I led a shift in paid social strategy at my previous work by personalizing retention campaigns for over 4M past purchasers. With only 20% of the budget allocated to retention, CTR was stagnating at 0.9% due to generic, one size fits all messaging. Partnering with the data science team, I developed a segmentation framework based on customer affinity, engagement, LTV, and behavior.

I built targeted strategies for high, medium, and low affinity users, reallocated budget accordingly (50/30/20 split), and launched tailored campaigns. This data driven shift resulted in an 18% improvement in CTR and a 20% lift in efficiency among high value customers all within 30 days.

At my previous work I noticed inefficiencies in the Google Shopping structure, which was based on manual product categorization and lacked performance based segmentation. I took ownership of the challenge and collaborated with the data science team to build a dynamic product grouping model that factored in seasonality, inventory, profitability, and sales trends.

The team created 5 product clusters, updated via supplemental feeds to Google every two weeks. An A/B test between the legacy structure and the new segmentation model ran for 3 months. The result: 79% improvement in conversion rate and 40% increase in channel efficiency, all with just a 16% increase in spend.

When I took over Paid Social channel, the channel was underperforming and declining YoY. After digging into incomplete performance data, I uncovered that Instagram was driving better ROAS and revenue compared to Facebook, despite Meta allocating more spend to Facebook.

To test my hypothesis, I manually split campaigns by placement and forced even spend across both platforms. After a 3 month test, Instagram emerged as the higher performing platform, validating my strategy. Paid Social turned into a profitable channel and YoY revenue more than doubled, this contributed to redefine investment strategy on Meta.