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• doc/muxers/image2 : drop unmatched quote in example

  11 mai 2023, par Andriy Utkin

  doc/muxers/image2 : drop unmatched quote in example
  Percent sign is not a special character in popular shells, so the
  
  quoting isn't necessary.
  

  • [DH] doc/muxers.texi

• 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

• How to Choose the Optimal Multi-Touch Attribution Model for Your Organisation

  13 mars 2023, par Erin — Analytics Tips

  If you struggle to connect the dots on your customer journeys, you are researching the correct solution. 

  Multi-channel attribution models allow you to better understand the users’ paths to conversion and identify key channels and marketing assets that assist them.

  That said, each attribution model has inherent limitations, which make the selection process even harder.

  This guide explains how to choose the optimal multi-touch attribution model. We cover the pros and cons of popular attribution models, main evaluation criteria and how-to instructions for model implementation. 

  Pros and Cons of Different Attribution Models 

  

  First Interaction 

  First Interaction attribution model (also known as first touch) assigns full credit to the conversion to the first channel, which brought in a lead. However, it doesn’t report other interactions the visitor had before converting.

  Marketers, who are primarily focused on demand generation and user acquisition, find the first touch attribution model useful to evaluate and optimise top-of-the-funnel (ToFU). 

  Pros 

  • Reflects the start of the customer journey
  • Shows channels that bring in the best-qualified leads 
  • Helps track brand awareness campaigns

  Cons 

  • Ignores the impact of later interactions at the middle and bottom of the funnel 
  • Doesn’t provide a full picture of users’ decision-making process 

  Last Interaction 

  Last Interaction attribution model (also known as last touch) shifts the entire credit allocation to the last channel before conversion. But it doesn’t account for the contribution of all other channels. 

  If your focus is conversion optimization, the last-touch model helps you determine which channels, assets or campaigns seal the deal for the prospect. 

  Pros 

  • Reports bottom-of-the-funnel events
  • Requires minimal data and configurations 
  • Helps estimate cost-per-lead or cost-per-acquisition

  Cons 

  • No visibility into assisted conversions and prior visitor interactions 
  • Overemphasise the importance of the last channel (which can often be direct traffic) 

  Last Non-Direct Interaction 

  Last Non-Direct attribution excludes direct traffic from the calculation and assigns the full conversion credit to the preceding channel. For example, a paid ad will receive 100% of credit for conversion if a visitor goes directly to your website to buy a product. 

  Last Non-Direct attribution provides greater clarity into the bottom-of-the-funnel (BoFU). events. Yet, it still under-reports the role other channels played in conversion. 

  Pros 

  • Improved channel visibility, compared to Last-Touch 
  • Avoids over-valuing direct visits
  • Reports on lead-generation efforts

  Cons 

  • Doesn’t work for account-based marketing (ABM) 
  • Devalues the quality over quantity of leads 

  Linear Model

  Linear attribution model assigns equal credit for a conversion to all tracked touchpoints, regardless of their impact on the visitor’s decision to convert.

  It helps you understand the full conversion path. But this model doesn’t distinguish between the importance of lead generation activities versus nurturing touches.

  Pros 

  • Focuses on all touch points associated with a conversion 
  • Reflects more steps in the customer journey 
  • Helps analyse longer sales cycles

  Cons 

  • Doesn’t accurately reflect the varying roles of each touchpoint 
  • Can dilute the credit if too many touchpoints are involved 

  Time Decay Model 

  Time decay models assumes that the closer a touchpoint is to the conversion, the greater its influence. Pre-conversion touchpoints get the highest credit, while the first ones are ranked lower (5%-5%-10%-15%-25%-30%).

  This model better reflects real-life customer journeys. However, it devalues the impact of brand awareness and demand-generation campaigns. 

  Pros 

  • Helps track longer sales cycles and reports on each touchpoint involved 
  • Allows customising the half-life of decay to improve reporting 
  • Promotes conversion optimization at BoFu stages

  Cons 

  • Can prompt marketers to curtail ToFU spending, which would translate to fewer qualified leads at lower stages
  • Doesn’t reflect highly-influential events at earlier stages (e.g., a product demo request or free account registration, which didn’t immediately lead to conversion)

  Position-Based Model 

  Position-Based attribution model (also known as the U-shaped model) allocates the biggest credit to the first and the last interaction (40% each). Then distributes the remaining 20% across other touches. 

  For many marketers, that’s the preferred multi-touch attribution model as it allows optimising both ToFU and BoFU channels. 

  Pros 

  • Helps establish the main channels for lead generation and conversion
  • Adds extra layers of visibility, compared to first- and last-touch attribution models 
  • Promotes budget allocation toward the most strategic touchpoints

  Cons 

  • Diminishes the importance of lead nurturing activities as more credit gets assigned to demand-gen and conversion-generation channels
  • Limited flexibility since it always assigns a fixed amount of credit to the first and last touchpoints, and the remaining credit is divided evenly among the other touchpoints

  How to Choose the Right Multi-Touch Attribution Model For Your Business 

  If you’re deciding which attribution model is best for your business, prepare for a heated discussion. Each one has its trade-offs as it emphasises or devalues the role of different channels and marketing activities.

  To reach a consensus, the best strategy is to evaluate each model against three criteria : Your marketing objectives, sales cycle length and data availability. 

  Marketing Objectives 

  Businesses generate revenue in many ways : Through direct sales, subscriptions, referral fees, licensing agreements, one-off or retainer services. Or any combination of these activities. 

  In each case, your marketing strategy will look different. For example, SaaS and direct-to-consumer (DTC) eCommerce brands have to maximise both demand generation and conversion rates. In contrast, a B2B cybersecurity consulting firm is more interested in attracting qualified leads (as opposed to any type of traffic) and progressively nurturing them towards a big-ticket purchase. 

  When selecting a multi-touch attribution model, prioritise your objectives first. Create a simple scoreboard, where your team ranks various channels and campaign types you rely on to close sales. 

  Alternatively, you can survey your customers to learn how they first heard about your company and what eventually triggered their conversion. Having data from both sides can help you cross-validate your assumptions and eliminate some biases. 

  Then consider which model would best reflect the role and importance of different channels in your sales cycle. Speaking of which….

  Sales Cycle Length 

  As shoppers, we spend less time deciding on a new toothpaste brand versus contemplating a new IT system purchase. Factors like industry, business model (B2C, DTC, B2B, B2BC), and deal size determine the average cycle length in your industry. 

  Statistically, low-ticket B2C sales can happen within just several interactions. The average B2B decision-making process can have over 15 steps, spread over several months. 

  That’s why not all multi-touch attribution models work equally well for each business. Time-decay suits better B2B companies, while B2C usually go for position-based or linear attribution. 

  Data Availability 

  Businesses struggle with multi-touch attribution model implementation due to incomplete analytics data. 

  Our web analytics tool captures more data than Google Analytics. That’s because we rely on a privacy-focused tracking mechanism, which allows you to collect analytics without showing a cookie consent banner in markets outside of Germany and the UK. 

  Cookie consent banners are mandatory with Google Analytics. Yet, almost 40% of global consumers reject it. This results in gaps in your analytics and subsequent inconsistencies in multi-touch attribution reports. With Matomo, you can compliantly collect more data for accurate reporting. 

  Some companies also struggle to connect collected insights to individual shoppers. With Matomo, you can cross-attribute users across browning sessions, using our visitors’ tracking feature. 

  When you already know a user’s identifier (e.g., full name or email address), you can track their on-site behaviours over time to better understand how they interact with your content and complete their purchases. Quick disclaimer, though, visitors’ tracking may not be considered compliant with certain data privacy laws. Please consult with a local authority if you have doubts. 

  How to Implement Multi-Touch Attribution

  Multi-touch attribution modelling implementation is like a “seek and find” game. You have to identify all significant touchpoints in your customers’ journeys. And sometimes also brainstorm new ways to uncover the missing parts. Then figure out the best way to track users’ actions at those stages (aka do conversion and events tracking). 

  Here’s a step-by-step walkthrough to help you get started. 

  Select a Multi-Touch Attribution Tool 

  The global marketing attribution software is worth $3.1 billion. Meaning there are plenty of tools, differing in terms of accuracy, sophistication and price.

  To make the right call prioritise five factors :

  • Available models : Look for a solution that offers multiple options and allows you to experiment with different modelling techniques or develop custom models. 
  • Implementation complexity : Some providers offer advanced data modelling tools for creating custom multi-touch attribution models, but offer few out-of-the-box modelling options. 
  • Accuracy : Check if the shortlisted tool collects the type of data you need. Prioritise providers who are less dependent on third-party cookies and allow you to identify repeat users. 
  • Your marketing stack : Some marketing attribution tools come with useful add-ons such as tag manager, heatmaps, form analytics, user session recordings and A/B testing tools. This means you can collect more data for multi-channel modelling with them instead of investing in extra software. 
  • Compliance : Ensure that the selected multi-attribution analytics software wouldn’t put you at risk of GDPR non-compliance when it comes to user privacy and consent to tracking/analysis. 

  Finally, evaluate the adoption costs. Free multi-channel analytics tools come with data quality and consistency trade-offs. Premium attribution tools may have “hidden” licensing costs and bill you for extra data integrations. 

  Look for a tool that offers a good price-to-value ratio (i.e., one that offers extra perks for a transparent price). 

  Set Up Proper Data Collection 

  Multi-touch attribution requires ample user data. To collect the right type of insights you need to set up : 

  • Website analytics : Ensure that you have all tracking codes installed (and working correctly !) to capture pageviews, on-site actions, referral sources and other data points around what users do on page. 
  • Tags : Add tracking parameters to monitor different referral channels (e.g., “facebook”), campaign types (e.g., ”final-sale”), and creative assets (e.g., “banner-1”). Tags help you get a clearer picture of different touchpoints. 
  • Integrations : To better identify on-site users and track their actions, you can also populate your attribution tool with data from your other tools – CRM system, A/B testing app, etc. 

  Finally, think about the ideal lookback window — a bounded time frame you’ll use to calculate conversions. For example, Matomo has a default windows of 7, 30 or 90 days. But you can configure a custom period to better reflect your average sales cycle. For instance, if you’re selling makeup, a shorter window could yield better results. But if you’re selling CRM software for the manufacturing industry, consider extending it.

  Configure Goals and Events 

  Goals indicate your main marketing objectives — more traffic, conversions and sales. In web analytics tools, you can measure these by tracking specific user behaviours. 

  For example : If your goal is lead generation, you can track :

  • Newsletter sign ups 
  • Product demo requests 
  • Gated content downloads 
  • Free trial account registration 
  • Contact form submission 
  • On-site call bookings 

  In each case, you can set up a unique tag to monitor these types of requests. Then analyse conversion rates — the percentage of users who have successfully completed the action. 

  To collect sufficient data for multi-channel attribution modelling, set up Goal Tracking for different types of touchpoints (MoFU & BoFU) and asset types (contact forms, downloadable assets, etc). 

  Your next task is to figure out how users interact with different on-site assets. That’s when Event Tracking comes in handy. 

  Event Tracking reports notify you about specific actions users take on your website. With Matomo Event Tracking, you can monitor where people click on your website, on which pages they click newsletter subscription links, or when they try to interact with static content elements (e.g., a non-clickable banner). 

  Using in-depth user behavioural reports, you can better understand which assets play a key role in the average customer journey. Using this data, you can localise “leaks” in your sales funnel and fix them to increase conversion rates.

  Test and Validated the Selected Model 

  A common challenge of multi-channel attribution modelling is determining the correct correlation and causality between exposure to touchpoints and purchases. 

  For example, a user who bought a discounted product from a Facebook ad would act differently than someone who purchased a full-priced product via a newsletter link. Their rate of pre- and post-sales exposure will also differ a lot — and your attribution model may not always accurately capture that. 

  That’s why you have to continuously test and tweak the selected model type. The best approach for that is lift analysis. 

  Lift analysis means comparing how your key metrics (e.g., revenue or conversion rates) change among users who were exposed to a certain campaign versus a control group. 

  In the case of multi-touch attribution modelling, you have to monitor how your metrics change after you’ve acted on the model recommendations (e.g., invested more in a well-performing referral channel or tried a new brand awareness Twitter ad). Compare the before and after ROI. If you see a positive dynamic, your model works great. 

  The downside of this approach is that you have to invest a lot upfront. But if your goal is to create a trustworthy attribution model, the best way to validate is to act on its suggestions and then test them against past results. 

  Conclusion

  A multi-touch attribution model helps you measure the impact of different channels, campaign types, and marketing assets on metrics that matter — conversion rate, sales volumes and ROI. 

  Using this data, you can invest budgets into the best-performing channels and confidently experiment with new campaign types. 

  As a Matomo user, you also get to do so without breaching customers’ privacy or compromising on analytics accuracy.

  Start using accurate multi-channel attribution in Matomo. Get your free 21-day trial now. No credit card required.