RTB House launches its updated recommendation engine technology
The computer version backed engine aims to improve product recommendations for e-commerce portals and user by up to 41%
RTB House, a company providing retargeting technology for top advertisers worldwide, has launched its updated recommendation engine technology for e-commerce market in India.
The new recommendation engine, which is backed by deep learning and computers' vision algorithms, provides a more accurate advice in the decision-making process to brands, advertisers, and marketers. By being able to precisely predict an online user’s buying needs and shopping habits, it provides product recommendations which are up to 41% more efficient.
The new recommendation engine needs only milliseconds to decide what to present to the user shopping online. The decision of “what to present” is made on the basis of what the particular user was looking for, taking into account click data, purchase history, information about the product, categories of interest, and shopping behaviour and search tactics. The final product displayed is based on a full range of information, which takes into account not only the online behaviour patterns expressed by the current user but also behaviour expressed by other users with a similar buying profile and previously presented product recommendations.
Bartlomiej Romanski, Chief Technology Officer 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 the one hand and performing extremely effectively on the other. The cutting-edge recommendation mechanism we’ve implemented brings personalisation to a new level. We have noticed that users clicked on ads up to 41% more than usual with the new RTB House’s deep learning recommendation mechanism over current approach. 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. At the end of the day, higher performance brings brands and marketers bigger return on ad spend and helps to multiply ROI,” Romanski summarises.
The new recommendation engine, which is backed by deep learning and computers' vision algorithms, provides a more accurate advice in the decision-making process to brands, advertisers, and marketers. By being able to precisely predict an online user’s buying needs and shopping habits, it provides product recommendations which are up to 41% more efficient.
The new recommendation engine needs only milliseconds to decide what to present to the user shopping online. The decision of “what to present” is made on the basis of what the particular user was looking for, taking into account click data, purchase history, information about the product, categories of interest, and shopping behaviour and search tactics. The final product displayed is based on a full range of information, which takes into account not only the online behaviour patterns expressed by the current user but also behaviour expressed by other users with a similar buying profile and previously presented product recommendations.
Bartlomiej Romanski, Chief Technology Officer 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 the one hand and performing extremely effectively on the other. The cutting-edge recommendation mechanism we’ve implemented brings personalisation to a new level. We have noticed that users clicked on ads up to 41% more than usual with the new RTB House’s deep learning recommendation mechanism over current approach. 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. At the end of the day, higher performance brings brands and marketers bigger return on ad spend and helps to multiply ROI,” Romanski summarises.
