Facebook & Instagram Ads Management — Scaling E-commerce & Lead Gen Campaigns Case Study

Facebook & Instagram Ads Management — Scaling E-commerce & Lead Gen Campaigns

A multi-client Meta Ads engagement spanning fashion, jewelry, and general e-commerce brands — combining structured A/B testing, audience scaling, and budget optimization to turn ad spend into consistently profitable, trackable revenue.

2023 – 2026

Meta Ads, Facebook Ads Manager, ROAS Optimization, Audience Targeting, A/B Testing, E-commerce Marketing

This engagement covered Facebook & Instagram ad campaign management for multiple e-commerce and lead-generation clients, with the core objective of maximizing return on ad spend rather than just driving traffic.

Campaign structures were built around broad-audience testing paired with lookalike audiences, allowing budget to scale without over-relying on narrow targeting that typically increases cost per result over time. Every campaign was set up with structured A/B tests across creatives and audiences, and budgets were reallocated weekly based on which ad sets were actually converting — not just generating clicks.

The results reflected this approach at both ends of the spectrum. A broad-audience campaign scaled to $208,729 in spend while maintaining a 2.56x average ROAS across 2,414 purchases. On a smaller, tightly-optimized campaign, a $2,474 spend generated $48,995 in purchase value — a 19.80x ROAS. Across a larger account managing over 1.3M SEK in spend, the account average held at 3.79x ROAS, proving the strategy held up at scale, not just on small test budgets.

Mission

To help each client turn ad spend into predictable, profitable revenue — whether that meant scaling a small budget efficiently or managing a large, multi-campaign account without losing performance.

Challenge

Accounts were either underspending on audiences that could handle more budget, or scaling too aggressively without the audience structure to support it — in both cases, ROAS was left on the table.

Solution

Restructured campaigns around broad and lookalike audience testing, then scaled budget only behind ad sets with proven performance — continuously feeding the algorithm cleaner signals instead of manually micromanaging every ad set.