Trustline Digital
Quantitative Digital Marketing · Internal product · Ongoing · Product architect and quantitative leadA marketing operating system that carries evidence into allocation
InsightThe problem was not another dashboard. It was carrying evidence from semantics, search, seasonality and campaign signals into one auditable media plan.
Try it
Follow the evidence
Open an engine to see how a different kind of evidence becomes an operational output, then change confidence to rebalance the illustrative plan.
Decision engines
Five questions. Five different models.
Choose an engine, then move its assumption. The graph, result and diagnostics rebuild around the question being asked.
Raise the gate to separate tighter, more explainable intent clusters.
- Clusters
- 5
- Cohesion
- 84%
- Coverage
- 83%
- Ambiguous
- 10%
01
Context
Trustline Digital needed one client-first workspace for marketing operations, SEO execution, campaign setup, optimisation logs, planning and team accountability — with the analytical method embedded in the product rather than living in separate spreadsheets.
02
Problem
Marketing evidence arrived at different frequencies and in incompatible shapes: raw search terms, seasonal time series, technical SEO checks, paid-media exports, expert recommendations and channel assumptions. A conventional dashboard could display all of it without helping anyone decide what to do next.
03
Insight
Analysis becomes operational only when its output is handed to the next decision in the same system. A semantic cluster should become campaign structure; a seasonal pattern should alter timing; an anomaly should create a review; and a forecast should change allocation while preserving the baseline it replaced.
04
Strategy
Design a set of explainable engines around one client and access model, then connect them to the everyday records — tasks, calendars, setup checklists, optimisation logs and audit history — that make analytical decisions repeatable.
05
System
The analytical suite links semantic clustering, seasonality and volatility analysis, SEO prioritisation, campaign-signal review, media planning, optimisation review and channel-viability assessment. Each module exposes the evidence needed for the next decision without publishing the internal product architecture behind it.
The planning layer converts channel assumptions into allocations and forecasts, optimises for volume or efficiency, preserves a baseline for comparison and runs 500 Monte Carlo simulations that vary CPC and conversion rate by ±15%.
The operational layer is client-scoped and SharePoint-backed through Entra, Azure Functions, Managed Identity and Microsoft Graph. Business data does not persist in the browser; role, client membership and audit history travel with the work.
06
Outcome
A working marketing operating system in which research, planning, execution and review share one client context. It makes the analysis usable by a team: not a private model, but a chain of decisions with visible inputs and recorded consequences.