“Marketers who embrace agility and constantly try to push boundaries are the ones that will thrive.” With this phrase, Scott Brinker, one of the world’s leading experts in Martech, imagines the future of marketers. Agility in first place to react effectively to the challenges of a changing world.
At the dawn of an unprecedented digital revolution, companies are facing a critical turning point: adapting to new policies imposed by tech giants like Google and Meta. A change that not only redefines the rules of the game in online advertising but also marks the urgency of a profound transformation in the approach to data collection and management.
Precisely in the days of publication of this article, companies are facing a new great challenge to tackle, in order not to lose the performance of their advertising campaigns on Google and Meta platforms.
In March, in fact, new policies of the big players came into force, a direct consequence of the Digital Acts (DMA, DSA), aimed at containing the dominant position of companies in the digital services market.
The implementation of these acts involves, for example, the mandatory installation of Google Consent Mode V2 in one’s portals, otherwise, in its absence, it will no longer be possible to advertise with Big G’s platform.
In practice, Consent Mode V2 is an additional protection for Google regarding user data collected from advertising. It requires an explicit reference to the user’s choice to send data to Google to receive personalized advertising.
This small example demonstrates how first-party data is increasingly important for all actors in the digital ecosystem, but it is becoming increasingly complex and dynamic to collect, manage, and actually activate it.
From a privacy perspective alone, we now have a granularity and level of detail of information to manage that puts classic consent collection platforms in difficulty.
Everything is pushing more and more towards a dynamic, reactive, and resilient vision of data collection, but how can we achieve it within our current stacks?
Companies, especially large ones, have a long history of first-party data. They have been collecting information about their customers and their business for decades and in every entity of any size. The real challenge today is not finding large quantities of data, but rather succeeding in reconciling and bringing all the information back into a coherent and high-quality center.
This is because, legitimately, over the years companies have purchased and layered many types of software, from ERP to CRM, to the billing system up to web analytics. In the choice of various software, the need to centralize data and then be able to exploit it for marketing and strategic purposes was not so evident, and “Best of Breed” solutions for each type were essentially preferred.
The other approach, apparently more virtuous from a consolidation point of view, was to choose an integrated and all-encompassing solution for all the functions necessary for the company.
The integrated suite approach certainly guaranteed fewer update and synchronization processes between many different suites, but it also carried the feeling of never having the best solution for every need. Furthermore, large integrated suites suffered and suffer from slowness in adapting to market news and low connection with the external ecosystem. For marketers especially, they began to represent insurmountable walls towards performance. In this light, we have witnessed the advent of hybrid solutions, which maintained a centrality of the integrated solution and at the same time began to integrate software from different vendors for specific needs such as Web Analytics or Email Marketing.
The more complex the scenario becomes, the harder it is to imagine that a single solution can be reactive and capable of adapting to all needs. Today, companies are also moving away from the hybrid solution and turning more towards an ecosystem approach, as well identified by Scott Brinker.
At the same time, problems of redundancy and duplication arise.
This is because each element of the stack often has similar or overlapping functions with others and its own database to collect data, which will then necessarily have to be synchronized with the others to ensure updated and high-quality data. Now, if this aspect is less evident when we talk about internal systems like ERP or DAMS, it becomes absolutely insurmountable for data with marketing purposes.
Thinking of being able to use data that is not updated with respect to user consents to create personalized marketing campaigns and experiences becomes risky and harmful in a privacy-centric world.
We can draw the same parallel regarding segmentation algorithms and user scoring. Complex synchronization mechanisms between CRM, ERP, and Marketing Automation platforms can lead to having to wait up to 20 days to have updated data on the customer’s status, a time that goes beyond the effective sales cycle of the service and makes all enrichment and segmentation efforts vain.
The “modern data stack” represents a significant evolution in data management and analysis compared to traditional approaches. Historically, organizations relied on heterogeneous systems and distinct software, each with its own database, to manage different aspects of data. This fragmented approach led to significant challenges in terms of data integration, management, and analysis, as information was scattered in various silos, making aggregation operations and obtaining unified insights complex.
In contrast, the modern data stack adopts a centralized approach, leveraging the potential of the cloud to overcome the limitations of previous systems.
The fundamental elements of this new approach are:
- Centralized approach to data in the Cloud
- Ingestion tools to collect information from all sources
- Processing and transformation tools to make data ready for processing
- Machine Learning and AI tools to extract maximum value from data in the form of clustering and insights
- Reverse ETL tools to extract processed data and send it effectively to all marketing tools and platforms.
At the heart of this new paradigm is the adoption of a single database, flexible and agnostic with respect to data sources, which serves as a unified platform for data collection, storage, and analysis. This database is designed to be highly scalable, allowing the management of growing data volumes and supporting a wide range of data types, from structured to unstructured data.
The key feature of the modern data stack is its ability then to easily integrate the cloud data warehouse with a variety of tools and analysis platforms, offering organizations the flexibility to use the tools best suited to their specific needs. Furthermore, thanks to data centralization, organizations can implement more effective data governance practices, improve security, and facilitate real-time data access for decision support.
The modern data stack with a single source of truth at its center with quality, centralized, and reconciled data allows us to obtain precisely that resilience and reactivity to all changes in the digital landscape, being able to quickly add the necessary functionality to exploit a new tool, algorithm, or player.
Thanks to the new approach, we can truly begin to build a high-performing and rapid ecosystem, in line with the needs of modern marketing.
Companies that leverage this approach begin to appreciate the simplicity of maintenance and low information redundancy and want to transfer this approach to the entire marketing suite.
Because in fact today we can have composable solutions in which we no longer need to choose between an integrated suite or best of breed. The new paradigm consists in selecting only the software features that are important to us and absent in the ecosystem, in order to create the leanest and most agile structure possible.
We will therefore choose the best anti-spam sending function of the email marketing software, while we will leave to our composable CDP the function of segmenting the database to give us the best user clusters.
We will exploit the functionality of data collection from the Google Analytics 4 pixel, but we could consider creating our analysis interface with dashboard systems based on the cloud data warehouse mentioned above.
The composable approach is growing and becoming a market leader precisely because of this historical moment, made of continuous changes, restrictions, and walled gardens.
Being able to take only the necessary parts of a software and integrate them perfectly into strategies represents the best response to challenges for companies and guarantees an infinitely shapeable and adaptable system.
As well described by Vijay Tella in the book “The New Automation Mindset”, to truly activate the potential of automation, generative AI, and hyper-personalization, companies today need democratization, orchestration, and plasticity in tools.
The composable approach of the martech stack is the only one capable of guaranteeing the three principles and allowing brands to successfully face today’s digital challenges.
