Shopify Niche Research: Uncovering Under-Served App Opportunities

I developed a method for identifying potential niches for new Shopify apps using a combination of web-scraping, TF-IDF text analysis, and historical view counts. This data-driven approach proves significantly faster than trying to identify niches by manually searching the Shopify Community forum.

Collecting the Data

To begin, I developed two web-scrapers to collect data from the Shopify Community forum: one to collect post metrics, including view counts and replies, and another to collect the complete chain of messages in each post. To view changes in view counts over time for every post, I ran the scraper for collecting metrics on a daily basis. (Running it weekly is probably sufficient, but I was curious if there were any patterns in when merchants got onto the forum.)

Since the scraper for collecting the full text for every post was more resource intensive and changed less often, I did one initial run to gather the text for every post then watched for changes in the replies metric to determine if I should re-scrape the post.

To more efficiently work with the data, I parsed it and stored all the data in a Postgres database on my laptop.

Analyzing the Text

While manually reviewing posts, I observed niches with successful apps included 2–3 words or phrases that are highly specific to those niches. Words like ‘consignment’ only appeared in a few posts and those posts had a very specific engaged audience supporting an ecosystem of several apps.

To find these special words more effectively, I used term frequency inverse document frequency (TF-IDF) as a measure of ‘specialness’ for all the words in the entire Shopify Community Forum.

Before calculating TF-IDF, I normalized the words by setting them to lowercase and lemmatizing them. For calculating term frequency, I decided to treat the entire post, with all of its messages as a single document, because I observed most people would only use a given niche word once or twice in their individual post but it usually appeared many times in conversations surrounding a focused niche.

Treating an entire post as a document rather than a single message helped boost the signal of these niche words. Since TF-IDF produced a value for each term in a given document and I was interested in the term across all documents, I calculated the min, max, and average TF-IDF for a given lemma across all posts in the forum.

Analyzing the View Data

To complement the TF-IDF values, I used historical snapshots of post views over the prior 30 days. The total number of views a post receives over this rolling period serves as a good proxy for the size of a market or niche, with some caveats.

I implemented several normalization steps to ensure meaningful results. I noticed posts consistently receive high view counts in the first 1–3 days after posting before rapidly declining, so I discarded this initial period to eliminate noise from this predictable spike pattern. This allowed me to focus on the sustained interest in topics rather than the initial attention burst.

I also examined the distribution of view patterns over time and identified two distinct scenarios that were relevant in interpreting view counts. First, posts about critical platform-wide issues (like major bugs affecting most shops) generated enormous initial view counts but quickly disappeared once Shopify resolved them. Second, posts about fundamental platform limitations showed consistently high view counts over extended periods but typically represented problems Shopify would eventually address directly.

To identify truly promising niches, I normalized the data to focus on posts with moderate but sustained view counts over the 30-day window. These posts frequently contained specific, unusual terminology (words like “gold,” “bookstore,” or “hospital”) and correlated strongly with successful niche-specific app launches. This approach filtered out both extremely high-traffic general issues and low-traffic obscure problems, leaving the moderate-traffic specialized needs that represent viable market opportunities.

Putting it All Together

The full list of lemmas was massive, so I filtered down the results to lemmas with moderate TF-IDF and view count metrics, avoiding terms that were too niche or with view counts that were too high or too low. This left me with a manageable list of terms small enough to review manually. As light validation for my approach, I confirmed terms associated with existing, successful niche apps identified earlier appeared in my shortened list.

I manually reviewed the list for standout lemmas, paying careful attention to terms that suggested a unique business, like ‘isbn’ or ‘barber’, and developed a simple tool to retrieve all posts containing these lemmas. This allowed me to quickly spot-check their potential. Using this approach, I rapidly uncovered several promising niches — significantly faster than manual forum searching.

This method of identifying Shopify app niches could be further improved with the following techniques:

1. Analyzing n-grams rather than just individual lemmas to identify niches that described their problems or ideal solutions with multiple words or short phrases.
2. Using embeddings on all posts to speed up the review process. For example, niches that had gone unserved for extended periods often included follow-up comments months or years later asking if solutions had been found. Writing queries for all possible permutations of people seeking similar solutions was tedious and ineffective. Embedding and semantic search would offer a huge increase in speed and accuracy when isolating these types of posts.
3. Reducing noise in the TF-IDF approach through embeddings. When filtering posts with interesting lemmas, I often found some with high-quality discussion around specific problems, while others merely mentioned the lemma in contexts tangential or completely unrelated to the main discussion. Filtering out these low-signal posts was a tedious manual process that could be significantly improved using embeddings.

If you find this approach useful or have questions, I’d love to hear from you!