Guide
Competitor analysis that ends in a ranked list of fixes
Competitor analysis is useful when it changes what you do next week. The job is to see what a small set of rivals are doing better in public, then turn that into a short list of fixes you can actually ship.
Pick a short list, not a category
Start with the companies a buyer would compare you with. One to five is enough. A long list dilutes the comparison and makes it harder to see which gap is yours to close. For each rival, write down the website domain and, if they have apps, the App Store and Google Play listings. If a listing does not exist, leave it blank and treat that store as unknown rather than as a zero.
Compare the same public surfaces
A useful pass looks at the places a customer already checks before they talk to sales:
- App stores: rating, how many ratings sit behind it, the star histogram, category, the words in the title and subtitle, and how recently the listing was updated.
- Site and content: how large the public URL inventory is, which page types dominate it, whether a blog is actually publishing, and whether key templates have a title, an H1, a meta description, and schema.org markup.
- Search and links: estimated organic visibility, ranked keywords, and referring domains. These are provider estimates. Label them as estimates, and leave them blank when the provider is not connected.
- Ads: the public ad libraries. Open the Meta Ad Library and Google Ads Transparency Center for each domain. Counting active ads by scraping those libraries is a separate, brittle project, so a link is the honest artifact.
App listings deserve their own pass. The app store competitor analysis guide covers ratings, histograms, and review samples.
Read gaps only where both sides were measured
A rating of 3.2 against 4.8 is a gap when both numbers came from the same kind of source. A missing sitemap is not “zero pages,” and a search tool you have not connected is not “no traffic.” Keep the labels distinct: a collected value, unknown, unavailable, and not connected. Speed scores, prices, follower counts, and posts per month follow the same rule. That discipline is what keeps the later fix list from chasing a number nobody measured.
When both sides have a figure, ask a plain question: is theirs meaningfully ahead, and is the difference something a marketing or product change can move? Examples that usually are:
- A store rating that trails by a few tenths, with a histogram that shows a heavy 1-star share.
- A content inventory that is an order of magnitude smaller, or a blog with no dated posts in the current year.
- A homepage that is missing an H1 or schema types the rival’s commercial pages already expose, such as Product and AggregateRating.
- Emails sitting in public profile URLs. That is a collected hygiene issue, not a traffic estimate.
Turn the gaps into fixes with effort attached
A gap is not yet a plan. For each one, write the action, why the collected evidence supports it, and whether the effort is low, medium, or high. Reply to recent 1-star reviews, add the missing H1, publish the page type you do not have, and refresh a stale screenshot set are different jobs. Ranking them by impact relative to effort is the whole point of the exercise.
An AI pass can draft that ranking, and it should be allowed to use only the evidence you already collected. If the model cannot see a source id for a figure, it should not invent one. The gap list itself does not need that step. It can be computed directly from the figures.
How Bizzik runs this
Bizzik is that workflow as a report. You enter your company and up to five competitors. A background job collects app store listings, robots.txt, sitemaps, a small HTML sample, PageSpeed scores, a pricing page when one is linked, social profiles linked from the homepage, and tech signatures in that HTML. It records search metrics only when DataForSEO credentials exist. The dashboard puts the companies side by side, lists the gaps, summarizes review themes, and ranks quick wins with the evidence attached. The saved Duolingo and Babbel sample is a real collection, not a mock.
Create an account to run it on your own domain, or read the crawler policy if you want to see what the site module fetches.