Optimizing Your Meta Ads: Why Multiple Creatives Per Adset Is the New Standard
In the fast-paced world of ecommerce, especially for agencies managing multiple client accounts, staying on top of platform changes and best practices is crucial. One area that consistently sparks debate and demands attention is Meta ad campaign structure. Recently, a very insightful discussion unfolded in an ecommerce community, touching on a core question that many of us grapple with: is it better to run one ad per adset or multiple ads per adset within a CBO (Campaign Budget Optimization) campaign?
The original poster in the discussion shared their current setup: a CBO testing campaign and a CBO winning ads campaign, each featuring one ad per adset. They admitted this structure was profitable for them but questioned if there was a more optimized way, specifically by grouping multiple similar ads under a single adset. This is a classic dilemma, and the community's response offered a fantastic deep dive into modern Meta ad strategy.
The Evolution of Meta Ads: From Targeting to Creative
One of the most compelling points raised by a community member highlighted a significant shift in Meta's approach, particularly post-Andromeda update. The old paradigm often involved meticulously crafting adsets to target different customer personas. However, the new Meta wants something different: "Your ads are now your targeting."
What does this mean for agency teams? Essentially, Meta's algorithm has become incredibly sophisticated. With open targeting and multiple creatives within an adset, Meta's AI can identify which audience cohorts engage best with specific ads and then dynamically push those ads to the most receptive users. This approach, as one respondent noted, often "works significantly better than the 'old' way of trying to scale a single ad in an ABO campaign."
Why Multiple Ads Per Adset Wins (Most of the Time)
The core of the argument for using multiple ads per adset lies in how Meta's learning phase and budget allocation mechanisms work. Another expert contributor broke it down brilliantly:
- Learning Phase Efficiency: Each adset has its own learning phase and budget pool. If you spread your budget thinly across many adsets (e.g., one ad per adset), you're essentially creating multiple, smaller learning phases that compete with each other. This can lead to erratic delivery and prevent any single adset from getting enough data to stabilize.
- Optimized Budget Allocation: When you place multiple ads under one adset, Meta views them as competing creatives in a single auction context. This allows the system to shift budget in real-time to the winning creatives, accelerating the learning phase and providing cleaner data on which ads are truly performing. Imagine a $100/day budget split across five adsets (one ad each) versus one adset with five ads. In the former, each adset gets $20/day, potentially below the threshold for stable signal. In the latter, the single adset gets the full $100/day, allowing Meta to find the best performing creative much faster.
This "1 ad per adset" advice, as pointed out, is largely "leftover from when interest stacking and audience splitting was the optimization lever" and "died with iOS 14.5 and definitely isn't right post-Andromeda."
The Crucial Caveat: Is Your Profitability Truly Optimized?
While the technical arguments for multiple ads per adset are strong, one seasoned community member offered a vital piece of advice: "If your business is financially healthy and growing, then I would never recommend you change any structure that is working for you."
This isn't to say you shouldn't optimize, but it's a call for introspection. They highlighted two common pitfalls when brands claim profitability:
- Wrong Profitability Calculation: Are you truly accounting for all costs? Many businesses mistakenly calculate their profitability, leading to a false sense of security.
- Stuck in a "Death Spiral": Sometimes, brands obsess over protecting early profitability at the expense of growth, getting stuck in a lower revenue trap.
Before making significant changes, it's wise to audit your brand's financial health and growth trajectory. This contributor even shared valuable resources on eCommerce Financials - important metrics to track and Margin obsession troubles. They also cautioned against spreading your budget too thin, especially if your daily ad budget is below $1,000. Complex account structures often seen in case studies are typically for brands spending hundreds of thousands or even millions per month.
To help assess if your budget supports your adset count, they even shared a helpful formula represented in this diagram:
Recommended Meta Ad Structure for Agencies
Based on the latest insights and successful client campaigns, here's a simplified yet powerful structure that agencies should consider for their ecommerce clients:
- Campaign Type: 1 Advantage+ Shopping Campaign.
- Adsets: Max 2-3 adsets, segmented by a meaningful axis (e.g., prospecting vs. site visitors, or geographical regions if applicable).
- Ads per Adset: 4-6 ads per adset, representing different angles, hooks, or formats to allow Meta's AI to optimize.
This structure ensures your budget isn't spread too thin, allows Meta's algorithms to learn efficiently, and ultimately drives better performance for your clients.
EShopSet Team Comment
We at EShopSet firmly believe that simplifying ad account structures for Meta, especially post-Andromeda, is a non-negotiable for modern ecommerce agencies. The "one ad per adset" strategy is outdated and inefficient, leading to budget waste and suboptimal learning phases. Agencies should embrace the shift towards fewer adsets with more diverse creatives, allowing Meta's powerful AI to do the heavy lifting in audience discovery and optimization. However, always validate a client's true profitability and growth before overhauling a "working" system—sometimes, the perceived profitability masks deeper issues.
Ultimately, the world of Meta ads is constantly evolving. What worked yesterday might not be optimal today. For agency owners, PMs, and developers, understanding these nuances is key to delivering superior results and staying ahead of the curve. Keep testing, keep learning, and keep those conversations going!
