Marketing automation is one of the trend topics in digital marketing. The field of automation is booming, both for smart bidding for search or programmatic Display advertising. In Germany alone, around 70% of all digital Display spendings will fall back on programmatic in 2019 and spends will continue to rise by 15% compared to the previous year (emarketer Report, Programmatic Ad Spending in Germany 2019).
This is why it’s all the more important that the algorithms that control the display of advertising and the bidding on advertising spaces in real time access relevant data that makes advertising measures as efficient as possible. Automated optimization is only achievable if the algorithms can draw from the results of an intelligent, data-driven attribution model. Our blog post reveals why.
AUTOMATION AS A COMPONENT OF EFFICIENT MARKETING
To simplify optimization processes and make campaigns even more successful, tools and marketing platforms are increasingly relying on machine learning. Smart bidding for SEA or programmatic advertising for Display campaigns also belong to this category and make use of intelligent algorithms that offer real-time optimization on advertising spaces and keywords.
But there is a catch with these systems.
The systems usually only know their own channel and move within this limited world. Although they know which keyword or which search query the consumer used and whether a conversion resulted from it, they do not know if and how the user was previously influenced by other channels and measures. The systems don’t have any information about other marketing channels that had an influence on the user’s behavior.
However, the knowledge of the complete customer journey is the basis for optimization. This is where the connection to attribution comes into play. Attribution systems calculate the efficiency of all digital marketing activities across channels and devices.
DATA-DRIVEN ATTRIBUTION IN A NUTSHELL
The purpose of marketing attribution is revealing the influence of each individual touchpoint between consumer and brand on the conversion. With help of machine learning and intelligent algorithms, the model dynamically determines the value contribution of each touchpoint in the customer journey across channels and devices. The focus is on customer-centricity and the journeys are analyzed at user level across all channels, devices and browsers.
AUTOMATED OPTIMIZATION OF PROGRAMMATIC
In order to actually realize automated optimization of programmatic advertising, the results of the data-driven attribution have to be imported into the bid management system. This way, data from marketing attribution flows directly into the system that manages and optimizes campaigns. Relevant insights are directly fed into the programmatic algorithms.
The system then knows the exact value contribution of each individual touchpoint down to creative and keyword level. With this information, the algorithms become much more precise and can adapt to the actual behavior of the customers. With the customer journey data, the bidding algorithms can now be continuously improved and marketing campaigns are considerably optimized.
Marketers receive optimized ads without any manual adjustments. They have full transparency, since all data sets needed for campaign optimization are centralized within the system that manages campaigns.
Findings from the data-driven attribution are made actionable and the programmatic campaigns are evaluated and optimized fully automatically, while advertisers save time and money in data collection and processing.
Would you like to learn more about automated optimization through attribution? Download our case study and learn how our customer Eurowings was able to reduce the costs for SEA measures by 13% with our new feature "Attribution Push".