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• Server-side tracking vs client-side tracking : What you need to know

  3 juillet, par Joe

  Server-side tracking vs client-side tracking : What you need to know

  Today, consumers are more aware of their online privacy rights, leading to an extensive use of ad blockers and stricter cookie policies. Organisations are facing some noteworthy challenges with this trend, including :

  • Limited data collection, which makes it harder to understand user behaviour and deliver personalised ads that resonate with customers
  • Rising compliance costs as businesses adapt to new regulations, straining resources and budgets.
  • Growing customer scepticism in data practices, affecting brand reputation.
  • Maintaining transparency and fostering trust with customers through clear communication about data practices.

  Server-side tracking can help resolve these problems. This article will cover server-side tracking, how it works, implementation methods and its benefits.

  What is server-side tracking ? 

  Server-side tracking refers to a method where user data is collected directly by a server rather than through a user’s browser.

  The key advantage of server-side tracking is that data collection, processing, and storage occur directly on the website’s server.

  For example, when a visitor interacts with any website, the server captures that activity through the backend system, allowing for greater data control and security. 

  Client-side tracking vs. server-side tracking 

  There are two methods to collect user data : client-side and server-side. 

  Let’s understand their differences. 

  Client-side tracking : Convenience with caveats

  Client-side tracking embeds JavaScript tags, pixels or other scripts directly into a website’s code. When a user interacts with the site, these tags fire, collecting data from their browser. This information might include page views, button clicks, form submissions and other user actions. 

  The collected data is then sent directly to third-party analytics platforms like Google Analytics or Adobe Analytics, or internal teams can also analyse it.

  This method is relatively easy to implement. That’s because marketers can often deploy these tags without needing extensive developer support, enabling quick adjustments and A/B testing. 

  However, there are some challenges. 

  Ad blockers and browser privacy settings, such as Intelligent Tracking Prevention (ITP), restrict the ability of third-party tags to collect data. 

  This results in data gaps and inaccuracies skewing analytics reports and potentially leading to misguided business decisions. 

  Reliance on numerous JavaScript tags can also negatively impact website performance, slowing down page load times and affecting user experience. This is especially true on mobile devices where processing power and network speeds are often limited.

  

  Now, let’s see how server-side tracking changes this.

  Server-side tracking : Control and reliability

  Server-side tracking shifts the burden of data collection from the user’s browser to a server controlled by the business. 

  Instead of relying on JavaScript tags firing directly from the user’s device, user interactions are first sent to the business’s own server. Here, the data can be processed, enriched, and analysed. 

  This method provides numerous advantages, including enhanced control over data integrity, improved privacy, and more, which we discuss in the next section.

  Benefits of server-side tracking 

  Server-side tracking offers a compelling alternative to traditional client-side methods, providing numerous business advantages. Let’s take a look at them.

  Improved data accuracy

  This method reduces inaccuracies caused by ad blockers or cookie restrictions by bypassing browser limitations. As a result, the data collected is more reliable, leading to better analytics and marketing attribution.

  Data minimisation

  Data minimisation is a fundamental principle in data protection. It emphasises that organisations should collect only data that is strictly needed for a specific purpose. 

  In server-side tracking, this translates into collecting just the essential data points and discarding anything extra before the data is sent to analytics platforms. It helps organisations avoid accumulating excessive personal information, reducing the risk of data breaches and misuse.

  For example, consider a scenario where a user purchases a product on an e-commerce website. 

  With client-side tracking scripts, the system might inadvertently collect a range of data, including the user’s IP address, browser type, operating system and even details about other websites they have visited. 

  However, for conversions, the organisation only needs to know the purchase amount, product IDs, user IDS, and timestamps. 

  Server-side tracking filters unnecessary information. This reduces the privacy impact and simplifies data analysis and storage.

  Cross-device tracking capabilities

  Server-side tracking provides a unified view of customer behaviour regardless of the device they use, allowing for more personalised and targeted marketing campaigns. 

  In-depth event tracking

  Server-side tracking helps businesses track events that occur outside their websites, such as payment confirmations. Companies gain insights into the entire customer journey, from initial interaction to final purchase, optimising every touchpoint. 

  Enhanced privacy compliance

  With increasing regulations like GDPR and CCPA, businesses can better manage user consent and data handling practices through server-side solutions. 

  Server-side setups make honouring user consent easier. If a user opts out, server-side logic can exclude their data from all outgoing analytics calls in one central place. 

  

  Server-side methods reassure users and regulators that data is collected and secured with minimal risk. 

  In sectors like government and banking, this level of control is often a non-negotiable part of their duty of care. 

  Extended cookie lifetime

  Traditional website tracking faces growing obstacles as modern browsers prioritise user privacy. Initiatives like Safari’s ITP block third-party cookies and also constrain the use of first-party cookies. 

  Other browsers, such as Firefox and Brave, are implementing similar methods, while Chrome is beginning to phase out third-party cookies. Retargeting and cross-site analytics, which rely on these cookies, encounter significant challenges.

  Server-side tracking overcomes this by allowing businesses to collect data over a longer duration. 

  When a website’s server directly sets a cookie, that cookie often lasts longer than cookies created by JavaScript code running inside the browser. This lets websites get around some of the limits browsers put on tracking and allows them to remember a visitor when they return to the site later, which gives better customer insights. Plus, server-side tracking typically classifies cookies as first-party data, which is less susceptible to blocking by browsers and ad blockers.

  Server-side tracking : Responsibilities and considerations

  While server-side tracking delivers powerful capabilities, remember that it also brings increased responsibility. Companies must remain vigilant in upholding privacy regulations and user consent. It’s up to the organisation to make sure the server follows user consent, for example, not sending data if someone has opted out.

  Server-side setups introduce technical complexity, which can potentially lead to data errors that are more difficult to identify and resolve. Therefore, monitoring processes and quality assurance practices are essential for data integrity. 

  How does server-side tracking work ? 

  When a user interacts with a website (e.g., clicking a button), this action triggers an event. The event could be anything from a page view to a form submission.

  The backend system captures relevant details such as the event type, user ID and timestamp. This information helps in understanding user behaviour and creating meaningful analytics.

  The captured data is processed directly on the organisation’s server, allowing for immediate validation. For example, organisations can add additional context or filter out irrelevant information.

  Instead of sending data to third-party endpoints, the organisation stores everything in its own database or data warehouse. This ensures full control over data privacy and security.

  Organisations can perform their own analysis using tools like SQL or Python. To visualise data, custom dashboards and reports can be created using self-hosted analytics tools. This way, businesses can present complex data in a clear and actionable manner.

  How to implement server-side tracking ?

  Server-side tracking can work in four common ways, each offering a different blend of control, flexibility and complexity.

  1. Server-side tag management

  In this method, organisations use platforms like Google Tag Manager Server-Side to manage tracking tags on the server, often using containers to isolate and manage different tagging environments. 

  

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  This approach offers a balance between control and ease of use. It allows for the deployment and management of tags without modifying the application code, which is particularly useful for marketers who want to adjust tracking configurations quickly.

  2. Direct server-to-server tracking via APIs

  This method involves sharing information between two servers without affecting the user’s browser or device. 

  A unique identifier is generated and stored on a server when a user interacts with an ad or webpage. 

  If a user takes some action, like making a purchase, the unique identifier is sent from the advertiser’s server directly to the platform’s server (Google or Facebook) via an API. 

  It requires more development effort but is ideal for organisations needing fine-grained data control.

  3. Using analytics platforms with built-in server SDKs

  Another way is to employ analytics platforms like Matomo that provide SDKs for various programming languages to instrument the server-side code. 

  This eases integration with the platform’s analytics features and is a good choice for organisations primarily using a single analytics platform and want to use its server-side capabilities.

  4. Hybrid approaches

  Finally, organisations can also combine client- and server-side tracking to capture different data types and maximise accuracy. 

  This method involves client-side scripts for specific interactions (like UI events) and server-side tracking for more sensitive or critical data (like transactions). 

  While these are general approaches, dedicated analytics platforms can also be helpful. Matomo, for example, facilitates server-side tracking through two specific methods.

  Using server logs

  Matomo can import existing web server logs, such as Apache or Nginx, that capture each request. Every page view or resource load becomes a data point. 

  Matomo’s log processing script reads log files, importing millions of hits. This removes the need to add code to the site, making it suitable for basic page analytics (like the URL) without client-side scripts, particularly on security-sensitive sites.

  Using the Matomo tracking API (Server-side SDKs)

  This method integrates application code with calls to Matomo’s API. For example, when a user performs a specific action, the server sends a request to Matomo.php, the tracking endpoint, which includes details like the user ID and action. 

  Matomo offers SDKs in PHP, Java C#, and community SDKs to simplify these calls. These allow tracking of not just page views but custom events such as downloads and transactions from the backend, functioning similarly to Google’s Measurement Protocol but sending data to the Matomo instance. 

  Data privacy, regulations and Matomo

  As privacy concerns grow and regulations like GDPR and CCPA become more stringent, businesses must adopt data collection methods that respect user consent and data protection rights. 

  Server-side tracking allows organisations to collect first-party data directly from their servers, which is generally considered more compliant with privacy regulations.

  Matomo is a popular open-source web analytics platform that is committed to privacy. It gives organisations 100% data ownership and control, and no data is sent to third parties by default.

  

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  Matomo is a full-featured analytics platform with dashboards and segmentation comparable to Google Analytics. It can self-host and provides DoNotTrack settings and the ability to anonymise IP addresses.

  Governments and organisations requiring data sovereignty, such as the EU Commission and the Swiss government, choose Matomo for web analytics due to its strong compliance posture.

  Balancing data collection and user privacy

  Ad blockers and other restrictions prevent data from being accurate. Server-side tracking helps get data on the server and makes it more reliable while respecting user privacy. Matomo supports server-side tracking, and over one million websites use Matomo to optimise their data strategies. 

  Get started today by trying Matomo for free for 21 days, 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.