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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. 

• Meta Receives a Record GDPR Fine from The Irish Data Protection Commission

  29 mai 2023, par Erin — GDPR

  The Irish Data Protection Commission (the DPC) issued a €1.2 billion fine to Meta on May, 22nd 2023 for violating the General Data Protection Regulation (GDPR). 

  The regulator ruled that Meta was unlawfully transferring European users’ data to its US-based servers and taking no sufficient measures for ensuring users’ privacy. 

  Meta must now suspend data transfer within five months and delete EU/EEA users’ personal data that was illegally transferred across the border. Or they risk facing another round of repercussions. 

  Meta continued to transfer personal user data to the USA following an earlier ruling of The Court of Justice of the European Union (CJEU), which already address problematic EU-U.S. data flows. Meta continued those transfers on the basis of the updated Standard Contractual Clauses (“SCCs”), adopted by the European Commission in 2021. 

  The Irish regulator successfully proved that these arrangements had not sufficiently addressed the “fundamental rights and freedoms” of the European data subjects, outlined in the CJEU ruling. Meta was not doing enough to protect EU users’ data against possible surveillance and unconsented usage by US authorities or other authorised entities.

  Why European Regulators Are After The US Big Tech Firms ? 

  GDPR regulations have been a sore area of compliance for US-based big tech companies. 

  Effectively, they had to adopt a host of new measures for collecting user consent, ensuring compliant data storage and the right to request data removal for a substantial part of their user bases. 

  The wrinkle, however, is that companies like Google and Meta among others, don’t have separate data processing infrastructure for different markets. Instead, all the user data gets commingled on the companies’ servers, which are located in the US. 

  Data storage facilities’ location is an issue. In 2020, the CJEU made a historical ruling, called the invalidation of the Privacy Shield. Originally, international companies were allowed to transfer data between the EU and the US if they adhered to seven data protection principles. This arrangement was called the Privacy Shield. 

  However, the continuous investigation found that the Privacy Shield scheme was not GDPR compliant and therefore companies could no longer use it to justify cross-border data transfers.

  The invalidation of the Privacy Shield gave ground for further investigations of the big tech companies’ compliance statuses. 

  In March 2022, the Irish DPC issued the first €17 million fine to Meta for “insufficient technical and organisational measures to ensure information security of European users”. In September 2022, Meta was again hit with a €405 million fine for Instagram breaching GDPR principles. 

  2023 began with another series of rulings, with the DPC concluding that Meta had breaches of the GDPR relating to its Facebook service (€210 million fine) and breaches related to Instagram (€180 million fine). 

  Clearly, Meta already knew they weren’t doing enough for GDPR compliance and yet they refused to take privacy-focused action. 

  Is Google GDPR Compliant ?

  Google has a similar “track record” as Meta when it comes to ensuring full compliance with the GDPR. Although Google has said to provide users with more controls for managing their data privacy, the proposed solutions are just scratching the surface. 

  In the background, Google continues to leverage its ample reserves of user browsing, behavioural and device data in product development and advertising. 

  In 2022, the Irish Council for Civil Liberties (ICCL) found that Google used web users’ information in its real-time bidding ad system without their knowledge or consent. The French data regulator (CNIL), in turn, fined Google for €150 million because of poor cookie consent banners the same year. 

  Google Analytics GDPR compliance status is, however, the bigger concern.

  Neither Google Univeral Analytics (UA) nor Google Analytics 4 are GDPR compliant, following the Privacy Shield framework invalidation in 2020. 

  Fines from individual regulators in Sweden, France, Austria, Italy, Denmark, Finland and Norway ruled that Google Analytics is non-GDPR compliant and is therefore illegal to use. 

  The regulatory rulings not just affect Google, but also GA users. Because the product is in breach of European privacy laws, people using it are complacent. Privacy groups like noyb, for example, are exercising their right to sue individual websites, using Google Analytics.

  How to Stay GDPR Compliant With Website Analytics 

  To avoid any potential risk exposure, selectively investigate each website analytics provider’s data storage and management practices. 

  Inquire about the company’s data storage locations among the first things. For example, Matomo Cloud keeps all the data in the EU, while Matomo On-Premise edition gives you the option to store data in any country of your choice. 

  Secondly, ask about their process for consent tracking and subsequent data analysis. Our website analytics product is fully GDPR compliant as we have first-party cookies enabled by default, offer a convenient option of tracking out-outs, provide a data removal mechanism and practice safe data storage. In fact, Matomo was approved by the French Data Protection Authority (CNIL) as one of the few web analytics apps that can be used to collect data without tracking consent. 

  Using an in-built GDPR Manager, Matomo users can implement the right set of controls for their market and their industry. For example, you can implement extra data or IP anonymization ; disable visitor logs and profiles. 

  Thanks to our privacy-by-design architecture and native controls, users can make their Matomo analytics compliant even with the strictest privacy laws like HIPAA, CCPA, LGPD and PECR. 

  Learn more about GDPR-friendly website analytics.

  Final Thoughts

  Since the GDPR came into effect in 2018, over 1,400 fines have been given to various companies in breach of the regulations. Meta and Google have been initially lax in response to European regulatory demands. But as new fines follow and the consumer pressure mounts, Big Tech companies are forced to take more proactive measures : add opt-outs for personalised ads and introduce an alternative mechanism to third-party cookies. 

  Companies, using non-GDPR-compliant tools risk finding themselves in the crossfire of consumer angst and regulatory criticism. To operate an ethical, compliant business consider privacy-focused alternatives to Google products, especially in the area of website analytics. 

• ffmpeg conversion fails for 640x360 video size

  12 novembre 2015, par user3132858

  Can somebody advice how I can fix the ffmpeg error below :

  Array ( [0] => ffmpeg version 2.0.1 Copyright (c) 2000-2013 the FFmpeg developers 
  
  [1] => built on Nov 11 2015 12:17:12 with gcc 4.4.7 (GCC) 20120313 (Red Hat 4.4.7-16) [2] 
  
  => configuration: --enable-gpl --enable-version3 --enable-shared --enable-nonfree 
  
  --enable-postproc --enable-libfaac --enable-libmp3lame --enable-libopencore-amrnb 
  
  --enable-libopencore-amrwb --enable-libtheora --enable-libvorbis --enable-libvpx 
  
  --enable-libx264 --enable-libxvid [3] => libavutil 52. 38.100 / 52. 38.100 [4]
  
  => libavcodec 55. 18.102 / 55. 18.102 [5] => libavformat 55. 12.100 / 55. 12.100 [6] 
  
  => libavdevice 55. 3.100 / 55. 3.100 [7] => libavfilter 3. 79.101 / 3. 79.101 [8] 
  
  => libswscale 2. 3.100 / 2. 3.100 [9] => libswresample 0. 17.102 / 0. 17.102 [10] 
  
  => libpostproc 52. 3.100 / 52. 3.100 [11] => Input #0, mov,mp4,m4a,3gp,3g2,mj2, from '3.mov': [12] 
  
  => Metadata: [13] => major_brand : qt [14] => minor_version : 537199360 [15] => compatible_brands: qt [16] 
  
  => creation_time : 2005-10-17 22:54:32 [17] => Duration: 00:01:25.50, start: 0.000000, 
  
  bitrate: 307 kb/s [18] => Stream #0:0(eng): Video: mpeg4 (Simple Profile) (mp4v / 0x7634706D), 
  
  yuv420p, 640x480 [SAR 1:1 DAR 4:3], 261 kb/s, 10 fps, 10 tbr, 3k tbn, 25 tbc [19] 
  
  => Metadata: [20] => creation_time : 2005-10-17 22:54:33 [21] => handler_name : 
  
  Apple Video Media Handler [22] => Stream #0:1(eng): Audio: aac (mp4a / 0x6134706D), 
  
  32000 Hz, mono, fltp, 43 kb/s [23] => Metadata: [24] => creation_time : 2005-10-17 22:54:34 [25] 
  
  => handler_name : Apple Sound Media Handler [26] => [libx264 @ 0x19fbb00] using SAR=944/945 [27] 
  
  => [libx264 @ 0x19fbb00] using cpu capabilities: MMX2 SSE2Fast SSSE3 SSE4.2 AVX [28] 
  
  => Output #0, mp4, to '4.mp4': [29] => Metadata: [30] => major_brand : qt [31] 
  
  => minor_version : 537199360 [32] => compatible_brands: qt [33] => Stream #0:0
  
  (eng): Video: h264, yuv420p, 630x472 [SAR 944:945 DAR 4:3], q=-1--1, 900 kb/s, 90k tbn, 
  
  10 tbc [34] => Metadata: [35] => creation_time : 2005-10-17 22:54:33 [36] 
  
  => handler_name : Apple Video Media Handler [37] => Stream #0:1(eng): Audio: aac, 32000 Hz, 
  
  mono, fltp, 128 kb/s [38] => Metadata: [39] => creation_time : 2005-10-17 22:54:34 [40] 
  
  => handler_name : Apple Sound Media Handler [41] => Stream mapping: [42] 
  
  => Stream #0:0 -> #0:0 (mpeg4 -> libx264) [43] => Stream #0:1 -> #0:1 (aac -> aac) [44] 
  
  => Error while opening encoder for output stream #0:0 - maybe incorrect parameters 
  
  such as bit_rate, rate, width or height )

  This is my code :

  $width=640;
  
  $height=360;
  
  $aspect=round($width/$height, 1);
  
  
  
  $command = "/usr/bin/ffmpeg -threads 1 -y -i 3.mov -vf \"scale=min(1\,gt(iw\,".$width.")+gt(ih\,".$height.")) * (gte(a\,".$aspect.")*".$width." + \
  
  lt(a\,".$aspect.")*((".$height."*iw)/ih)) + not(min(1\,gt(iw\,".$width.")+gt(ih\,".$height.")))*iw : \
  
  min(1\,gt(iw\,".$width.")+gt(ih\,".$height.")) * (lte(a\,".$aspect.")*".$height." + \
  
  gt(a\,".$aspect.")*((".$width."*ih)/iw)) + not(min(1\,gt(iw\,".$width.")+gt(ih\,".$height.")))*ih\"  -b:v 900k -acodec aac -strict -2 -ab 96k 4.mp4 ";

  It is rather strange but when I change the width and height parameters to 300 and 200 respectively, the command executes, the error appears when the width x height is 640x360.

  Any ideas of what might be wrong ?

  UPDATE : I am pretty sure the error has got something to do with the audio encoding. If I change the -ab 96k part to -c:a copy, command executes.
  I cannot however use -c:a copy because for some audio formats the video does not play on mobile devices. Therefore i need the audio settings to be fixed and the same for all videos.