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SDC Verifier · Engineering simulation software (CAE)

Content, lifecycle, and technical product marketing

Building an organic growth and demand system for software sold to structural engineers, where every claim has to survive technical review.

SEOContent opsLifecycleProduct marketingAnalytics
Category
Engineering / CAE structural analysis & code checking
Role
Senior Content Marketing Manager, Jan 2024 – present
Audience
Technical; long, technical evaluation cycles
Scope
Content, SEO, lifecycle, product marketing, analytics

Measured outcomes

YoY organic search sessions
+134%YoY organic search sessions2024 vs. 2023 · GA4
YoY organic search sessions
+38.6%YoY organic search sessions2025 vs. 2024 · GA4
Lead-to-qualified conversion rate
18.9%Lead-to-qualified conversion rate2025 · CRM
YoY trial growth
+137.8%YoY trial growth2025 vs. 2024 · Product data
Email subscriber growth
+137%Email subscriber growthProgram to date · Email platform

Program-level figures across a multi-year effort. They are not attributable to any single intervention, and no revenue or pipeline-value claim is made here.

Starting context

SDC Verifier is structural analysis and code-checking software used by engineers working with FEA models in Ansys, Femap, and Simcenter 3D / Simcenter Nastran. The audience is technical, and evaluation cycles are long and standards-driven.

My role since January 2024 covers content strategy and operations, SEO and search reporting, topic architecture and internal linking, lifecycle and email, technical product marketing and GTM, analytics and reporting, and working across technical, product, and sales contexts.

The design problem

In a category like this the marketing problem is not "how do we publish more" — it is how to make a technical library legible to engineers who frame problems by analysis type, by standard, and by the CAE environment they work in.

The second design problem is measurement. When evaluation runs for months, short-horizon reporting systematically under-values depth. Any reporting model has to be agreed before it is used to prioritize, otherwise the prioritization argument inherits the model's bias.

The principles behind the approach

Structure content around engineering workflows and standards rather than raw keyword volume, because a smaller query with a live project behind it is worth more than a large one without.

Treat technical review as part of the critical path. In this category a wrong assumption about a standard costs more credibility than a missing article gains.

Segment lifecycle communication before increasing frequency, so each send can say something specific to a role and a product interest.

Define the reporting model around the length of the cycle, and review it on a fixed cadence.

What was built

Content architecture mapped to analysis types, standards, and host CAE environments, with internal linking rules that keep new pages connected to their parents rather than stranded.

An editorial operating system: intake, SME interview, brief, draft, technical review, publish, and a scheduled refresh queue driven by search and engagement signals.

A lifecycle program segmented by role and product interest, covering subscription, trial activation, evaluation support, and release communication.

Technical product marketing that positioned against real evaluation criteria — workload, standard, host environment — instead of documenting features, with comparison and enablement material for both the buyer and the sales conversation.

A reporting layer combining GA4, Google Search Console, and CRM stages, reviewed on a fixed cadence rather than ad hoc.

Decision log

The approach taken, why it fits this context, and the trade-off it carries. These describe design choices, not a claimed before-and-after.

What changed, and what did not

What changedWhat did not
Content is organized by engineering workflow, standard, and host environment.The product, its pricing, and the size of the sales team.
Reporting is defined around the length of the buying cycle and reviewed on a fixed cadence.The underlying CRM and analytics tooling — no platform migration was part of this scope.
Email is segmented, with trial, evaluation, and release paths.The trial mechanics themselves, which remain a product decision.

Result

Reported at program level, with sources named in the strip above.

  • +134%YoY organic search sessions— 2024 vs. 2023, GA4
  • +38.6%YoY organic search sessions— 2025 vs. 2024, GA4
  • 18.9%Lead-to-qualified conversion rate— 2025, CRM
  • +137.8%YoY trial growth— 2025 vs. 2024, Product data
  • +137%Email subscriber growth— Program to date, Email platform

What this does not claim

  • No revenue, pipeline-value, or closed-won figure is claimed here.
  • The figures are program-level across 2024–2025. They are not attributed to any single intervention, and no causal claim is made for one workstream.
  • No artifacts are published on this site yet. The slots above stay visibly empty until real assets are added.

Artifacts

Each slot states what it demonstrates. Slots without a published asset stay visibly empty.

Artifact not added yet

Content architecture

Artifact not added yet

Search reporting

Artifact not added yet

Lifecycle flow

Artifact not added yet

Product / GTM artifact

Implication

In technical markets, editorial credibility functions as distribution. Material that survives engineering review is read, cited, and returned to.

Measurement design has to be settled before content volume becomes the lever, otherwise the reporting model decides the plan.

The durable leverage is usually the intake and review process — the part that makes subject-matter expertise repeatable without depending on one person.

Have a similarly messy problem? Send me the context.