Programmatic SEO for SaaS: A Real Case Study

Programmatic SEO is one of the most underused growth levers in B2B SaaS. The concept is straightforward: instead of publishing one article at a time, you build a system that generates hundreds or thousands of optimised pages at scale using structured data. The results, when the architecture is right, are exceptional.

The case study we'll walk through involves a B2B HR SaaS platform that was spending £28K/month on Google Ads and had plateaued at 3,200 monthly organic sessions for 18 months. Their team had tried blog content, but the editorial cadence was too slow to generate meaningful SEO volume.

The first step was keyword universe mapping. We identified 4,800 high-intent keywords across three page types: comparison pages ("Workday vs [Client Product]"), integration pages ("[Client Product] + Salesforce integration"), and use-case pages ("HR software for construction companies"). Each cluster had different intent signals and required different page architecture.

The second step was building the data layer. Comparison pages required structured competitor data. Integration pages required feature documentation per integration. Use-case pages required industry-specific pain point mapping. We built a content schema that could feed all three page types from a single data model.

The third step was technical implementation. The dev team built Next.js dynamic routes that rendered each page type from the data layer. No editorial involvement was needed per page — only per template. The total build time was 10 weeks.

Results: organic sessions grew from 3,200 to 9,800 monthly in 90 days. Organic-sourced trials increased 140%. Paid spend was reduced by 30%. The key insight is that programmatic SEO is not a content strategy — it is an engineering strategy with content as the output.