Value Expression · Hew Suber

01The stakes

Q4 2022

Performance+ was approaching $500M in revenue and beating targets when the requests started.

Hard constraints fragment the automation's ability to optimize: every audience excluded shrinks the pool it can target, every spending cap limits its runway.

Performance+ was working. Then advertisers started asking for things that would break it.

As adoption grew, a pattern emerged: advertisers kept asking for controls that would hurt their own performance. Cap retargeting spend at 30%. Exclude California. Limit delivery to five audience segments. These reflected real business needs: margin differences across customer types, inventory constraints, regional priorities.

The traditional model · constraints

“Only target California”
“Cap retargeting at 30%”
“Exclude these audiences”

The Value Expression model · signals

01“Prioritize California”

02“Weight prospecting (reaching new customers) higher”

03“These audiences matter more”

The same needs, expressed as signals.

Advertisers don't actually want constraints. They want to express what matters more.

The reframe that became the product family

02The strategy

2023 to 2024

The “signals, not constraints” language became how PM, Eng, and PMM talked about the product.

The framing recast months of circular control-versus-performance conversations as a new kind of input advertisers could give.

The reframe waited for its moment: only once the enterprise evidence made the urgency undeniable did constraints become signals.

Set the strategic direction. Developed the designer who shipped it.

My job on Value Expression was the strategy layer and the person carrying it: define the problem space, build the frameworks, and coach a mid-level designer from guided exploration to independent ownership.

Framing

Established the framing the org adopted; it settled the standing tradeoff between advertiser control and automation performance.

Coaching

Ran design reviews as the designer moved to owning the work. That designer shipped Bid Multiplier, then took on Audience Weight and Spend Preference independently.

Principles

Lean into value: importance in relative terms, not absolute budget figures. Simplicity over precision. Expectation setting: a preference, not a cap. History over forecasting.

Prioritization

Studied how Meta handles Advantage+ audience definition and budget allocation, then led prioritization with PM: Bid Multiplier sequenced first, Audience Weight and Spend Preference mapped as follow-on releases.

Performance+ targeting module before value expression: hard constraints only. — **Fig. 01 · Before.** The Performance+ targeting controls, limited to country level, with region or state exclusions. Intent arrives as hard rules that cannot be softened.

Performance+ targeting with value expression signals. — **Fig. 02 · After.** Performance+ targeting with value expression that guides how the system bids, with support for exclusions.

Fig. 03 · The reframe. Constraints become signals. The conceptual shift that became the foundation for Bid Multiplier, Audience Weight, and Spend Preference.

Traditional
model

Constraints

“Only target California”
“Cap retargeting at 30%”
“Exclude these audiences”

Fragments delivery

Shrinks what the model can learn from

Value expression
model

Signals

“Prioritize California”
“Weight prospecting higher”
“These audiences matter more”

Guides delivery

Gives the model more to learn from

03Shipped

Q3 2024 to Q2 2025

Advertisers read bid adjustments as spend guarantees; auction dynamics make spend unpredictable. Expectation setting was the hard part.

Warning states catch extreme settings; advanced controls stay available but not foregrounded.

Value Expression surfaces across three levels: account settings, campaigns that inherit those settings, and reporting that reflects them back.

The product leans on plain guidance that a signal is a preference, historical reporting over forecasting, and warning states for extreme settings.

Account-level value rules interface. — **Fig. 04 · Settings level.** Account-level value rules for locations, products, ad placements (where ads appear), and devices. They define an advertiser's way of doing business and inherit across campaigns.

Value rules reflected in Ads reporting. — **Fig. 05 · Reporting reflection.** The value rules surface back in Ads reporting, set against live campaign performance, so an advertiser can see the preference at work.Account and campaign figures shown are demo data.

Expectation-setting challenges and mitigations table. — **Fig. 06 · Expectation setting.** The hard part, mapped: a signal reads as a preference, not a cap; reporting shows what happened, not a forecast; warning states catch extreme settings.

$104M

Q4 2024, with Bid Multiplier

3

Products from one framework

Org-wide

Framework adopted

0

ML optimization fragmented

04What it enabled

2025 → 2026

The breakthrough was the framing; once it landed, the design principles, the UI patterns, and the reporting requirements all followed.

The same signal model kept extending: audiences, product groups, budget allocation, reporting.

Audience Weight · the same signal model applied to customer lists and custom audiences. Shipped

Priority Products · product-group-level bid prioritization for margin, inventory, and seasonality. Backend alpha

Spend Preference · prospecting versus retargeting allocation as a signal rather than a hard budget split. 2026 roadmap

Reporting clarity · a bid weight indicator shows how preferences translated to delivery outcomes. 2026 roadmap

The part I'm proudest of is the handoff: coaching a designer through the ambiguity of a new product category, from needing strategic direction to owning execution across three product lines. That growth was the model.

Signals, not constraints.