Meta · Campaign Recommendations · 2020 to 2021

A recommendation framework for Meta Ads Manager.

Ads Manager kept telling advertisers how to fix a campaign mid-flight. They had learned to ignore it.

Role
Product DesignerAcross product and platform
Company
Facebook (Meta)Ads Manager · Mid-Flight Recommendations
Timeline
2020 to 2021Feedback shipped 2021 · framework followed
Impact
Framework adoptedPartner teams build their own recommendations
01The stakes
2020

Delivery is the machine learning system deciding where, when, and to whom an ad shows.

Recommendations were held to one job, surfacing where a campaign was losing performance and never selling a feature. Each one traced to what the delivery model could predict, ROAS or conversions, not to what the platform wanted to promote.

Accuracy wasn't the problem. Trust was.

Mid-flight recommendations were meant to help advertisers fix results while a campaign was still running. Shipping a new recommendation type was slow. Relevance was uneven. And even accurate advice died in workflows too complex to act on.

Before · bespoke per case
  • Every recommendation designed one-off
  • No shared design or content framework
  • Relevance hit or miss for advertisers
  • Complex workflows blocking adoption
  • Approvals on every detail
After · unified framework
01One framework any team can apply
02Feedback from advertisers, in the product
03Simpler workflows to act on advice
Any team ships without starting from zero.

An ignored recommendation isn't neutral. It spends the trust the next one needs.

The core tension both workstreams answered
02The workstreams
2020 to 2021

Operated across Product Marketing, Content Strategy, UX Research, Engineering, Data Science, and the Interfaces Platform team.

Two workstreams ran at the same time. I was the designer on both.

One was a framework, so a new recommendation didn't have to be designed from scratch every time. The other was a feedback system, so the recommendations could learn from being ignored.

Workflow types

Named five interaction patterns, Flow A through E. The names gave five teams one vocabulary.

Research

Ran concept studies asking what advertisers expected feedback to change, and whether a dismissed recommendation should ever return.

Framework

Built the design and content framework with the platform team, replacing per-case approvals with patterns any team could apply. After that, adding a recommendation didn't mean adding another round of approvals.

Feedback

Mapped lightweight advertiser feedback to signal the ranking system could read.

Fig. 01 · Before the framework. Cost scaled with the number of cases.
Every delivery case, rebuilt from zero
Lookalike drop-off Budget pacing Schedule gap + every other case
One-off design One-off content Manual approval
Shipsone at a time
Fig. 02 · Concept studies. The question they surfaced, whether a dismissed recommendation should ever come back, shipped as snooze.
We built the concepts to get a reaction
Projected impact shown up front The reason a recommendation surfaced Dismiss, with a reason attached
“Should a dismissed recommendation ever come back?”the question nobody had asked
Shipped as snooze
03The framework
2021

A dismissed recommendation could return with updated evidence and a “returning with new information” indicator. That addressed the cry-wolf problem directly.

Five workflow types, one framework.

The framework set how a recommendation looks, reads, and behaves. Then it ran against a live case, Lookalike audiences, which find new people who resemble existing customers.

Fig. 03 · The workflow framework. Developed with the platform team.
One recommendation, five ways to act on it
A recommendationone framework for how it looks, reads, and behaves
Flow ABottom sheet in Account Overview
Flow BBottom sheet in the campaign table
Flow CEdit-panel flow
Flow DBulk edit in the table
Flow EIn-line edit
Fig. 04 · A recommendation, mid-flight. A goal-at-risk card on the account overview, then a guided workflow that combines similar ad sets so they can leave the learning phase.
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Fig. 05 · Resolution, automated. The same case through the simplified workflow. Resolving the issue moves from a multi-step edit to a guided action.
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Fig. 06 · Feedback expressions. Snooze, mute, and dismiss turned advertiser feedback into signal the ranking system could read. Shipped in 2021.
The first closed loop between behavior and what surfaces next
Recommendation shown
Advertiser respondsact · snooze · mute · dismiss
Mapped to signalplus adoption speed, time to action
Ranking learnswhich surface, and when
↻ a better next recommendation
Fig. 07 · Intent-aware feedback, in motion. The expressions respond to why a person is dismissing, so every dismissal finally carries a different signal.
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5
Workflow types, one framework
↑
Resolution rate up, first live test
2021
Feedback expressions shipped
04What it outlasted
2021 onward

The framework’s job was other teams shipping without us.

Partner teams started building their own recommendations on the framework. Resolution rate went up in the first live test.

Every dismissal used to read the same to the ranking model. Snooze, mute, and dismiss split them apart, and snooze covered the thing nobody had designed for, an advertiser saying not now. That work became an engineering workstream on recommendation ranking, and it was still running after I moved on.

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