Partner & Marketing Platforms

Build better decisions on top of better attribution

Partner and marketing platforms depend on knowing what actually drove a conversion, which partners deserve more investment, and where revenue is leaking.

Forma Pro helps build the data, scoring and decision systems behind attribution, partner performance, commissions and campaign intelligence.

Talk to us about your attribution or partner-data challenge
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Attribution foundation

When the attribution layer is weak, everything above it becomes harder to trust

Most platform problems that look like “AI problems” or “analytics problems” start lower in the stack.

We work on the systems underneath those decisions, from event and attribution pipelines to scoring, anomaly detection and operational tooling.

  1. Events arrive late or out of order

  2. Channels report differently

  3. Duplicate conversions need to be resolved

  4. Commission logic sits in a separate layer

  5. Partner-performance models are trained on incomplete signals

Reports disagree

Scoring models drift away from actual commercial outcomes

Teams spend more time reconciling data than acting on it

Decision systems

Where we typically contribute

Attribution and conversion data

Reliable attribution is an infrastructure problem before it is a modeling problem.

We help design and improve event pipelines that can handle inconsistent inputs, delayed events, deduplication, conversion crediting and the business rules that sit between raw activity and reported outcomes.

This creates a more dependable foundation for reporting, optimization and ML.

Typical applications

  • Event pipelines
  • Delayed events
  • Deduplication
  • Conversion crediting

Partner and creator scoring

The biggest partners are already obvious. The harder problem is identifying smaller publishers, creators or channels with genuine upside before their value is visible in aggregate reporting.

We build scoring systems that combine conversion history, attribution signals, partner behavior and other relevant data to rank future potential rather than simply repeat past spend.

The same approach can support publisher development, creator programs, channel allocation and partner discovery.

Typical applications

  • Publisher development
  • Creator programs
  • Channel allocation
  • Partner discovery

Commission integrity and anomaly detection

Commission leakage rarely arrives as one clean fraudulent event.

It appears as unusual partner behavior, changing conversion velocity, duplicate or inconsistent crediting, payout timing anomalies or combinations of signals that are easy to miss in periodic manual reviews.

We build monitoring and investigation tooling that helps surface suspicious patterns earlier and gives operational teams enough context to review them.

Typical applications

  • Behavior monitoring
  • Crediting review
  • Payout anomalies
  • Investigation tooling
  • Human review

Campaign and partner intelligence

Marketing data is useful only when it changes a decision.

We build analytics and ML systems for questions such as:

  • Which partners are creating incremental value?
  • Which campaigns or audiences are deteriorating before the headline metrics show it?
  • Where should budget or partner-management effort move next?
  • Which anomalies require human review?
  • Which repetitive campaign or partner operations can be safely automated?

Where appropriate, agentic workflows can prepare analysis, recommendations or operational actions while keeping important decisions under human control.

AI and the source of truth

AI works better when it is connected to the source of truth

Adding an AI layer to a marketing platform is easy. Making its outputs reliable is much harder.

The visible layer
  • Models
  • Scores
  • Recommendations
  • Automation
What has to be connected underneath
  • Attribution decisions
  • Crediting logic
  • Commission adjustments
  • The underlying business logic

A scoring model that only sees clean reporting tables can miss the attribution decisions, commission adjustments and event-quality problems that created those tables in the first place.

Our approach is to connect models and automation to the underlying data and business logic whenever the problem requires it.

The tool follows the problem, not the other way around.
  • Machine learning for scoring or anomaly detection
  • An LLM-based workflow for operations
  • Simply better data engineering

Partner economics

Built for platforms where partner economics matter

The strongest fit is with products where partner, affiliate, creator, advertising or performance data is part of the core business model.

Typical environments

  • Affiliate and partner-marketing platforms
  • Creator and influencer platforms
  • Attribution and measurement products
  • Performance marketing and ad-tech platforms
  • Retail and commerce-media systems
  • Marketing products with complex channel or commission logic

Embedded senior engineers

Forma Pro works inside the existing product organization, delivery process and priorities.

Ownership of a focused stream

Forma Pro takes responsibility for a bounded problem such as a data pipeline, scoring layer, monitoring system or AI-enabled workflow.

Production perspective

Experience behind the work

Members of Forma Pro’s team have production experience with attribution, conversion tracking, partner scoring, commission logic and real-time crediting in large-scale partner-marketing environments.

That experience now informs the systems we build for smaller platforms facing the same underlying problems at a different scale: incomplete attribution, invisible partner potential, operationally expensive commission logic and fragmented campaign data.

Related Case Study

Illuminating the Long Tail

How adaptive partner scoring and real-time crediting can help a creator-commerce platform identify high-potential partners that static historical reporting leaves invisible.

Read the Case Study

A useful first conversation

Have an attribution, scoring or partner-data problem?

We can review the product context, the data available and the decisions the system needs to support, then identify where engineering or applied AI can make a meaningful difference.

Talk to us about your attribution or partner-data challenge