRO features are available on a Pro plan. 

  Price : Access to CRO features costs from $300/month on a Pro plan. Subscription costs also increase, based on the total number of monthly views. 

  Google Optimize vs CovertFlow : The Verdict 

  ConvertFlow is equally convenient to use in conjunction with Google Analytics as Google Optimize is. But the similarities end up here since ConvertFlow combines funnel design features with CRO tools. 

  With ConvertFlow, you can run more advanced experiments and apply more targeting criteria than with Google Optimize. You can observe user behaviour and conversion rates across multi-step CTA forms and page funnels, plus benefit from first-touch attribution reporting without switching apps. 

  Though CovertFlow has a free plan, it doesn’t include access to CRO features. Meaning it’s not a free alternative to Google Optimize.

  Comparison of the Top 5 Google Optimize Alternatives

  Feature	Google Optimize	Adobe Target	Matomo A/B test	Optimizely	VWO	ConvertFlow
  Supported channels	Web	Web, mobile, social media, email	Web, mobile, email, digital campaigns	Websites & mobile apps	Websites, web and mobile apps	Websites and mobile apps
  A/B testing
  Easy GA integration
  Integrations with other web analytics apps
  Audience segmentation	Basic	Advanced	Advanced	Advanced	Advanced	Advanced
  Geo-targeting
  Behavioural targeting	Basic	Advanced	Advanced	Advanced	Advanced	Advanced
  Heatmaps			No extra cost with Matomo Cloud	〰️ *via integrations	〰️ *requires another subscription
  Session recordings			No extra cost with Matomo Cloud		〰️ *requires another subscription
  Multivariate testing (MVT)
  Dynamic personalisation					〰️ *only on higher account tiers	〰️ *only on the highest account tiers
  Product recommendations				〰️ *requires another subscription	〰️ *requires another subscription
  Support	Self-help desk on a free tier	Email, live-chat, phone support	Email, self-help guides and user forum	Knowledge base, online tickets, user community	Self-help guides, email, phone	Knowledge base, email, and live chat support
  Price	Freemium	On-demand	From €19 for Cloud subscription From €199/year as plugin for On-Premise	On-demand	Freemium From $365/mo	From $300/month

  Conclusion 

  Google Optimize has served marketers well for over five years. But as the company decided to move on — so should you. 

  Oher A/B testing tools like Matomo, Optimizely or VWO offer better funnel analytics and split testing capabilities without any usage caps. Also, tools like Adobe Target, Optimizely, and VWO offer advanced content personalisation, based on aggregate analytics. However, they also come with much higher subscription costs.

  Matomo is a robust, compliant and cost-effective alternative to Google Optimize. Our tool allows you to schedule campaigns across all digital mediums (and even desktop apps !) without a

• Mirror not found when trying to install FFMPEG on CENTOS7

  31 octobre 2016, par Peter

  I’m on a dedicated server with Root access. not familiar with servers. Im trying to install FFMpeg on my server but following online instructions I’m getting errors can’t figure out how to solve it. So any light on this will be very appreciated.

  [root@ns335004 ~]# yum update
  
  base                                                                                                           | 3.6 kB  00:00:00     
  
  http://apt.sw.be/redhat/el7/en/x86_64/dag/repodata/repomd.xml: [Errno 14] HTTP Error 404 - Not Found
  
  Trying other mirror.
  
  To address this issue please refer to the below knowledge base article 
  
  
  
  https://access.redhat.com/articles/1320623
  
  
  
  If above article doesn't help to resolve this issue please create a bug on https://bugs.centos.org/
  
  
  
  
  
  
  
   One of the configured repositories failed (DAG RPM Repository),
  
   and yum doesn't have enough cached data to continue. At this point the only
  
   safe thing yum can do is fail. There are a few ways to work "fix" this:
  
  
  
       1. Contact the upstream for the repository and get them to fix the problem.
  
  
  
       2. Reconfigure the baseurl/etc. for the repository, to point to a working
  
          upstream. This is most often useful if you are using a newer
  
          distribution release than is supported by the repository (and the
  
          packages for the previous distribution release still work).
  
  
  
       3. Disable the repository, so yum won't use it by default. Yum will then
  
          just ignore the repository until you permanently enable it again or use
  
          --enablerepo for temporary usage:
  
  
  
              yum-config-manager --disable dag
  
  
  
       4. Configure the failing repository to be skipped, if it is unavailable.
  
          Note that yum will try to contact the repo. when it runs most commands,
  
          so will have to try and fail each time (and thus. yum will be be much
  
          slower). If it is a very temporary problem though, this is often a nice
  
          compromise:
  
  
  
              yum-config-manager --save --setopt=dag.skip_if_unavailable=true
  
  
  
  failure: repodata/repomd.xml from dag: [Errno 256] No more mirrors to try.
  
  http://apt.sw.be/redhat/el7/en/x86_64/dag/repodata/repomd.xml: [Errno 14] HTTP Error 404 - Not Found

  repolist

  [root@ns335004 ~]# yum repolist all
  
  http://apt.sw.be/redhat/el7/en/x86_64/dag/repodata/repomd.xml: [Errno 14] HTTP Error 404 - Not Found
  
  Trying other mirror.
  
  To address this issue please refer to the below knowledge base article 
  
  
  
  https://access.redhat.com/articles/1320623
  
  
  
  If above article doesn't help to resolve this issue please create a bug on https://bugs.centos.org/
  
  
  
  http://apt.sw.be/redhat/el7/en/x86_64/dag/repodata/repomd.xml: [Errno 14] HTTP Error 404 - Not Found
  
  Trying other mirror.
  
  repo id                                  repo name                                                                     status
  
  C7.0.1406-base/x86_64                    CentOS-7.0.1406 - Base                                                        disabled
  
  C7.0.1406-centosplus/x86_64              CentOS-7.0.1406 - CentOSPlus                                                  disabled
  
  C7.0.1406-extras/x86_64                  CentOS-7.0.1406 - Extras                                                      disabled
  
  C7.0.1406-fasttrack/x86_64               CentOS-7.0.1406 - CentOSPlus                                                  disabled
  
  C7.0.1406-updates/x86_64                 CentOS-7.0.1406 - Updates                                                     disabled
  
  C7.1.1503-base/x86_64                    CentOS-7.1.1503 - Base                                                        disabled
  
  C7.1.1503-centosplus/x86_64              CentOS-7.1.1503 - CentOSPlus                                                  disabled
  
  C7.1.1503-extras/x86_64                  CentOS-7.1.1503 - Extras                                                      disabled
  
  C7.1.1503-fasttrack/x86_64               CentOS-7.1.1503 - CentOSPlus                                                  disabled
  
  C7.1.1503-updates/x86_64                 CentOS-7.1.1503 - Updates                                                     disabled
  
  base/7/x86_64                            CentOS-7 - Base                                                               enabled:  9,007
  
  base-debuginfo/x86_64                    CentOS-7 - Debuginfo                                                          disabled
  
  base-source/7                            CentOS-7 - Base Sources                                                       disabled
  
  c7-media                                 CentOS-7 - Media                                                              disabled
  
  centosplus/7/x86_64                      CentOS-7 - Plus                                                               disabled
  
  centosplus-source/7                      CentOS-7 - Plus Sources                                                       disabled
  
  cr/7/x86_64                              CentOS-7 - cr                                                                 disabled
  
  dag/7/x86_64                             DAG RPM Repository                                                            enabled:      0
  
  epel/x86_64                              Extra Packages for Enterprise Linux 7 - x86_64                                enabled: 10,764
  
  epel-debuginfo/x86_64                    Extra Packages for Enterprise Linux 7 - x86_64 - Debug                        disabled
  
  epel-source/x86_64                       Extra Packages for Enterprise Linux 7 - x86_64 - Source                       disabled
  
  epel-testing/x86_64                      Extra Packages for Enterprise Linux 7 - Testing - x86_64                      disabled
  
  epel-testing-debuginfo/x86_64            Extra Packages for Enterprise Linux 7 - Testing - x86_64 - Debug              disabled
  
  epel-testing-source/x86_64               Extra Packages for Enterprise Linux 7 - Testing - x86_64 - Source             disabled
  
  extras/7/x86_64                          CentOS-7 - Extras                                                             enabled:    393
  
  extras-source/7                          CentOS-7 - Extras Sources                                                     disabled
  
  fasttrack/7/x86_64                       CentOS-7 - fasttrack                                                          disabled
  
  nux-dextop/x86_64                        Nux.Ro RPMs for general desktop use                                           disabled
  
  nux-dextop-testing/x86_64                Nux.Ro RPMs for general desktop use - testing                                 disabled
  
  plesk-php-5.6                            PHP v 5.6 for Plesk - x86_64                                                  enabled:     31
  
  plesk-php-7.0                            PHP v 7.0 for Plesk - x86_64                                                  enabled:     28
  
  updates/7/x86_64                         CentOS-7 - Updates                                                            enabled:  2,560
  
  updates-source/7                         CentOS-7 - Updates Sources                                                    disabled
  
  repolist: 22,783

  I also tried :

  sudo yum clean metadata
  
  sudo yum clean all

  But still having same 404 Error.

  Thanks.