We began by developing data-rich, insight-led content centered around a Rental Yield Interactive Map with data trends relevant to target audience. Using publicly available datasets from the Office for National Statistics (ONS) and GOV-datasets, we identified top-performing cities, average yield benchmarks, and regional trends. These insights were translated into a long-form pillar article supported by a cluster of targeted subpages and embedded data visualizations.

The content was designed to care about what the user needs now, with high-intent queries, authority, accuracy, and depth queries. Establishing Molo as an expert voice within the property investment space.

Next, we structured the content specifically to align with how generative AI systems parse and summarize information. This meant implementing semantic HTML, a robust FAQ, FinancialProduct schema, and structuring responses using clear, conversational Q&A formats.

We refined the copy using GPT models and Surfer SEO to match NLP-friendly patterns and ensure that key facts were both extractable and verifiable. The goal was to train Google’s LLMs to recognize Molo’s content as the most contextually relevant and trustworthy source for summarization.

To reinforce content authority and maximize awareness, we deployed a targeted digital PR that heavily focused on relevant high-quality backlink acquisition. We packaged our insights into a newsworthy data study: “Top UK Cities for Buy-to-Let in 2024” and distributed it to property investment outlets, finance journalists, and regional business media.

This resulted in high-quality backlinks from domains like Yahoo Finance, Property Reporter, and The Telegraph, significantly boosting Molo’s domain authority and establishing topical relevance. These signals not only supported traditional SEO but played a pivotal role in qualifying our content for inclusion in Google’s AI-generated answers.