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What Are Signals?

Signals are predictions derived from Innkeepr’s causal models and come into two different forms:
  • Audience Signals – Predictive user segments for targeting, lookalikes, and exclusions
  • Conversion Signals – Incrementality-weighted conversion values to enhance bidding systems
Both signal types are powered by Innkeepr’s causal engine, which estimates the true incremental impact of your marketing touchpoints.

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How Signals Work

Data
Innkeepr collects behavioral data through client-side tracking (innkeepr.js, server-side APIs, and warehouse connections. This data is processed into time-series user sessions containing interaction sequences, temporal context, marketing exposure, and user attributes. Objectives
Signals are optimized for specific business outcomes defined through objective functions - the measurable results you want to drive, they are called Objectives Common objectives include:
  • Conversion likelihood – Binary outcomes like purchases, sign-ups, or installs
  • Customer lifetime value (CLV) – Predicted value over 90 or 180 days
  • Lead qualification – MQL probability based on engagement and firmographic data
  • Custom objectives – Any measurable outcome from tracked events or calculated traits
Objectives can be filtered by additional traits, such as “CLV for new customers in EU markets” or “7-day retention from paid social traffic.”
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  • Objectives
Treatments & Attribution
Treatments represent marketing touchpoints - ad clicks, email opens, campaign landing page visits. Innkeepr uses 7-day first-touch attribution to assign treatment exposure to user sessions based on UTM parameters, referrer data, and platform click IDs. Treatments map to platform-native campaign structures: ad sets (Meta), ad groups (Google/TikTok), and asset groups (Performance Max). This granularity reveals which specific campaign elements drive the most incremental lift. Treatment data is the foundation for signal learning. The causal engine observes which users were exposed to which treatments and what outcomes they achieved, then estimates individual treatment effects - the predicted incremental impact of a marketing treatment on a user’s likelihood to reach an objective. Treatment effects answer: “If this user sees this ad, how much will their conversion probability increase compared to baseline?”

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Signal Types

Audience Signals
Audience signals are predictive user segments built from treatment effect scores. They identify users most (or least) likely to deliver incremental value from marketing exposure. Use cases:
  • Seed audiences – High-incrementality users (top 10-20%) for lookalike expansion
  • Exclusion audiences – Low-incrementality users to suppress wasted spend
  • Retargeting audiences – Users predicted to respond incrementally to re-engagement
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Conversion Signals
Conversion signals adjust conversion values by incremental contribution. Instead of passing raw transaction amounts to platform algorithms, Innkeepr weights each conversion based on its predicted lift over baseline. How it works: When a €100 conversion occurs and the predicted treatment effect indicates 60% incremental lift, the signal passes €60 as the incrementality-weighted value. This trains bidding algorithms to prioritize users and touchpoints that deliver true causal impact.
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Signal Quality Over Time
As more behavioral data flows through Innkeepr, treatment effect models become more precise. Early implementations may have wider confidence intervals; after 2-3 model training cycles (4-6 weeks), predictions stabilize and signal quality improves measurably. Monitoring
Innkeepr tracks signal health through prediction accuracy, audience overlap rates, conversion signal delivery rates, and model confidence scores.