RTB House has implemented new algorithms for precise estimation of cost-through clicks (CTRs), which it claims allows better prediction for potential clicks on ads, thereby yielding higher return on investment (ROI) for customers.
This development makes the retargeting tech company 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 percent within the same budget limitation.
“We’ve been working on these innovations for a year and a half, gradually extending upgrades to our solution. 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,” said Shady Francis, Regional Country Manager MEA, RTB House.
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 percent more efficiently, compared to campaigns that did not utilize the same methods.
“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,” said Bartlomiej Romanski, Chief Technology Officer, RTB House.