In principle, artificial intelligence (AI) has enabled targeted digital marketing and with every further development, the results are becoming more accurate. While machine learning (ML) was until now the most modern method for predicting user decisions, its further development, deep learning (DL), has refined the approaches and can achieve more precise results.
Machine learning refers to algorithms that analyze data, learn from that data, and then apply what they learned to make informed decisions. However, machine learning still requires the involvement of a person to provide the definitions necessary for categorization, interpreting the data and checking the accuracy of the predictions. This means that machine learning can only be as good as the input and rules human users provide.
Deep learning is the next evolutionary step that eliminates the need for humans to make the rules. In deep learning, this is done instead by neural networks, which check and interpret data and predictions or adapt algorithms. Deep learning can also detect and respond to unforeseen situations that users didn't anticipate, in other words seems to “think”, “learn” and “make decisions”.
With DL, even subconscious and not knowingly controlled user intentions can be recognized and used. DL does not work according to specifications but learns from all the data collected in order to achieve the best possible results. There is no subjective influence, such as in the establishment of business rules, which inevitably involve pre-categorization of human behavior. Well, the benefits of deep learning for campaign targeting can be summarized as follows:
- DL learns independently and writes new rules without reprogramming
- No need for rule-based algorithms to be written by programmers
- DL allows finding more connections between datasets of different sizes (also some hidden ones)
- Structured, organized, categorized data is not required
- DL sees opportunities in a “chaotic”, less predictable environment
As usual, there is no “one size fits all” tool here; however, deep learning is particularly superior when large amounts of data are available.
For example, one application where deep learning is clearly beneficial is product recommendations. With every purchase made, DL algorithms determine a "footprint" with the probability that customers who have viewed or bought certain products will also buy other specific products. DL learns from the behavior of all customers, based on this, and can suggest certain goods, since customers with similar user behavior were also interested in them.
By Daniela La Marca