Ads recommendation

Most small businesses run their own Meta ads without an expert on staff, and small setup mistakes quietly waste their budget. Recommendations are small cards in Ads Manager that spot a problem with a running ad and suggest a fix: “These three ads are competing with each other, combine them into one.”

As the sole product designer for ad recommendations, I led their growth across the platform in four moves: from earning back advertisers’ trust, to scaling a system across 20+ teams, to earning back attention, to preventing setup problems entirely.

Ads Manager recommendation UI with campaign score and a recommendation card

Impact

direct revenue

recommendation adoption by advertisers

cost per result for advertisers

DurationJuly 2024 – November 2025

My roleProduct Designer

TeamPM, Engineer, Data Scientist

LocationBellevue, Washington

Recommendations felt like upsell ads for our own products, and people don’t trust it

When I joined, the platform had 12 recommendations, and 9 of them upsold Meta’s core products. Advertisers had learned we weren’t on their side: average adoption was 3%.

“It feels like Meta just wants me to spend more.”

- ONE ADVERTISER

Recommendation before

Spammy upsell

Promoted Meta products instead of fixing the advertiser’s problem.

Wide eligibility

The suggestion was too general, shown to nearly every account whether or not it matched their real problem.

USER PROBLEM

Small advertisers often get lost on the Ads Manager platform, have setup issues that quietly hurt ad performance

Small and medium businesses (SMB) set up their own Meta ads without an expert on staff, some setups is hurting ad performance.

01

High spend, low return

Poorly set up campaigns cause an average of 17% higher cost per result.

02

Suboptimal setup

Conflicting settings and missed best practices slip in during ad creation without advertisers noticing.

03

No way to diagnose

Small businesses cannot tell which setting is to blame or how to fix it.

I designed recommendations that resonate with advertisers, and fix real issues

I designed new recommendations that focusing on common setup issues advertisers really care about, and propose solution for them.

SOLUTION

Lead with real issues

Lead the recommendation with performance issue advertiser can see themselves, so it resonates.

Show up at the right moment, at the right place

Appears contextually at the time and place the issue happens, when it is most relevant and easiest to fix.

Preview modal for transparency

For complex fixes, a one step modal previews exactly what will change before advertisers confirm.

10 new recommendation launched · 5% avg adoption

up from the 2% baseline, growing into the $100M+ revenue impact

I set the design criteria so 20+ partner teams could launch hundreds of recommendations at the same quality bar

Challenge

As recommendation adoption rate improved, 20+ partner teams wanted to create their own recommendations, far more than one designer could make by hand.

Solution

So I turned my design process into a standard criteria, to support partner teams launching high quality recommendations on their own, increasing design velocity by 70%.

40 → {{ scaleNow }} recommendations shipped

Set the standard

I distilled design standards and criteria from the recommendations already live on the platform.

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Playbook and office hours

I codified the patterns into a playbook and consulted partner teams so every recommendation launched by partner team are at the same quality bar.

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Self-serve hub

I built an AI bot with data, past context, and Figma prototypes, so teams launch without waiting on me.

It answers partner team questions from past Q&A, with every standard resource linked, so collaboration moves faster.

It lets partner teams visualize the new recommendation experience end to end before writing a single spec.

Recommendations everywhere becomes noise, so I focused them into one hub

PROBLEM

Advertisers stopped paying attention with banner blindness

Recommendations scattered across every surface of the ad set editor

Overall recommendation adoption increase slowed down, and new recommendations barely got adopted. Research pointed to recommendation blindness: with too many recommendations floating around the platform, advertisers had tuned them out.

Too many at once

Recommendations showed up in every setting, feeling scammy and overwhelming.

One-size answers

Some solutions did not fit the business’s limitations or intent.

No proof

Advertisers could not tell if adopting one would actually improve performance.

Solution

I redesigned the experience so each complaint got a direct answer: a performance overview cuts the noise and surfaces only the recommendations that matter most, multiple solutions let advertisers choose what fits their business, and a forecast per solution shows the expected impact before they apply anything.

Performance overview

Cuts the noise: surfaces campaign health, delivery, and cost, and highlights the one issue hurting performance most.

Forecast per solution

Projects how each option will change performance, giving advertisers confidence to act.

Multi solutions

Options instead of one forced fix, so advertisers choose what fits their business.

After: campaign performance panel with root-cause diagnosis and choose-your-fix options

average adoption in the beta test

Vision with Meta AI - Move recommendations upstream to prevent issues in planning stage

PROBLEM

Fixes arrived only after budget was wasted

Recommendations, at their best, were still reactive. A card appears only after an ad underperforms, which means some budget is already gone. The end goal was never faster fixes. It was making fixes unnecessary.

Solution

So I folded the strategy into Meta’s AI vision: an assistant that monitors campaigns, catches issues before they surface, diagnoses the cause, and applies the fix with one-click approval. Small businesses get expert-level corrections without hiring an expert.

The next step moves recommendations upstream into campaign planning with Meta AI. Advertisers preview how different settings will perform and land on the setup that matches their intent, before a single dollar is misspent.