RTB House
Leaning toward deep learning
### Leaning toward deep learning

In 2017, RTB House's retargeting solution was fully based on deep\-learning algorithms in all of its products. Into the product [recommendation engine](/wiki/Recommender_system "Recommender system"), the company implemented deep learning components, with various scenarios for implementing business strategies: increasing sales, increasing traffic in [e\-shop](/wiki/Online_shopping "Online shopping") applications, and supporting [cross\-sellings](/wiki/Cross-selling "Cross-selling").

At the time, the company released a [paper](/wiki/Academic_publishing "Academic publishing") explaining how a [long short\-term memory](/wiki/Long_short-term_memory "Long short-term memory") (LSTM) [neural network](/wiki/Artificial_neural_network "Artificial neural network") could be used to describe a user of a particular website without human intervention, building an understanding of a user without requiring them to click on an advert.

Throughout 2018, RTB House expanded in Europe, [Australia](/wiki/Australia "Australia"), and Asia, followed by subsequent offices in the [US](/wiki/United_States "United States"). The company's revenue was €99\.89 million. It had over 300 employees.

In 2019, the company changed its business model, by increasing its role as a development supporter and technology supplier for subsidiaries. A [private equity fund](/wiki/Private_equity_fund "Private equity fund") [Cinven](/wiki/Cinven "Cinven") bought a package of shares of RTB House from its founders, to further internationalize the company, given Cinven’s presence in the United States, and to continue investing in the RTB House’s technology. The number of employees grew to 575\.


Is it possible to use machine learning for other business strategies beyond increasing sales and traffic?