Add Audience Alignment to Signals Protocol

[bokelley]

Summary

Add audience alignment capabilities to the Signals Protocol, parallel to how Content Standards handles content alignment. This enables buyers to describe who they're looking for and calibrate that understanding with sellers through dialogue.

Background

Content Standards Protocol handles:

• Standards: Hard rules for content exclusion (safety, suitability)
• Calibration: Dialogue to align on content interpretation

Audience Alignment would handle:

• Stories: Positive descriptions of target audiences ("cat yoga people", "wellness-minded millennials")
• Calibration: Dialogue to align on audience interpretation

Key Concepts

Stories vs Standards

• Standards (Content): "Here's what I don't want" → pass/fail (binary)
• Stories (Audience): "Here's who I'm looking for" → affinity matching (gradient)

Example: Cat Yoga Story

A buyer doesn't just want "cat yoga content" - they want to reach "cat yoga people" or "people who like what cat yoga people like" (wellness, flexibility, mindfulness, pet lovers).

Stories are defined through examples:

• "Someone browsing morning stretching routines? Perfect."
• "Someone researching competitive powerlifting? Close but not quite."
• "Someone reading about meditation retreats? Yes, that's the vibe."

Calibration Flow

Same pattern as content calibration:

1. Buyer describes target audience (natural language + examples)
2. Seller's signal agent evaluates against available signals
3. Back-and-forth dialogue to refine understanding
4. Seller trains local model for runtime matching

Applies Across Dimensions

Stories could work for:

• Audiences: "People who exhibit cat-yoga-adjacent behaviors"
• Context: "Moments when people are relaxed and browsing"
• Content: "Articles about wellness, mindfulness, pets"

Proposed Tasks

Task	Description
create_audience_story	Define a target audience through description + examples
calibrate_audience	Dialogue to align on audience interpretation
match_audience	Runtime evaluation of signals against a story

Questions to Resolve

1. Is the response pass/fail or a score/ranking?
2. How do stories compose with standards? (e.g., "cat yoga people" + "no political content")
3. Should stories be first-class objects with CRUD, or embedded in signal activation?

Related

• Content Standards Protocol (parallel pattern for content)
• Signals Protocol activate_signal (where audience matching would apply)

[bokelley]

The key here is that we want to be able to evaluate a "vector" of characteristics from the signals provider. This could be derived from first-party data about the user, but could also be inferred from a postal code, page context, or other sources. Example:

buyer: "I want cat yoga people" --> geo-intent company finds 200 postal codes where this kind of people live and shares the attributes of those postal codes --> ["suburban dads", "love football", "don't have pets"]
buyer: No! That's not right, that doesn't seem like my people. Try again.

Then, post-campaign, the seller could send vectors of audience attributes through to the buyer and share of delivery: ["cohort": {"dads", "50-59", "no pets"}, 3.5%] and then the buyer can use this to decide how "on target" this was. Of course all of this presumes that the seller here is honest... but of course this is where measurement and studies come to bear around validating this