According to the Central Association of the German Advertising Industry, more than €25 billion in net advertising revenue was generated within Germany in 2021. Online measures accounted for more than 40% of this, with the lion’s share of around €5 billion each for search and display ads – and the trend is rising. To ensure that the digital advertising measures and the platforms they choose achieve the highest possible ROAS, many companies rely on extensive collection and analysis of large amounts of customer data using marketing attribution.
What are the limitations of marketing attribution?
Marketing attribution is a widespread analysis method within online marketing that is based on intensive tracking of user behavior. However, growing privacy concerns among users and authorities, as well as the spread of ecosystems that are inaccessible for external tracking (such as Facebook or Apple iOS), have recently increasingly highlighted the limitations of this method: the less data that’s available for your marketing mix, the lower its informative value for ROAS. In addition, the data obtained via web analytics can only be examined for correlations and dependencies at great expense.
Decision Intelligence (DI) enables the visionary co-working of man and machine
Depending on the industry and even the company, an increasing number of social media channels, search engines, corporate websites and any proprietary sales portals should be considered for these investigations. Since much of the data generated there is interrelated, it’s advisable to study it as comprehensively as possible and also factor in external influences in order to arrive at usable findings. One solution is the use of Decision Intelligence (DI), which utilizes Artificial Intelligence (AI) and machine learning to continuously monitor relevant data streams, perform root cause analyses and offer options for action.
How does DI work?
The Decision Intelligence system from paretos uses a self-learning set of forecasting and recommendation algorithms that can identify correlations and potential conflicts between individual marketing measures in the marketing mix much faster than previous methods. Based on this, the system generates several optimal solutions, which can then be evaluated by the AI and converted into corresponding budget allocations by human decision-makers. The results can be sent directly from the paretos Cockpit back to the channels without having to transfer any additional data. As soon as the first outcomes of these adjusted solutions are available, they are immediately fed back into the calculation models, thus allowing continuous improvement of the predictions.
This is how comprehensively companies benefit from DI
For marketing optimization, DI doesn’t require attribution or the use of tracking cookies. It works with more general data such as product costs and margins, current budget distribution, outcomes of previous campaigns and the buying behavior of specific target groups, as well as with external factors such as seasonal sales or weather influences. Depending on requirements – from a single image ad to a comprehensive marketing strategy – the amount of data used is scalable and delivers meaningful results even from a small input, or it can also process very large data sets if required.
This makes it easier for marketing managers to decide which marketing mix to focus on and immediately respond to changes. The resulting flexibility and accuracy brings further advantages:
- more targeted budget allocation;
- more efficient campaign planning;
- reduced overhead costs;
- higher conversion rates.
In addition, DI is straightforward to implement and is suitable for a wide range of uses.
Whether it’s Google Ads or TikTok videos, classic banner advertising or lead generation with contests on Facebook, omnichannel marketing optimized with DI gives companies the opportunity to play all channels and formats efficiently. Because of this, your budget is used where it will have the greatest effect – and, ultimately, this means increased ROAS and more sales.