Shall we not talk about AI for once? Likely your data set is not ready for it. Just not yet.

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 behavioural 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 adblockers 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.

Harm Linssen

Co-Founder | Code Cube

Suze Löbker

Co-Founder | Code Cube