Building for Outcomes

As a MarTech engineer, what drives you? MarTech engineers are often tasked with building bridges, making connections, pipelines across seemingly disjoint set of tools that do completely disparate things. All these tooling is expected to unlock a new vein of experimentation, speed up processes so you can do more experimentation, better measurement of experiments or simply save costs of experimentation. In theory it all looks good, after all MarTech is marketing enablement. So whenever you ship a new API, integrate a new vendor, add a new event, that should start ringing up the cash registers, right?

In practice, however, when the math strikes, the reality is rarely so.

A variety of reasons why they do not check out.

1. The solution was not the right solution in the first place
2. Solution was an MVP and was not meant to scale, i.e. does not allow for all types of experiments that would enable the kind of incremental revenue that this was sold on. This one is the most common.
3. The discount factor was not counted when the incremental revenue impact was thrown around a lot
4. The time horizon for the incremental revenue to break in, and the engineering efforts to keep the systems running up until that point, does not really add up.
5. Running two or more systems concurrently that do similar things,

While, we can all finger point each other — PMs, Engineers, Marketing, Data Engineering and so on, the fact that all these problems exist because we speak different languages. There is rarely a common taxonomy, or a value framework that we all subscribe to.

Enter the OLED Framework. OLED is a lightweigh framework that expands to Outcomes, Levers, Experiments and Diagnostics. It doesn’t require a new tool, a new process, or a new committee. It simply forces clarity — the kind of clarity that prevents wasted work, misaligned expectations, and “we shipped it, but nothing changed” moments.

Outcome

Define the business result before you define the feature

Most teams start with the feature.
“What if we add this event?”
“What if we build this workflow?”
“What if we integrate this tool?”

Outcome‑oriented teams start with the business result.

• Increase conversion by X%
• Reduce churn for Y segment by 5%
• Improve identity match rate
• Increase experiment velocity
• Reduce time‑to‑launch for campaigns
• Improve data freshness or reliability

Your outcome should not simply be a vague “increase incremental revenue by X basis points”. You want to break it down into as simple terms as possible, simply because it forces clarity of thought for everyone.

Levers

Identify the system levers the feature actually influences. This is where engineers thrive. Every feature touches a lever — a part of the system that can actually move the outcome.

Examples of levers:

• Data quality (accuracy, completeness, latency)
• Identity resolution (match rate, dedupe rate)
• Activation speed (API latency, batch windows)
• Experimentation throughput (# of tests per month)
• Content velocity (time to generate variants)
• Model performance (AUC, precision, recall)

This step answers the critical question:

“Does this feature actually have the power to move the outcome we care about?”

It prevents magical thinking — the belief that a feature will influence a KPI it has no leverage over.

Experiments

Define how you’ll validate the impact — before you ship. Every feature should have a validation plan:

• A/B test
• Holdout group
• Stealth mode releases
• Dogfooding releases
• Pre/Post comparison
• Model offline vs online comparison

This step protects engineering teams from being blamed for “no impact” when the business never set up a way to measure impact. It also protects product teams from shipping features that feel good but do nothing.

Diagnostics

Define the signals that tell you why it worked or didn’t. How long you want to keep running this until you kill it or build the next phase. This is the most overlooked part of outcome measurement — and the most important.

Diagnostics are the leading and lagging indicators that tell you whether the feature is behaving as expected.

Examples:

• Event volume
• Metadata completeness
• API latency
• Error rates
• Identity match rate
• Model inference time
• Data freshness
• Enrollment rate

Diagnostics answer two essential questions:

1. Is the feature functioning the way we intended?
2. If the outcome didn’t move, do we know why?

Without diagnostics, teams end up in circular debates:

“It didn’t work.” “The experiment was wrong.” “No, the data was wrong.” “No, the KPI was wrong.” We have seen these play out in so many different forums.

Let’s run the OLED model through a couple of different examples.

Reducing Email Send Latency in the Activation Layer

Primary Outcome:

Increase revenue from triggered lifecycle campaigns (e.g., abandoned cart) by reducing the delay between user action and email send.

Secondary Outcomes:

• Increase conversion rate of triggered emails
• Improve customer experience
• Reduce engineering maintenance overhead
• Improve deliverability (fewer stale events)
• Increase experiment velocity for lifecycle flows

Levers

These are the system levers that actually move the outcome:

1. Event Freshness

How quickly events (cart_add, checkout_start) reach the activation system.

2. Trigger Latency

Time between event ingestion → trigger evaluation → ESP send.

3. Queue Throughput

Kafka/Kinesis/SQS queue depth and processing speed.

4. ESP API Performance

Braze, Iterable, SFMC all have rate limits and throttling.

5. Data Model Completeness

Missing metadata (SKU, price, discount) reduces email relevance.

6. Retry & Error Handling

Dropped events = lost revenue.

E = Experiments

How we validate the impact:

1. Latency Bucket Testing

Group users by latency buckets:

• <1 mins
• 1–5 minutes
• 5–15 minutes
• 15–30 minutes

Measure conversion rate by bucket.

This is how companies like Amazon, Walmart, and Shopify optimize lifecycle triggers.

2. A/B Test: Batch vs. Streaming

• Group A: Batch sends every 30 minutes
• Group B: Real‑time streaming triggers

Measure:

• conversion
• revenue per email
• unsubscribe rate
• deliverability

3. Before/After Analysis

Compare:

• average latency
• revenue per trigger
• event drop rate
• ESP throttling events

4. Shadow Mode

Run new pipeline in parallel to validate:

• event counts
• trigger accuracy
• metadata completeness

Diagnostics

These are the signals that tell you why latency improved or didn’t.

1. End‑to‑End Latency Breakdown

• event → warehouse
• warehouse → CDP
• CDP → ESP
• ESP → inbox

2. Queue Depth & Lag

If Kafka lag > 5 minutes, you have a bottleneck.

3. ESP Throttling

Braze and Iterable both expose throttling metrics.

4. Error Rate

• dropped events
• malformed payloads
• schema mismatches

5. Trigger Enrollment Rate

If fewer users enter the flow, something broke.

6. Deliverability Metrics

• bounce rate
• spam complaints
• inbox placement

Introducing a New Self-Serve Audience Builder for Marketers

Primary Outcome:

Increase campaign velocity and reduce dependency on engineering by enabling marketers to build, test, and activate audiences without SQL or ticket queues.

Secondary Outcomes:

• Increase number of experiments per quarter
• Improve segmentation accuracy
• Reduce time‑to‑launch for campaigns
• Increase pipeline influenced by targeted audiences
• Reduce data engineering backlog by 20–40%

Levers

1. Data Accessibility

Marketers can access unified customer data (CRM + product + marketing events) without engineering tickets.

2. Query Generation Accuracy

LLM‑powered or UI‑driven audience logic must translate into correct SQL or API calls.

3. Identity Resolution

Audience accuracy depends on the underlying identity graph.

4. Activation Speed

How fast audiences sync to channels (email, ads, push, SMS).

5. Governance & Compliance

Audience builder must enforce consent, suppression, and PII rules.

6. Experimentation Throughput

More audiences → more tests → more learnings.

Experiments

1. A/B Test: Self‑Serve vs. Engineer‑Built Audiences

Group A: Marketers use the new builder

Group B: Marketers submit tickets to engineering
Measure differences in:

• time‑to‑launch
• number of audiences created
• number of experiments run
• campaign performance

2. Shadow Mode Query Validation

Run LLM‑generated queries in parallel with human‑written SQL to compare:

• audience counts
• segment membership
• logic accuracy
• cost to run

3. Pre/Post Analysis

Compare:

• average campaign launch time
• number of active segments
• number of experiments per month
• engineering ticket volume

4. Channel‑Level Performance Tests

Test whether more granular audiences improve:

• open/click rates
• personalization experiments
• conversion rates
• CAC
• ROAS

Diagnostics

1. Query Error Rate

% of audience definitions that fail due to schema issues, missing fields, or invalid logic.

2. Query Execution Time

Slow queries, using up too much bandwidth, leading to slow activation

3. Query Cost

How poorly written, leading to cost to run over an extended period of time.

4. Audience Size Drift

Unexpected spikes or drops indicate logic issues.

5. Adoption Metrics

of marketers using the tool

of audiences created

of audiences activated

of channels connected

6. Data Freshness

If warehouse → CDP sync is delayed, audiences are stale.

7. Governance Violations

• PII misuse
• consent violations
• missing suppression logic

Why OLED Works

O.L.E.D. works because it aligns three worlds that rarely speak the same language: Engineers, Product Managers and Marketing/Business Leaders. It turns feature delivery into hypothesis‑driven engineering, not ticket‑driven engineering. You create a culture where teams don’t just ship — they learn, iterate, and improve — together.

And in a world where AI is accelerating everything — good systems and bad systems alike — clarity of outcomes is no longer optional.

The Future Belongs to Outcome‑Oriented Teams

The next era of MarTech engineering won’t be defined by who ships the most features. It will be defined by who ships the most impact.

Teams that adopt OLED or a similar version of it, will:

• Build less, but achieve more
• Move faster, with fewer mistakes
• Align engineering, product, and marketing
• Deliver outcomes, not artifacts

A feature is only as valuable as the outcome it creates.

[All opinions are my own and have no relation with my employers — past or present. In a rapidly growing Agentic world, I write about the theme of accountability across different systems — humans or technology. I use https://huffl.ai to structure my thoughts]