Recherche avancée

Sur d’autres sites (11343)

• 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. 

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

  17 mars 2023, par Erin — Analytics Tips

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

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

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

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

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

  Google Optimize vs Matomo : Key Capabilities Compared 

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

  Supported Platforms 

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

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

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

  A/B Testing 

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

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

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

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

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

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

  

  Multivariate testing (MVT)

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

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

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

  Redirect Tests

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

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

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

  Experiment Design 

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

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

  Experiment Configuration 

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

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

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

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

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

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

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

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

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

  Reporting 

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

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

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

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

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

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

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

  User Privacy & GDPR Compliance 

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

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

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

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

  Digital Experience Intelligence 

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

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

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

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

  Integrations 

  Both Matomo and Google Optimize integrate with multiple other tools. 

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

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

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

  Pricing 

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

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

  Google Optimize vs Matomo A/B Testing : Comparison Table

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

• 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.