Goal Expression in Ads Manager

Designing a shared language for communicating intent and defining successful advertising outcomes

Overview

Advertisers think in outcomes. They want to acquire customers, increase sales, drive subscriptions, generate leads, or improve return on ad spend. Meta’s advertising systems, however, were built around delivery mechanics: conversion events, optimization goals, bidding strategies, attribution settings, budgets, and performance controls. The platform was built in a way that reflected how the delivery system worked, rather than how advertisers thought about achieving outcomes.

While these tools enabled sophisticated optimization, they created a fundamental disconnect between how advertisers thought about success and how campaigns were configured. As a result, many advertisers struggled to translate their business goals into effective campaign setups.

I led design efforts to explore a new framework for connecting business objectives, marketing goals, campaign configuration, and performance measurement into a more intuitive and scalable experience. Our team set out to answer a simple question:

What if advertisers could express their goals directly, and the platform could translate those goals into the right campaign configuration?

My role

Product strategy
Experience vision
Information architecture
Interaction design
Prototyping
Research synthesis
Cross-functional alignment

Partnered with Content Design, Product Management, Research, Data Science, Engineering, and Product Marketing to define a new goal-oriented framework for campaign creation.

The problem

Nearly half of advertisers reported that they could not fully achieve their marketing goals through Meta’s ads, even though their goals were supported in the platform.

01: Complexity

Advertisers had low familiarity with Meta’s delivery systems and optimization models. Even experienced advertisers struggled to understand how performance controls interacted behind the scenes.

02: Fragmentation

Controls related to a single business objective were scattered across multiple surfaces. To express a simple goal like “drive profitable purchases from my website”, advertisers had to search for and configure multiple product levers.

03: Dependencies

Many controls contained hidden relationships and eligibility requirements. Changing one setting could have unexpected (and sometimes unexplainable) effects on others, making campaign creation feel unpredictable.

Researching advertiser mental models

To better understand how advertisers approached campaign setup, we conducted research focused on goal expression. One insight appeared consistently across sessions: advertisers rarely described goals using Meta’s terminology. They never said “I want conversion optimization for ad delivery using purchase events.” They said things like “I want to maximize purchases on my website,” or “I want the highest possible return from my budget.” The language advertisers naturally used was fundamentally different from the language required by the platform.

47% of advertisers weren’t optimizing campaigns toward their primary marketing objective.

85%

85% of advertisers made business decisions based on more than one KPI.

22% of advertisers made business decisions based on metrics that weren’t supported in product.

26% of advertisers were limited from sharing the data Meta needed to optimize for primary marketing goals.

18%

18% of advertisers defined successful advertising outcomes in ways that Meta did not measure.

25% of advertisers needed to optimize for more complex goals than Meta supported (pLTV, etc).

I identified a strategic opportunity to shift from configuration-first campaign setup to goal-first campaign setup. Instead of asking advertisers to select from a series of optimization products, we would simply ask them what they wanted to achieve, and let the system handle the complexity of configuring itself.

Advertisers in research sessions rarely described their goals in terms of attribution windows or delivery optimization strategies. They described outcomes. They used human-centered language to describe business value.

From Meta’s perspective, this same simple goal statement represented multiple different products owned by different teams across organizational lines, building disconnected experiences independently.

Hypothesis

Advertisers will optimize for their true business goals if they no longer need to understand Meta’s optimization framework.

By combining multiple individual product settings into “combos” that represented complex goal setups, advertisers will be able to more easily configure new ad campaigns to drive their true business objectives.

Before:

Use multiple fragmented products and complex configurations to try to drive true business goals.

After:

Enter true business goal, and let the system’s built-in dependencies and deep knowledge handle the complexity of configuration.

Solution

We converted goal expression into a unified framework for defining performance goals. Rather than configuring individual delivery settings across multiple surfaces, advertisers started by selecting a goal:

Maximize the number of conversions
Maximize the value of each new conversion
Maximize engagement on the ad itself
Maximize traffic to a destination website
And so on

The system then progressively revealed the supporting inputs required to achieve that outcome. Instead of navigating disconnected optimization products, advertisers interacted with a single conceptual model: define what success looks like, let the platform handled the rest.

Intelligent defaulting

The system predicts an advertiser’s most likely goals based on factors like budget and account maturity, so every campaign is configured for success from the start.

Natural language

Advertiser’s goals were framed in the language they’re accustomed to and already using to define success, rather than complex and technical product taxonomy.

Defining success

Because goals are tied to business metrics, performance goal became a cornerstone decision point for advertisers to define outcomes in familiar terms.

Validation

We evaluated the framework through usability testing with advertisers across experience levels. Participants were able to complete setup tasks successfully, explain goals confidently, and understand the impact of decisions. Most importantly, advertisers were able to consistently describe campaigns in terms of business outcomes rather than platform configurations.

8.2

Confidence

9.0

Satisfaction

8.6

Efficiency

7.2

Meets requirements

8.4

Ease of use

Outcome

For Meta

2x increase in deeper funnel outcomes
Advertisers adopted deeper funnel optimization products that were more aligned with their true business outcomes.

Improved revenue for deeper funnel products
Products like value optimization saw statistically significant revenue increases.

Created a scalable performance goal framework
Established a new abstraction layer that translated complex optimization settings into simple business goals, creating a foundation for future optimization products and automation initiatives.

For advertisers

Express goals more easily
Advertisers could choose goals like “Maximize number of conversions” or “Maximize value of conversions” instead of configuring technical optimization settings.

Reduced complexity and cognitive load
By organizing decisions around outcomes rather than product configurations, advertisers could set up campaigns more quickly and confidently.

Improved understanding of optimization products
Advanced capabilities became easier to discover and adopt because they were framed around business objectives rather than system terminology.

More work

Ad tech

Goal Expression in Meta’s Ads Manager