Improving media strategy by identifying customer profiles
Considering the substantial volume of daily users visiting the ASUS e-commerce website, determining an effective online media strategy capable of distinguishing users at different stages of the purchase funnel was as challenging as imperative.
Rating individual users by applying machine learning to first-party data
Our data-driven Machine Learning solution allows for rating individual users through a variety of comprehensive signals, like session depth, engagement rate & conversion probability, which would have been highly complex and time-consuming to accomplish through manual work.
Implementing a distinctively dynamic and assertive communication strategy towards high conversion-probability users is at the core of this methodology. Using digital signals to identify high-value clients early in the process, media budgets can prioritize audiences with the highest purchase potential. For example, as shown in the image below, visitor #2 is highly likely to complete a purchase; accordingly, ASUS should set bidding prices higher and implement a more assertive communication strategy for this user.
ROAS improvement and optimized budget planning
Accurately scoring users helps ASUS better understand the profiles of their high-value visitors, optimize budget planning by concentrating more resources on high-value users, and ultimately improve the ROAS of their media campaigns. ASUS saw huge performance uplifts across media channels, and the program is currently being rolled out on new markets.