Leverage our ecosystem user profiles data and our machine learning-based recommendation engine to generate precise and complete look-alike seeds for social media targeting.

We leverage online browsing and shopping profiles, offline consumption habits, experimental data-driven behavioural traits (e.g. brand loyalty, social influence, individual price sensitivity) to train our neural-net based recommendation engine to generate user seeds for look-alike marketing in social media

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precision
Precision

Multifaceted data sources allows building seeds with exceedingly superior performance compared to simple "best-shopper" seeds not to mention the crude demographics-based targeting

completeness
Completeness

Our approach allows uncovering many more hidden pockets of value that are typically obscured by the subjective definition of "target consumer"

continous-improvement
Continuous Improvement

Targeting model is designed to continuously improve precision, i.e. your advertising ROI will be continuously improving taking in the feedback of the advertising campaign. Many of our clients leverage this feature to only launch mass scale campaigns when optimal targeting precision has been reached.

Platforms available:
logo-wechat
WeChat
one billion users
logo-tiktok
Douyin (Tik Tok)
400 million users
logo-weibo
Weibo
313 million users