Differences between rules-based model and machine learning.
Numerous industries are researching and implementing artificial intelligence (AI) in order to automate business capabilities such as innovation, customer experience, and data analysis. This article focuses on automating data analysis in order to optimally advertise your eCommerce businesses. Two types of automation are discussed:
Our belief is that machine learning is best suited to eCommerce's rapid pace and our clients' marketing needs.
As its name suggests, the rules-based models are programmed to respond to a set of predefined if-then-else rules. For each task, the number of rules depends on the number of actions - ten actions require to manually code at least ten rules. This system works best for small tasks, such as email routing or expense report approval, which have fixed outcomes. Rules-based models are simple automation and require less effort and data to run. However, the rules also constraint the AI capabilities as it becomes dreadful to add new rules or parameters without introducing contradictions. Additionally, the predefined rules based on a small amount of data might lead to human bias and mistaking random noises and outliers for industry trends.
In contrast with deterministic rules-based systems, machine learning systems rely on probabilistic methods. It still involves complex coding and mathematical rules, but primary learns from a sizable amount of training data. After digesting the data, the machine learning algorithms set their own rules based on statistically significant patterns they identified. One of the main advantages of machine learning models is that they continually test the data and adapt to change. Therefore, they are easily scalable and accommodate complex projects with a fast pace of change.
At m19, we use machine learning AI to automate your search advertising goals. Structuring marketing campaigns is an extensive task and difficult to boil down to predefined rules. Therefore unsuited for rules-based models. Consequently, we purely focus on machine learning that constantly retests your data to optimize your marketing goals in real-time. While you focus on business operations, our machine learning engine updates your bids on both the most performing search terms or product pages and the less performing ones. In comparison to rules-based, machine learning can be quickly scaled to any brand or industry, regardless of size. Finally, our AI solution can explore massive lists of search terms and product pages to identify and mainly invest in those that drive conversion to your products.
Overall, rules-based and machine learning models have different advantages. Although machine learning is harder to develop, we strongly recommend its adaptive and learning capabilities for eCommerce search advertising.
We will constantly share insightful articles about Amazon ads with you.
Endlich hat es Amazon seinen Partnern ermöglicht, stündliche Statistiken für Anzeigen auf den globalen Marktplätzen abzurufen — also haben wir getan, was wir tun mussten: Wir haben die Zahlen analysiert und nach einer Möglichkeit gesucht, unser Tool zu verbessern.
Finally, Amazon allowed its partners to retrieve hourly stats for Ads on the global marketplaces, so we did what we had to: we crunched the numbers looking for a way to improve our tool.