Closely monitor segmented campaigns alongside the client to incorporate their feedback

Campaigns have been segmented by countries and languages, which requires close monitoring and rapid updates based on client feedback. We met with the client every second day to align on campaign updates and share performance progress. This allowed us to stay agile to client feedback as well as promotional and regional updates, which are crucial for seasonal campaigns.

Run multivariate tests using machine learning

We utilized a dynamic creative ad format, which takes multiple media (images, videos) and multiple ad components (such as headlines, titles, text and calls-to-action) and then mixes and matches them in new ways to improve audience engagement. It allows advertisers to automatically create personalized creative variations for each person who views your ad, with results that are scalable.

Adopt a data-driven approach to the bidding strategy updates and budget distribution

Based on the large audience size and its accelerated growth rate during this specific time of year, we opted for a more aggressive budget distribution towards buyers rather than free users. 80% of the budget was assigned to the Buyer’s campaigns, whereas 20% of the budget was assigned to the Free User’s campaigns.

We began with a conversion-focused bidding strategy to accumulate conversion volume and exit the learning phase rapidly before moving into a cost cap strategy. This allowed us to maximize results within the daily budget limit, eventually shifting campaigns with sufficient performance data into target ROAS to align with client’s main KPI (revenue goal).