E-commerce of the future. 5 artificial intelligence opportunities to increase sales

According to the results of the Adlucent study, 7 out of 10 respondents prefer personalized advertising. At the same time, 46% of respondents are ready to provide their personal data in exchange for receiving personalized content and advertising. Apply this data with the maximum business benefits help modern technology of artificial intelligence.

On the wave of trends programmatic

The popularity of automated online advertising purchases (programmatic) is growing from year to year. According to forecasts of Zenith Programmatic Marketing Forecasts, the share of purchases of such banners until 2019 will exceed 67% of global marketing expenses, in monetary terms will amount to $ 84.9 billion.

The use of artificial intelligence (AI) as a standard for processing large data arrays is very useful for e-commerce. In this case, machine learning algorithms will be gradually superseded by more intelligent algorithms of in-depth training, in data processing and decision-making modeling, operating on the principle of the human brain. This allows you to better understand the intentions of consumers and provide an excellent consumer experience.

Tools to transform potential interests into real sales

The value of data for business is determined by the methods of their acquisition and analysis. This is especially important for advertising campaigns. Indeed, the degree of personalization of advertising depends on how relevant and comprehensive the data is. Today it is a prerequisite for sales promotion.

Artificial intelligence technologies are capable of providing super-precise personalization. In-depth learning algorithms, as the most promising area of ​​artificial intelligence research, collect and interpret not only banner transition data for individual advertisements, but also include information on how suggestions were viewed, which categories were displayed, transaction finalization methods, and the desired tactics of searching for goods. At the same time, the high accuracy of advertising targeting using such technologies ensures that they learn like the human brain, only at a much higher rate. By capturing and analyzing the slightest changes in the behavior of a potential buyer, they also bring in the experience of millions of others. This ensures the achievement of ultra-precise personalization and even more - forecasting what the consumer will want in the future.

Formation of promotional offers in real time

Consumer behavior patterns are constantly changing and may even differ depending on the device on which advertising is viewed. Therefore, to achieve success in e-commerce, it is important to update advertising recommendations every time advertising is shown to the consumer.

Thanks to powerful algorithms and an ongoing self-learning process, retargeting mechanisms based on in-depth training are able to generate personalized recommendations in real time. And the whole process will take a split second.

What is the use of it? According to RTB House, after the introduction of deep learning mechanisms to form recommendations, consumers click on banners by 41% more often than usual.

Improved recommendation engine

In-depth learning algorithms, in contrast to machine learning algorithms, analyze not only what is on the surface: what the consumer has looked at, what similar products are, and so on. By analyzing the slightest changes in the behavior of a particular consumer according to many criteria, the process of selecting relevant offers is more flexible and accurate. This approach provides highly accurate personalization of recommendations that are more relevant to the needs of a particular consumer.

Better prediction

The use of depth learning algorithms opens a new page in predicting consumer behavior. The computational power and the absence of inaccuracies or bias inherent in the results of processing large data arrays by man, brings the quality and value of such a forecast to a new level. This means that it is possible not only to accurately calculate consumer intent, but also to influence the decision-making process for making a purchase.

The advantages of such an in-depth analysis speak for themselves: for example, analysis of each step of the consumer who is going to buy a camera, including the purchase date, behavior patterns, product search history, etc., taking into account product recommendations for related products (memory card, lenses, tripod ), will entail demonstration to such a consumer of a banner with some, at first glance, abstract product. For example, self-drone, which makes great shots and exciting video. Thus, a fairly accurate advance formation of personal recommendations for certain products that the consumer will look for in the near future, or will be interested in taking into account the range of their interests, takes place.

In the field of e-commerce, an understanding has already formed that big data can and should be used for business development. And such data is actively used for advertising. However, at the same time, the negative perception of advertising is growing due to its excessive amount and irrelevance. Normal targeting no longer works. Only the latest solutions will allow brands to remain competitive. One of these is the introduction of modern technologies of artificial intelligence in digital platforms. Thanks to their advanced algorithms, they provide ultra-precise personalization of advertising messages and contribute to the growth of the effectiveness of advertising campaigns.