38 recipes, seven conditions, and the seven collection pages we did not build
Munchable has a hand-written recipe library: 38 recipes, each checked against seven gut conditions by the same rules engine that runs on a barcode in the app. Every recipe is a public page. Every page ranks for its own long tail. The obvious next move, and the one every programmatic SEO guide would tell you to make, is seven collection pages: low FODMAP recipes, reflux recipes, lactose free…
Munchable maintains a recipe library containing 38 recipes, which undergo a stringent seven-condition assessment using the same rules engine found within the app. Each recipe is a standalone public page, all ranked individually for their long tail keywords. The logical progression would be to create seven collection pages focusing on low FODMAP, reflux, lactose-free diets, and similar dietary restrictions. However, the team refrained from doing so, focusing instead on the distribution of the data.
A closer examination of the seven potential lists revealed that 37 out of 38 recipes aligned with the low FODMAP condition, while 30 matched reflux. The thinnest condition still had 30 matches. These figures indicate that the seven collection pages would primarily contain overlapping content, with most pages sharing over 80 percent of their items across siblings. Google's search algorithms are adept at identifying such near-duplicates, which would result in a single collection page instead of seven separate ranking pages.
Before constructing any collection page, the team employed a test to assess whether the lists diverged sufficiently to provide readers with unique content. In this case, the 37 out of 38 recipes clearing the low FODMAP condition did not warrant the creation of multiple pages. The team's strategy evolved accordingly, focusing on grouping recipes by meal (breakfast, lunch, dinner, and snack) rather than condiments.
The recipe index is structured by meal type, ensuring that breakfast and dinner lists are genuinely unique. Condition-related linking still occurs, but this time through individual guides, rather than thin hubs. Each recipe page contains a "Who this suits" block, generated using a function that evaluates recipe compatibility with specific conditions.
The engine converts recipes into a format identical to scanned products, ensuring consistent processing. The "suits" function determines if a recipe aligns with a given condition, using a "fitCheck" method that applies a high lactose sensitivity setting. The verdict provided by this function is consistent across both the web and mobile app environments, preventing confusion or discrepancies.
A notable aspect of the system is the generation of meta descriptions, which are derived from the content displayed on each page. Handwritten meta descriptions were not necessary due to the automated generation process. This approach ensures consistency and accuracy in presenting the information to both web visitors and app users, each with distinct contexts and needs.
Written by urgent.news from Dev.to's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.