We began with a deep dive into the client’s Meta Ads account history and analytics. By analyzing seasonal trends and product performance, we identified top-performing ads and products. They became our “starting pool” to target a broad US audience (18-65+). Later, we expanded to Canada for further reach.

To target the right users, we built special audiences:

• Lookalike audiences. We created diverse audiences based on video viewers, page engagers, purchasers, and checkout initiators – all with maximum lookback windows. To maximize audience size and optimize cost efficiency, we strategically grouped these audiences in various combinations.
• Advantage+ audiences. We used the Advantage+ audience in the Advantage+ shopping campaigns, which was very effective for all our eCommerce clients.
• Remarketing audiences. Grouping users who already interacted with the client’s ads or website allowed us to lower CPMs.

Initially, CBO (campaign budget optimization) was ineffective for scaling within Meta Ads. However, it started showing great results after giving it more time and budget to learn. We adopted CBO for most Meta Ads campaigns, with a few exceptions.

Sometimes, certain ads got all the attention and ran out of stock, disrupting the continuity of the ad campaigns. To fix this, we created separate campaigns for specific products we wanted to push. This way, if one best-seller product ran out, the others kept going strong, and our budget wouldn’t drop significantly.

After dozens of experiments, we focused on the best ad formats, like image ads, animations, and videos for men (showing the product, reviews, price, and USPs) and UGC for women (highlighting discounts). We kept trying new things but always prioritized the winners.

We also tested various messaging and found that novelty (“new product”) and scarcity (“sells out fast”) themes resonate strongly. Sales and discounts were important parts of the strategy, and we tested various offers to reach maximum efficiency. We noticed that 10% price increases didn’t really affect the performance of best-selling products.

To have TikTok ad incrementality in the media mix, we tested new ad formats and experimented with different content, always keeping an eye on what brought in money without hurting profits. Boosting videos that were already organically gaining traction on TikTok also worked really well. We also tried various audience targets but ultimately found broad interest segments brought the best results.

For Google Ads, we analyzed the product and the seasonality trends and based our account structure to maximize the delivery of best-performing product segments.

Recognizing the higher profit margins in men’s products, we tailored our campaigns with a gendered approach. We never stopped testing new keywords, ad structures, and bidding strategies, even gleaning insights from Shopping and PMax campaigns to optimize Search campaigns further.

One of the main challenges was sustaining growth in a very competitive environment. We decided to look for niche, long-tail keywords to promote the products. We also used a split approach to promote the products across Shopping listings, scaling the best-performing products in one campaign and sorting underperforming products in another to better control the spending and bids.