Knowledge & Insights
MarTech claims: separating signal from noise
MarTech businesses often do genuinely difficult work, identity resolution, attribution, cross-channel orchestration, experimentation, data quality and performance at scale, but claims can get diluted when the write-up focuses on the outputs (dashboards, pipelines, “AI features”) rather than the underlying technological uncertainty.
This guide helps you isolate the signal (qualifying R&D) from the noise (routine build, implementation, and commercial iteration), and capture a claim that reads clearly to a reviewer who doesn’t live in ad-tech and marketing ops every day.
1) Why MarTech claims go wrong
MarTech work is frequently cross-functional: product, data, engineering, customer success, and implementation teams collaborate to ship improvements quickly. That velocity is great for growth, but it can blur the line between: resolving technological uncertainty (potentially qualifying) and building, configuring, or optimising known approaches (typically non-qualifying).
A simple rule of thumb
If the work could reasonably be delivered by applying standard practices with predictable outcomes, it’s unlikely to be R&D. If the team had to experiment, iterate and prove a solution because success was not readily deducible, that’s where the signal is.
Another frequent issue is that claims over-index on “AI” and “data” language. Those terms don’t make work qualifying. What matters is the underlying uncertainty: what specifically was hard, why was it hard, and what did you do to resolve it?
2) What “signal” looks like in MarTech
In a strong MarTech claim, the “signal” is a small set of workstreams where the team pushed beyond routine delivery. The narrative is anchored in technological advance (even if incremental), framed through: uncertainty → hypothesis → experiment → result → learning.
Uncertainty
A genuine technical unknown: performance, accuracy, scalability, reliability, or feasibility.
Systematic work
Evidence of iteration: benchmarks, ablation tests, incident learnings, re-architecture.
Advance
A capability improvement that wasn’t readily deducible at the outset (not just a feature launch).
3) The usual “noise” categories (non-qualifying)
These workstreams are often essential to a MarTech product, but they’re generally not R&D unless they involve a real technical uncertainty and systematic experimentation.
- Standard integrations: connecting to common CRMs, ad platforms or CDPs using known APIs and patterns.
- Customer-specific configuration: rules, mappings, dashboard set-up, standard ETL, routine schema changes.
- Feature assembly: stitching existing libraries/managed services without non-obvious technical challenge.
- UI/UX iteration: front-end changes driven by user feedback unless tied to a deeper technical uncertainty.
- Routine QA & release work: regression tests, deployments, standard monitoring, normal bug fixing.
- Commercial optimisation: pricing experiments, packaging, go-to-market, sales enablement, marketing ops.
This isn’t about “minimising” your work, it’s about isolating the R&D core so the claim is credible, focused, and easy to follow.
4) High-signal MarTech themes that often qualify
Below are common MarTech areas where we see genuine uncertainty arise. Whether they qualify depends on how you approached them, i.e., whether the team had to resolve a non-obvious technical challenge.
Identity resolution & stitching
Cross-device / cross-session identity graphs; probabilistic matching; privacy constraints; reconciliation strategies; accuracy vs latency trade-offs.
Attribution & incrementality
Robust causal inference under noisy data; model drift; partial observability (cookie loss); evaluation frameworks and bias correction.
Real-time decisioning
Low-latency orchestration; feature stores; SLAs under spike traffic; consistency models; failover strategies.
Data quality & anomaly detection
Detecting schema drift, missingness, and semantic breakage; automated guardrails; explainable alerts and remediation.
Measurement under privacy constraints
Aggregation, differential privacy, clean-room style approaches; ensuring useful signal with constrained granularity.
Scalability and cost-performance engineering
Query performance at scale; storage design; vector search trade-offs; streaming vs batch architecture; cost controls without quality loss.
How to write these up effectively
Replace “we built X” with “we couldn’t reliably achieve Y under conditions Z, so we tested approaches A/B/C and measured outcomes.”
5) Evidence that reads well for MarTech
MarTech teams often already generate strong evidence, it just isn’t labelled as “R&D evidence”. The key is to choose artefacts that support both the uncertainty and the systematic work.
Benchmarks & evaluation
Latency/throughput benchmarks, offline metrics, online experimentation outcomes, drift analysis.
Architecture & decision records
ADR documents, trade-off notes, migration plans, incident post-mortems, rollback learnings.
Delivery trail
Sprint goals tied to uncertainty resolution, PR threads, test plans, experiment logs, tickets.
Evidence should support your story, not replace it. A handful of strong artefacts beats a large dump of generic documentation.
6) Methodology: apportionments that make sense
For MarTech businesses, the hardest part is often separating product R&D from “platform delivery” and customer implementation. A good methodology usually combines: role-based allocation (who did what) with a project/workstream allocation (where uncertainty lived).
What tends to be defensible
- Role mapping: engineering/data roles mapped to qualifying workstreams; implementation/CS separated unless they contributed to uncertainty resolution.
- Workstream apportionment: a reasoned percentage split based on delivery plans, sprint themes, or technical OKRs.
- Category-specific logic: e.g., cloud/data costs allocated based on the workloads that served qualifying experiments.
Avoid
“One percentage across everything” without explanation. If you do use a global allocation for certain costs (e.g., tools), the rationale should be explicit and consistent with how the business actually operates.
7) AIF and project framing: clarity over volume
MarTech product backlogs can include hundreds of tickets, improvements and experiments. Your claim doesn’t need to mirror that granularity. Instead, group work into a small set of stable projects/workstreams where uncertainty was addressed.
Good project framing
“Identity graph accuracy under privacy constraints” (clear uncertainty + objective).
Weak framing
“Platform enhancements” (too broad; uncertainty not obvious).
AIF alignment
Project list and cost totals should tie cleanly back to the master schedule and report.
8) A practical checklist
- Can we clearly describe the technical uncertainty in plain English for each workstream?
- Do we show a systematic approach (tests/iterations/benchmarks), not just delivery?
- Have we separated implementation/configuration from product R&D unless it truly addressed uncertainty?
- Do apportionments reflect the business reality and have a clear rationale?
- Does the claim pack read consistently across AIF → report → schedules → computation?
Want a MarTech-specific sense check?
We can review your projects and methodology, identify where the signal is strongest, and help you present a clear, defensible narrative aligned to how MarTech teams actually build.
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