Data-driven marketing should be the norm these days. Unfortunately, data collection is becoming increasingly difficult and has to deal with various challenges.
For a start, data collection is severely restricted by ad blockers. Data collection is also affected by the new data protection regulations and by technical restrictions such as ITP (Internet Tracking Prevention), where browsers such as Safari or Firefox block third-party cookies by default.
Google’s requirements now come on top of this: With Consent Mode 2.0, Google is forcing website operators to obtain user consent via a cookie management platform in order to continue using remarketing campaigns.
This means that the data strategy for your marketing data is becoming increasingly important, and it is a decisive advantage if you have a well-thought-out data strategy that is set up with the right tools.
But what data does this include, and what does the data flow for marketing data look like? There are currently over 9,000 different marketing tools. How can you identify which are the right ones for your individual requirements?
This overview shows the data landscape and the data flow of marketing data. Each color represents a different category of tools.
Data Collection Tools
Data collection tools, i.e. tools that collect data on websites or apps, are marked in dark green.
Tag management systems (TMS) are used to implement tracking mechanisms with which user data can be collected through simple tag management, without extensive programming of the website code. The data collection can be carried out on the client side as it was the standard so far. However, it is smarter to do this on the server side so that as much data as possible can be collected. We have already shown the advantages of server-side tracking in this article.
Another essential part of data collection tools are web analytics solutions. These tools measure the performance of websites, apps and online marketing campaigns as well as user behavior.
A selection of tag management tools (client-side and server-side) can be found below, as well as examples of common web analytics tools:
Data Storage Tools
Data storage tools are needed to ensure that your data is securely stored, easily accessible and available at all times for analysis, processing and operative use.
Data warehouses and customer data platforms (CDPs) make it possible to store data collected from different touchpoints. A data warehouse is designed to store and query large volumes of structured data, while customer data platforms are used to standardize customer data from different sources and create a comprehensive real-time customer profile.
Data Visualization Tools
Visualization tools are used to understand complex data sets, identify trends and present and communicate data, findings, or patterns. By using meaningful visualizations in dashboards and reports, data can be better interpreted and thus data-based and actionable decisions can be made.
Conversion Optimization Tools
Conversion optimization tools aim to improve the user experience, increase the performance of each marketing channel and enable more targeted and effective customer interactions.
Heatmap tracking provides visual insights into how users interact with a website. A/B testing enables the comparison of two or more versions of a web page or a campaign to determine which version performs better based on specific metrics.
Marketing Tools
On the one hand, marketing tools include marketing platforms such as Meta or Google. On the other, we have marketing automation tools that are used to automate and measure marketing tasks and processes. These tools allow repeatable processes such as email campaigns, social media posts or lead nurturing to be managed efficiently.
Summary
The data flow of marketing data is spread across different tool categories and involves a large amount of data. A multitude of platforms and tools need to be connected, and a coherent ecosystem needs to be created to guarantee a seamless data flow. This seamless data flow is the foundation of your data strategy and the only way data-driven marketing is still feasible.