Deep Learning and Hyper-Personalization

Traditional neural networks only contain 2–3 hidden layers, while deep networks can have as many as 150.Deep learning models are trained by using large sets of labeled data and neural network architectures that learn features directly from the data without the need for manual feature extraction.Applying Deep Learning techniques for recommendations means that the recommendations stay relevant and highly personalized over time..Basically, it makes us capable of predicting the future instead of looking into the past.With the advancements of back-propagation and all the new inference techniques, it’s now way easier to train a deep neural network than it has been in the past.Deep Learning for personalizationDeep learning methods are becoming a powerful tool to improve recommender systems tasks such as music, news, e-commerce, and mobile apps recommendation..One of the most promising applications of deep learning in marketing is to enable “hyper-personalization.”Personalization is already a big trend in marketing, and it’s only the beginning ­– transaction rates of personalized emails are six times higher than generic emails.Collaborative filtering is one of the most famous techniques for developing a recommender system.Collaborative filtering: a method of making automatic predictions (filtering) about the interests of a user by collecting preferences or taste information from many users (collaborating).Traditionally, collaborative filtering employs the use of concepts like SVD and like Eigen Vectors..There is some really interesting research going on around Deep Learning for recommender systems..The general idea revolves around embedding vectors, where feature embedding is performed first and it is then fed into a DNN.Deep understanding of consumer shopping behavior hasn’t been widely available for e-commerce players yet, but it’s critical for marketers..For instance, it’s quite easy to find patterns in decision making for typical, predictable sales peaks like Black Friday..It gets more complicated when it comes to identifying individual events, with a very specific context (like a shortly upcoming friend’s birthday or any sudden occasion).For instance, imagine that you’ve forgotten about your uncle’s birthday..With only four days left, there isn’t much time to search for a product, but still enough to look for something special..In these cases, ultra-accurate personalization can make the difference, and deep learning models can begin to know that you’re enthusiastically looking for something.The basic goal of a recommender system is to predict future user-item interactions based on previous interactions and features.Based on an individual customer profile, we can display to a specific visitor different sets of offers or messages to increase the chances that a customer will convert..This personalization is based on a set of experiences defined by the marketer, and the delivery of these experiences is automated thanks to deep learning.Another example, let’s assume you’re a travel company, and you want to deliver a personalized offer for a beach vacation to Franck, one of your customers..We could collect thousands of data points or attributes about Franck— where he lives, where he is now, which browser he uses, his favorite color, what the weather is like where he is, etc.These attributes also can be related to site behavior (new or returning visitor), temporal variations (time and day of week), channels (mobile and desktop), referral source ( social media ad), and even your own internal CRM data (whether Franck is a loyalty club member).With these information, we can already build a very solid customer experience..However, we might achieve more with Deep Learning.Indeed, Deep learning will not just rely on the interaction history of the customer but will consider their intent.. More details

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