Simple retargeting is no longer enough to reach users at the right moment. Deep learning can help place customized and personalized messages for users, increase customer satisfaction, and increase the efficiency of campaigns. Guest author Daniel Volož from RTB House explains how deep learning with hyper-personalization helps advertisers improve their targeting and retargeting.
Users often find unimportant advertising annoying. Even an ideal product, presented at the wrong stage of the purchase decision, can negatively impact a potential customer's decision. With the help of deep learning, advertisers can calculate the best time to show an ad to a person.
Deep learning algorithms are able to sift through complex and anonymized data from educated interest groups or cohorts for behavioral matches. Behavioral profiles are created in real time based on how users react to offers in the online shop.
© IMAGO / Alexander Limbach
Personalization is becoming increasingly important in marketing. Intelligent algorithms analyze the behavior of customers and can thus display offers that are precisely tailored to them.
With every purchase made, the algorithms also determine a "footprint" that describes the likelihood of customers who have viewed or bought certain products also buying other products. Deep learning learns from the behavior of all customers and can suggest suitable goods based on this, since customers with similar user behavior were also interested in it.
Deep learning algorithms learn more efficiently
This pays off especially in retargeting. Unlike conventional retargeting methods, the deep learning algorithm can calculate where buyer groups are in the sales funnel and when is the individually most suitable time to show a person an ad. This is not possible on this scale without deep learning.
The special advantage of deep learning algorithms: the more often you use them, the better they get. Unlike machine learning, they can recognize patterns without human intervention, allowing for more efficient self-learning. The algorithm becomes more and more precise based on the data fed to it.
How deep learning helps to place ads in the best possible way
A retargeting process with deep learning can be easily illustrated by the purchase of add-ons (headphones, cases, etc.) when purchasing a smartphone. Deep learning calculates whether an add-on is suggested to the individual customer before, during or after the smartphone purchase or at all.
It is therefore essential to spread effective marketing campaigns that identify users in all phases of the purchase decision and select the right message, the right product and the right medium.
Deep learning provides valuable help and can implement the communication strategy based on analyzes of millions of websites and achieve a click rate (CTR) that is up to 16% higher with the same budget.
Example TriStyle
TriStyle Customerce, the digital unit of the TriStyle Group, which includes Peter Hahn and Madeleine, is already using deep learning in its targeting and retargeting strategy to reach customers when they are most likely to make a purchase.
In contrast to Peter Hahn's earlier retargeting strategy, re-addressing is now more targeted. Through deep learning, the shipper was able to increase order volume by 16%, click-through rate (CTR) by 50% and new customer traffic by 8% within two months.
Conclusion
When sensory overload in ads reduces their effectiveness, advertisers and their partners can turn to leading technology to differentiate their marketing and make their campaigns more efficient.
Deep learning is becoming increasingly popular and is transforming many different areas of business, from automotive to entertainment to marketing and commerce. Investing in new solutions ensures brands survive in future competition.
Deep learning technologies enable advanced algorithms and data models that allow merchants to analyze and identify user needs and use them for their targeting and retargeting.
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