AdTech and MarTech will merge in the future. However, solutions that combine the data and advantages of both, are still more of an exception than a rule. Despite the fact that customers should have a consistent and holistic user journey via paid and owned media.
However, this is not possible as long as both areas are considered separately. But how can both divisions successfully merge? The answer is provided in our blog post.
UNDERSTAND THE DIFFERENCE BETWEEN ADTECH AND MARTECH
While AdTech and MarTech are both essential tools for every advertiser, each serves a specific purpose. MarTech is primarily used for known or existing customers, while AdTech is aimed at anonymous target groups.
Here is a summary of the most important differences:
AdTech solutions use performance-based models, while MarTech solutions rely on annuity-based pricing models.
AdTech platforms operate primarily in one-to-many environments, MarTech platforms in one-to-one environments.
Agencies and partners are an integral part of the AdTech world, while at MarTech, brands work directly with suppliers.
The price models of both disciplines differ, which is why they are valued differently by the market.
These differences make the merger more difficult at first glance. However, the multitude of advantages speaks for a combination of both disciplines.
AdTech and MarTech converge when valuable interaction and audience data from core systems such as customer relationship management and marketing automation software flows into programs such as email, search engine marketing and display advertising.
ADTECH AND MARTECH COMPLEMENT EACH OTHER
The challenge for marketers is to combine the different data to generate actionable insights that can be used to inform and improve marketing programs.
Through a merger, they benefit, among other things, from precise personalization with which they can optimize the customer journey. MarTech tactics can be improved with data science approaches from the AdTech area. Traditional MarTech campaigns via web, email or product experience are then enriched by modeling results from AdTech Data Science teams and no longer dictated by targeting based on statistical rules.
On the other hand, data and systems that drive MarTech's efficiency can be used to optimize AdTech campaigns. This allows AdTech campaigns to be distributed to high-quality target groups and bidding to be driven by lifetime value metrics or optimization algorithms that are delivered by internal MarTech tools and teams.
ADTECH DATA SCIENCE FOR EFFICIENT MARTECH MODELING
In the AdTech universe, instead of manual methods and individual targeting, machines have long been used to analyze the huge amounts of data. In MarTech, on the other hand, targeting tailored to precisely defined segments before the start of the campaign is extremely important. Nevertheless, the integration of machine learning into the MarTech area has the potential to improve everyday tasks and streamline processes.
The main challenge here is to find resources that are abundant in the AdTech world - namely data and data scientists. In the MarTech area, the volume and accuracy of data is often not good enough for efficient modeling. This can be bridged by adding customer information collected from AdTech to basic interactivity data such as opens or clicks.
ENRICH ADTECH WITH MARTECH FIRST PARTY DATA
In contrast to MarTech, AdTech should see a decline in data science investments. The large amount of money spent on manual analysis are no longer justified from an ROI perspective - especially in view of the machine-learning investments of large AdTech platforms. However, it is not the right way to fully rely on these platforms.
MarTech generally works with a much smaller number of customers, contacts or leads. In general, it includes first party experiences such as email or mobile campaigns. The right strategy for brands should therefore be to set appropriate conversion targets and to complement modeling with very accurate feedback in the form of their first party data. This approach has been continuously refined in the MarTech area in recent years.
CONCLUSION: TRANSFERRING PROVEN METHODS
Many brands stand in their own way by dividing budgets and teams for paid and owned media. The merger of AdTech and MarTech could pave the way for a holistic user journey from which advertisers could gain completely new insights and results.
It is not even necessary to develop new concepts and methods - the existing techniques from AdTech and MarTech can be easily transferred. With this approach, companies and brands can break open silos in the long term and create a world where paid and owned tactics work in harmony to provide a brand dialogue that is intelligent and unique across every digital touchpoint.