Innkeepr is the signal optimization platform that makes your marketing stack incrementality-aware. Our causal analytics engine sits between your customer data and engagement tools - transforming behavioral data into audience and conversion signals optimized for incremental impact on your business metrics.

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Overview

Innkeepr operates as data infrastructure for your marketing stack. We process interaction data from websites, apps, and backend systems to identify cause-and-effect relationships between touchpoints and business outcomes. These insights become incrementality-optimized signals that flow directly into Google, Meta, TikTok, Amazon, and other platforms. Two components activate Innkeepr: Event collection: Connect your data sources using Innkeepr’s tracking libraries or server-side APIs. Send events like checkout_completed, signup, or product_viewed with relevant attributes (order_value, is_new_customer, source). Objective definition: Define what matters to your business - CLV, purchase probability, AOV, retention - and Innkeepr optimizes signals for those outcomes. Once configured, Innkeepr analyzes daily traffic to detect which touchpoints drive incremental impact on your objectives. These predictions become signals that sync automatically to connected platforms, making your entire marketing stack optimize for causation, not correlation.

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Common use cases

Marketing and growth teams use Innkeepr’s signal layer to:

• Seed lookalikes with high-incrementality users for new customer acquisition
• Weight conversion values by incremental contribution
• Target segments by predicted CLV impact
• Suppress spend on low-AOV or non-incremental traffic
• Build exclusion audiences that protect budget

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Collecting data

Innkeepr provides tracking infrastructure through two source types: Innkeepr.js – Client-side tracking for web properties. Captures behavioral data as users navigate your site. Server-side sources – Backend tracking for mission-critical events like revenue or when client-side isn’t feasible.

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The Innkeepr tracking spec

Our tracking libraries follow a standardized message structure that feeds Innkeepr’s causal engine. Three core methods capture user context:

• Identify – Who is the user?
• Page – What are they viewing?
• Track – What action are they taking?

Consistent implementation ensures clean signal generation. These events flow into our causal analytics engine and become activation-ready signals for downstream platforms.

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How Innkeepr builds incrementality signals

Signals are how Innkeepr makes platform algorithms incrementality-aware. The system processes two inputs: objectives (the business outcomes you’re optimizing for) and treatments (the marketing touchpoints users encountered). Together, these power our causal engine.

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Objectives

Objectives define what success means for your business—CLV, purchase probability, order value, retention. They can come from tracked events (checkout_completed with revenue) or calculated user traits (is_new_customer, revenue_30d). You can define multiple objectives and version them independently. Segment by additional traits like CLV for new customers in EU or 7-day retention from paid social to optimize different growth surfaces.

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Signals

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Audience signals

Predictive segments built on forecasted incremental value. Instead of targeting by demographics or past behavior, Innkeepr identifies users most likely to deliver lift against your objective. These segments sync directly into platform audience targeting (Meta Custom Audiences, Google Customer Match) so algorithms focus on high-incrementality users.

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Conversion signals

Conversion values weighted by incremental contribution. Rather than passing raw transaction amounts, Innkeepr adjusts each conversion based on its predicted lift over baseline. Platform bidding algorithms receive signals aligned with actual causal impact—not just correlation with sales.

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Treatment effects

Innkeepr’s signals are powered by continuous analysis of treatment effects - the incremental impact of specific marketing touchpoints on your objectives. Treatments in Innkeepr correspond to ad sets, ad groups, or asset groups across Google, Meta, and other platforms. Our attribution system matches each session to the treatment that drove it using a 7-day first-touch attribution model, allowing us to determine which touchpoints caused each visit and how users behaved afterward. Treatment effects quantify incremental impact. For example: This Meta ad set drove a +€12 lift in CLV compared to baseline. Innkeepr learns these effects from historical data and continuously refines predictions as new data flows in. This feedback loop keeps models aligned with current behavior and market conditions. Over time, signal accuracy improves—meaning connected platforms optimize bidding and targeting based on the most current causal insights available.