Every day, millions of people make Albania Email Lists purchasing decisions based on the search for products, nearby restaurants, and many other options. However, according to the Nielsen report “Global Trust in Advertising”, although consumers trust online reviews or price comparisons, most of the time it is personal recommendations that are the most effective. The most effective advertising comes directly from the people we know and trust, and more than 83% of respondents fully or partially trust suggestions from friends and family.
So when we make the final decision to buy, it is reasonable to assume that we will seek advice from a partner, family, or closest friends. After all, they are the ones who know our tastes, preferences, etc.
The digital age has made shopping increasingly accessible, but making decisions more complex. Choosing from hundreds, or even millions of options, makes it more difficult to make a decision. Online recommendation systems change the way we navigate and choose products: they limit our decision-making process by getting closer to what we are looking for, suggesting complementary or even alternative products.
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This “insight” about buying personality typically comes from what consumers have previously purchased or viewed, what shoppers with similar profiles have viewed or purchased, and the date and time of viewing. Recommendation technologies study what consumers are looking for and suggest products to them. They collect and analyze millions of data about your preferences to provide ultra-precise suggestions.
It sounds simple, but these technologies require massive volumes of data to make accurate predictions. And, of course, the more information the better. This is where deep learning comes in : an innovative branch of artificial intelligence that solves problems by mimicking the work of the human brain in processing data and creating decision-making patterns. Most of us already have experience with suggestions and recommendations based on behavioral and browsing data. We have purchased products on Amazon recommended in the section “Customers who bought this product also bought” or have added new people to Linkedin after seeing “People you might know” We even watch movies on Netflix thanks to AI recommendations.
And now the engines are getting smarter. They use Deep Learning tools that personalize the experience of a user trying to find out their habits even after having made just one visit. Along with real-time analytics, self-learning algorithms can improve suggestions to the point of prediction. Services like Spotify can predict the next song suggestion, while YouTube queues recommended videos based on the one you’re watching.
The Deep Learning ultra-precise used in all digital industries, including advertising. According to RTB House, self-learning algorithms help achieve highly accurate recommendations that make advertising activities up to 50% more efficient. But how does it work in practice?
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Take as an example buying a new dress. When a buyer clicks on any section of the website, the recommendation mechanism captures every piece of information. Check the dress color, details like prices, sizes, and dozens of points from other stocks. Then connect as many interaction patterns as possible. By measuring and analyzing them (in real time), the system can understand history, taste, interests, or even mood, and then make accurate predictions of interesting products. Matches between shoes and jewelry, evening dresses, summer clothes, could be recommendations based on effective predictions. All of this happens without any human intervention from the advertiser. In the field of purchase prediction, the algorithms ofDeep Learning have already gained so much knowledge, that it has made manual interventions unnecessary.
Typical recommendation models cannot do this. Most recommendation software simply collects information and then selects products to display with rules predefined by a human, such as: “show jewelry only to those who visited women’s clothing, as women are more likely to buy.” Now this can be replaced by “Our system knows having visited women’s clothing is a predictor for jewelry purchases, but it has also learned to detect men who intend to purchase jewelry for themselves or as a gift.”
Deep Learning algorithms simulate our way of thinking, but we learn by practicing results without any human input. A machine will analyze countless data sets relentlessly, without getting tired or bored, and will produce logical, risk-proof decisions without stress, doubt, or emotions. You will obey the general rules of the advertiser, but more importantly, you are able to learn and write new rules with proactivity and performance unattainable by human labor. This is the essence of self-learning a