Author Archives: CodeCubeAdmin

How Tag Volume monitoring is critical for data-driven marketing

In marketing, data is the engine that drives everything. Decisions are made in the dark when there is not enough of it. To make the right decisions about campaigns, budgets, and strategy, it is a priority to have enough data points to determine what is and is not working. The real problem occurs when tags suddenly stop collecting data without anyone noticing. Most organizations are unaware of how often this occurs.

When data collection goes silent

When tags aren’t firing consistently – important business decisions are based on only a small portion of actual user data. Most organizations have experienced these tag problems:

  • Profitable campaigns appear unsuccessful because conversion tracking only records a small percentage of sales.
  • Thousands of dollars are spent on advertisements, but tags quietly miss half of conversions.
  • When tags for specific user groups stop to function, entire customer segments are removed from analytics.

The most dangerous aspect is the silence. Dashboards don’t flash red warnings or reports don’t alert on missing data. Decisions are still being made based on information that is becoming less and less trustworthy.

The growing complexity of tag management

Tracking is more unreliable than ever. Browser updates, developer changes, cookie settings, and speed optimizations can all break tags without warning. One failure can trigger others, quietly disrupting data collection. Even small site speed improvements can unintentionally cut tags short, while cookie consent rules how data is gathered. The real danger? Hidden dependencies. One tagging error can lead into multiple errors due to complex dependencies and set-ups.

Worst of all, these issues rarely raise red flags. Tags might fire at a fraction of their normal volume, yet dashboards and monitoring tools make it look like everything’s fine.

The real cost of tag volume drops

The impact of tags missing data is extensive, such as:

  • Bad decisions: Teams make budget choices based on incomplete data.
  • Wasted spending: Money flows to channels that only look effective because their tracking works better.
  • Falling behind competitors: They use complete data while you work with partial information.
  • Missing UX problems: Customer issues go undetected when tracking fails to capture them.
  • Loss of data trust: Teams return to gut feelings after seeing too many unexplained data fluctuations.

When tag volumes silently drop, immediate consequences follow:

  1. Effective campaigns get paused because performance metrics appear poor
  2. Investment increases in channels that seem successful but are actually just tracking more consistently
  3. Leadership receives reports of declining performance when the only actual problem is compromised tracking

This leads to wasted resources, missed opportunities, and damaged credibility. All because tag firing rates weren’t being monitored.

Introducing Tag Volume monitoring

Undetected drops in tag firing volumes can corrupt your marketing decisions for days before you notice. That’s why we built a functionality for Tag volume monitoring with the Tag Monitor – to ensure your data collection volume never silently declines again.

Rather than discovering data collection problems during monthly reporting cycles (when it’s far too late), organizations learn within minutes when a tag begins underperforming.

How Tag Monitor protects your data volumes:
  • Set individual rules for monitoring per tag, page or domain.
  • Our tool learns the daily and weekly patterns in your tag firing volumes and alerts you to anomalies.
  • Get notifications via email, Slack, or Teams the moment tag volumes drop below expected thresholds.
  • See tag firing frequency over time in the Tag Monitor dashboard to spot gradual decreasing data before it becomes a problem.

With Tag Monitor, you transform those gut feelings about declining data quality into concrete volume alerts – so you always know your tags are firing at the expected frequency and your marketing decisions are based on complete information.

Take control of your tag volumes

The most successful digital marketers I know don’t just analyze data – they verify its collection volume and consistency. Code Cube’s Tag Monitor gives you confidence that you’re capturing the expected volume of interactions, not just a fraction of what should be tracked.

Start monitoring your tag firing frequency today and ensure every marketing decision is based on complete data collection.

At Code Cube, we build data monitoring tools that protect marketing teams from errors or data loss in there web-analytics or marketing systems. Our Tag Monitor, ensures your analytics and tracking systems capture every interaction at the expected volume, giving you complete confidence in your marketing data.

Shall we not talk about AI for once?

Likely your data set is not ready for it. Just not yet.

Did you happen to see the Netflix documentary about Fyre, the once in a lifetime luxury music festival in the Bahamas?

In case you missed it, the overhyped festival, a supposed mix between Coachella and Burning Man, ended up becoming “The Greatest Party That Never Happened”. Everyone was talking about it, it was overhyped, people bought tickets driven by “Fear Of Missing Out” and the organisers had little or no experience with organising such a large festival.

Why is this relevant for a blog on the website of the Digital Analytics Summit you might wonder? Well, there are actually many parallels between the current state of AI and the Fyre Festival. Everyone is talking about AI, it is a bit overhyped, there is a lack of experience and marketers are suffering from “Fear Of Missing Out”.

But there is no need to be afraid you will miss out on AI. Conferences like the Digital Analytics Summit and blogs like this one, give you valuable tips to help avoid making the same mistakes as others made before you.

Recent data collection challenges

With online marketing being a significant and indispensable driver for traffic to any webshop, the importance of data is growing year after year. In the “State of Martech 2024” report by Chiefmartec 71% of the respondents (leading martech and marketing operations professionals), reported that they’ve already integrated a data warehouse within their martech stack.

We all acknowledge the importance of data, and we all have to continuously navigate between collecting valuable data on the one hand while still respecting the customer’s privacy and being legally compliant on the other hand.

In the recent past we have seen businesses migrating en masse to server-side tracking (whereas traditionally behavioral data was captured in the browser of the website’s visitor). The goal was clear, to improve data quality and secure it for the longer term because server-side tracking is not affected by ad blockers and tracking prevention settings in browsers.

More recently we have seen almost every website implement Google’s Consent Mode to ensure GDPR compliance while still being able to track valuable data for insights.

Is your data tracking ready for AI?

AI models rely solely on clean and complete data flowing into them. Without correct data, any model lacks the fuel to deliver an intelligent prediction. Therefore your data collection process (which is the foundation of any data driven strategy) should be your top priority. Without validated and rich data, you will never be able to be successful with AI.

So in order to ensure the effective execution of marketing AI you should never forget that garbage in, results in garbage out. This applies to the simplest dashboard in which you monitor basic KPI’s and even more so to AI. Therefore, before starting with AI you need to make sure your data tracking is in order and works permanently. The DataLayer and all tags must do their work well at all times in order to capture- and pass on the right data.

When your tracking works perfectly it will have a significant positive impact on the costs of your first AI project. The correct tracking setup contributes to the data quality and structure and it will eventually save your data scientists lots of valuable time. Instead of wasting time on checking, cleaning and preparing the collected data they can actually spend more time on developing and improving the AI models.

A well working- and well documented tracking setup contributes to transparency and helps explain the AI model’s logic and its predictions to stakeholders or even customers when needed.

Real-time monitoring: Let the AI party begin!

There are some tools available in the market that help you with checking your data collection set-up. What most of these tools don’t do however, is real-time monitoring of the most vulnerable parts in your data collection.

You don’t want your artificial intelligence capabilities to be dependent on the need for human checks. To boost your AI efforts, you will be far better off with a real-monitoring tool which alerts you instantly when something is off and also states in detail what the actual problem is.

Monitoring your dataLayer, tag manager and tags in real-time ensures a constant stream of reliable data at low costs:

  • AI will be working at full speed and full power without any required human interference;
  • There is no longer any need for periodical and time consuming manual (human) checks of the tagging setup;
  • When new code is conflicting with other content or objects it will be instantly detected;
  • Valuable time for debugging is saved by automatically pinpointing the exact cause of an issue and reverse engineering the setup;
  • You will have full control over the tracking on your platform and maximise the quality of the data you collect.
  • Conclusion

    Despite the many challenges and potential pitfalls, the successful implementation of marketing AI is surely feasible.

    Data quality is key because any AI model needs good quality data. It is the fuel needed to deliver intelligent predictions. High quality data will save time and resources for data preparation and maximise the chances of success with AI. Therefore data quality assurance and correct tracking should be your first priority.

    With real-time monitoring of your tracking set-up you have a permanent safeguard in place to protect your data quality. Real-time monitoring of your data collection process will pave the way for being successful with AI.

    Revolutionizing Monitoring: Code Cube’s New Anomaly Detection!

    Code Cube’s has officially taken Tag Monitoring to the next level with an important upgrade – anomaly detection.

    This major enhancement marks a change from the traditional way of tracking tag inactivity issues through static rules. Instead, an advanced AI model is used which is designed to identify anomalies in tag behaviour. Anomalies, simply put, are events, items, or actions that deviate from the normal-, expected behaviour in data. Anomalies based on AI models detect and alert you about irregularities.

    The model is taking many more variables into account than is possible manually with a set of static rules. For example, the model takes seasonality and holidays into account, so the model gives different outliers per hour, day or month. When sudden unexpected increases or decreases for a specific tag happen, the system alerts you via mail, Slack or Teams.

    Why is this upgrade important for me?

    1. Issues will be identified proactively
    You will now be able to detect irregularities in real-time and you can address issues before they impact your campaigns.

    2. You can say goodbye to false positives
    Our AI-driven approach reduces misidentifications, delivering more accurate data for informed decision-making.

    3. Let the technology work for you.
    Anomaly Detection eliminates the need for manual data examination, allowing you to concentrate on optimising your marketing strategy.

    What is the difference with the previous Tag Inactivity model?

    Dynamic Thresholds for Tag Activity
    With static rules there are fixed thresholds which trigger alerts. Potentially this leads to false positives. With the new Anomaly Model the thresholds adjust dynamically based on learned behaviour, accurately detecting anomalies.

    Adaptive Response to Seasonal Changes
    With static rules the system struggles to adapt for example to seasonal variations, resulting in inconsistent alerts. The new Anomaly Model however recognizes patterns and adjusts for seasonal changes, ensuring contextually relevant alerts.

    With anomaly detection we monitor all outliers and exceptions for all your tags. Based on the AI model the expected number of requests is predicted over time – you will be alerted about all tags that fire other than expected.

    The old model was based on static rules; you would be notified when for example a specific tag would completely stop firing.

    What should I do?

    Do you already have an active advanced or premium Tag Monitor licence for your domain? In that case we will automatically activate this new model for you and no action is required.

    If you do not yet take advantage of the benefits of monitoring your tags, this is a good moment to start. Having peace of mind about your tagging is only one click away. Just get in touch with us here.

    You don’t know what you got, till it’s gone

    Why you need to monitor your tracking- and tagging setup

    It is probably the biggest nightmare for online professionals, decision makers and data analysts: unreliable and incomplete data.

    Within any organisation, data is the foundation for strategic decisions, solving problems and the ability to run and assess marketing campaigns. We are increasingly dependent on the quality of the collected data to stay relevant and competitive.

    Moreover, running online marketing campaigns relies for an increasing part on trustworthy data. Algorithms which are used by for instance Google, Microsoft and Meta work more effectively when you feed sufficient and correct data into their systems.

    Therefore it is no surprise that data collection is in the spotlight for quite a while already. Already in 2019 the “tracking rat race” between Safari and Mozilla on one hand and marketers on the other hand was in full swing. Since then many episodes have been added to the saga with most recently the launch of iOS17 and the removal of utm-tracking parameters. With the introduction of new technologies like server-side tagging and the introduction of Data Warehousing the landscape is getting more technical along the way.

    Bad or missing data gives a wrong image of your return on investment, algorithms can’t do their job well and ultimately you are wasting part of your advertising budget.

    Despite all the attention for data collection, the sad reality in most companies is that stakeholders don‘t even know if and when tags are firing or not. Most websites have extensive monitoring in place for their website infrastructure but the functioning of tag containers and tracking tags are frequently overlooked while they have a significant impact. Correct tagging and tracking, a crucial aspect for collecting consistent and accurate data, is almost always overlooked.


    Tracking threats

    1. Tag managers allow marketers to inject code into your website without the help of a developer. At the same time those tag managers don’t have a safeguard in place to stop new code from conflicting with other content or objects. You will not be alerted when tags are not loading, malfunctioning, causing errors and conflicts or slowing down your pages.
    2. Frequent updates on the site as well as third party technologies potentially cause errors which result in a collection gap of several days up to a few weeks. Issues don’t get noticed till it’s too late and the irreparable damage to the data and data quality has already been done.
    3. Dependencies and risks are getting bigger over time due to a growing number of technology partners and tags, each forming a potential point of failure.
    4. A lack of process, lack of knowledge and lack of communication between teams, departments and external agencies poses a risk. This risk is even bigger for companies with multiple domains and larger teams.
      Manual online checks of the tagging setup are time consuming and are not consistent. The results are influenced by for example the time of the day, the location, the device and browser. It’s impossible to test all edge cases.
    5. You can’t influence everything when it comes to tracking and tagging, for example the endpoint could be offline or an API could be malfunctioning without you knowing.

    6. Everyone has the same struggle

      Sometimes you find out by coincidence you have been missing conversions for a specific period and you can only guess how many you have missed based on the numbers of the previous period. During this time you have annoyed all those buying customers with irrelevant retargeting ads. This is money wasted on annoying your customers.

      You are not alone in this struggle. Most websites are not being protected against the tracking threats described above. An average website regularly encounters issues with error percentages of 5% up to 25%. Basically you and most of your competitors currently have no oversight or insight into how tags are impacting data collection and the user experience of your visitors.

      In the land of the blind, one-eyed data collection currently still is king

      You never get a second chance to capture the relevant data of your website’s visitors. Therefore it is fundamental that your tagging- and tracking setup is done in a manner which guarantees a steady and uninterrupted process of data collection. You need to put in place safeguards which alert you realtime when shit hits the fan. This will allow you to act accordingly when something is happening with your tagging, whatever the cause may be.

      Currently the one eyed man can still be king. But what if you could be the two eyed emperor? You can have full control over the tracking on your platform, maximise the quality of the data you collect and your marketing tagging will no longer negatively affect user experience.

      Besides making sure your dataset is up to par, this will save you a lot of time not having to pinpoint the exact cause of the issue and reverse engineer the setup.

      It is possible with real-time monitoring of your tracking and tagging. Automated monitoring ensures a constant stream of reliable data at low costs. Reliable data tracking starts with real-time monitoring and it is the foundation on which any data-driven strategy should be built.

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      This blog was first published on the website of the Digital Analytics Summit an event organised by the DDMA, the largest association for data-driven marketing, sales and service in The Netherlands.

      About the authors: Suze Löbker and Harm Linssen are co-founders of Code Cube