Modern attribution in user acquisition isn’t limited by a lack of data. It’s limited by the quality, shape, and timing of the signals we send. Signal engineering for user acquisition is the solution.
In a post-IDFA world, ad networks don’t optimize on raw events. They optimize on interpreted signals that are partial, delayed, and often stripped of context before they ever reach the algorithm, causing gaps in performance.
Our new feature, Pulsar, is Audiencelab’s answer to this problem.
It is not another attribution layer nor another SDK feature, but a signal engineering system designed to let growth teams decide what ad networks see, learn, and optimize toward.
Why signal engineering matters now
As platforms become more automated, marketers lose direct levers, but with Pulsar gain something else in return: leverage over inputs. The algorithms powering Meta, TikTok, and Google Ads are only as good as the signals they receive. Yet most setups still forward events in a generic, one-size-fits-all way where every purchase looks the same, level completion is treated equally, and every network receives identical data, regardless of how it optimizes. This results in expensive learning phases, noisy optimization, and campaigns that plateau early.
What Pulsar actually does
At its core, Pulsar is a high-performance signal forwarding and transformation layer built directly into Audiencelab. It consumes high-volume in-app events, enriches them with attribution metadata, applies marketer-defined logic, and forwards them to ad networks in the most algorithm-friendly way possible
Think of Pulsar as the system that sits between your app and ad platforms and you deciding:
- Which events matter
- How they should be shaped
- When they should be sent
- Where they should be routed
- All without locking you into a fixed measurement model
One event, many meanings
One of Pulsar’s core strengths is customer-driven signal engineering. Instead of sending the same raw event everywhere, teams can now define intentional logic inside Audiencelab:
- Only forward high-value purchases
- Rename events per network requirements
- Route specific signals to specific geos
- Send unattributed optimization signals without leaking click IDs
For example: A €1 purchase and a €50 purchase should not train the algorithm in the same way.
With Pulsar, they don’t have to. Marketers define the rules based on their goals. Pulsar executes them reliably, consistently, and at scale.
Deterministic attribution where it matters
With upcoming SDK updates, Pulsar supports deterministic event attribution using platform identifiers such as IDFA, IDFV, and GAID. This means events can be linked directly to:
- Click ID
- Creative
- Dataset or pixel
- Network source
- Reducing rematching drop-off and preserving signal accuracy where privacy allows
Importantly, this doesn’t replace Audiencelab’s privacy-first rematching logic, it complements it. Teams can opt in, opt out, or mix both approaches depending on their compliance and performance needs.
From raw data to algorithm-ready signals
Signal quality doesn’t matter if delivery fails. Pulsar is built for high-throughput, low-latency forwarding by using network-specific queues, intelligent batching by dataset or pixel, automated retry and backoff handling, and dead-letter queues for permanent failures. This ensures signals arrive on time, in the right format, and at the lowest possible cost, even during peak volume spikes.
Privacy is not an afterthought
We designed Pulsar for a privacy-first future by default with pseudonymous user identifiers, strict data minimization, and transparent and auditable behavior. At its core, it’s intentional signal design that respects regulation, performance requirements and gives control to marketers to optimize with customizable values.
By combining creative-level attribution, audience insights, and now precision signal engineering, Audiencelab gives growth teams control with the inputs that drive automated optimization.
Why this matters for performance teams
Pulsar enables teams to:
- Train ad algorithms faster
- Reduce wasted learning signals
- Align optimization with real business value
- Scale without losing measurement clarity
In short, it turns raw in-app events into high-quality, algorithm-ready signals for modern user acquisition. Where automation keeps increasing, signal quality is no longer a nice-to-have it’s the strategy.
If you aren’t already using Audiencelab, what are you waiting for? Let’s talk!
