RTB House, a global company providing state-of-the-art retargeting technology for top advertisers worldwide, has implemented new algorithms for ultra-precise estimation of cost-through clicks (CTRs), which allows better prediction for potential clicks on ads, thereby yielding higher return on investment (ROI) for customers.
This development makes RTB House one of the first retargeters to extensively use deep learning – the most promising subfield of Artificial Intelligence (AI) research. Additionally, it also helps to boost the total number of clicks by 16.5% within the same budget limitation. Thanks to this innovative approach, conversion rate and conversion value algorithms are able to increase overall performance from retargeting activities up to 29%.
By using deep learning technology and processing models inspired by the biological neurons in our brains, RTB House makes it possible to get more reliable, richer, machine-interpretable user profiling of customer’s buying potential, without any human expertise.
‘‘We’ve been working on these innovations for a year and a half, gradually extending upgrades to our solution, said Shady Francis, Regional Country Manager MEA at RTB House. ‘‘In the travel industry there are so many metrics that need to be taken into consideration and even the purchasing patterns are complicated and difficult to predict users behavior, hence algorithms powered by deep learning are needed to better react to user’s needs. It’s a vast improvement over other methods typically used in retargeting’’, he added.
On the other hand, RTB House has also presented a new upgrade to its recommendation mechanism using a combination of deep learning and computer vision. The new method enables ultra-precise predictions of possible user’s buying needs, leading to product recommendations up to 41% more efficiently, compared to campaigns that did not utilize the same methods. Growth is noted especially in sectors such as: fashion and multi-category e-shops, where the possibilities to use cross-categories recommendations are almost endless.
This approach also employs deep learning, the most promising subfield of AI research, which imitates the way the human brain works at solving problems. It makes decisions about what a user is most likely to click, browse, or buy. Without deep learning, it wouldn't be possible to exploit dynamic personalization from multiple dimensions, not only based on standard recommendation systems, but on where products on banners are also chosen according to the user’s impressions history.
It uses technology referred also as computer vision, which allows for automated extraction, analysis and understanding of information from a single image or a sequence of images. It looks for similarities between products checked by potential buyers.
Bartlomiej Romanski, Chief Technology Officer at RTB House, notes that over the past few years the industry has worked on tools that in some ways exceed the human intuition or eye’s capabilities.
‘‘Our goal is to make retargeting ads delighting customers on one hand and performing extremely effectively on the other. The innovative recommendation mechanism we’ve implemented brings personalization to a new level.
‘‘Thanks to deep learning, our mechanism evolved to adept select products that should be shown on banners and have the biggest potential to be bought. In combination, with computer vision we have the ability to analyze thousands of images per second, define patterns with a great precision and adjust recommendations to every small change in the customer’s behavior. At the end of the day, higher performance brings out clients bigger return on ad spend and helps to multiply ROI,’’ Romanski summarizes.
RTB House is one of few companies in the world that managed to develop and implement its own technology for purchasing advertisements in the RTB model, or real-time bidding—a solution in which buyers participate in real-time advertising space auctions. The company operates worldwide and runs more than 1,000 unique campaigns for global brands in more than 40 markets across Europe, Latin America, Asia and Pacific, Middle East and Africa.
The company’s findings in the field of artificial intelligence were lately presented during the 2017 International Joint Conference on Neural Networks in Anchorage, the 33rd International Conference on Machine Learning (ICML 2016) in New York City and the 31st AAAI Conference on Artificial Intelligence (AAAI 2017) in San Francisco.