What Competitor Data Is Public on Each Channel

This has a consequence most reports never state. Your own engagement rate, taken from your own analytics, is probably reach-based or impression-based, because that is what the platform shows you. Your competitor rate is necessarily follower-based. Putting the two side by side compares two different formulas and calls the difference performance.

The fix is not to find better competitor data, because it does not exist. The fix is to compute your own rate the same way you compute theirs, using followers, purely for comparison, and keep your reach-based rate for internal work.

Meta lists every active ad with creative, format, placements and start date. Google lists ads for verified advertisers across Search, Display and YouTube, each with a date range. Both are open to anyone.

Spend and targeting are published only for political and social issue advertising, plus additional reach disclosure for ads served in the EU under the Digital Services Act. For commercial advertising you get what is running and for how long, and nothing about cost.

Inactive commercial ads generally disappear, which makes ad libraries a live snapshot rather than an archive. That is the strongest argument for recording ad state on a schedule instead of investigating after the fact: the history you did not capture is gone.

Sitemaps sometimes list pages before they appear in the navigation, which is the closest thing to advance notice a public source offers. A new pricing page or a new solution page in the sitemap is a plan, not a guess.

What websites hide: traffic, conversion rates, test variants you did not happen to be served, and anything behind a login. Traffic estimates from third-party tools are modelled from panels and clickstream samples, and their error is largest exactly where it matters most, on smaller sites.

Read the two-star and three-star reviews rather than the extremes. One-star reviews are usually about a single support incident, five-star reviews are often solicited at the moment of purchase, and the middle of the distribution is where people explain what almost worked.

Review velocity is the underused number here. A sudden increase in review count usually means a solicitation campaign rather than a surge of spontaneous enthusiasm, and knowing which competitors run one changes how you read their rating entirely.

Job requirements are unusually candid because they are written by the people doing the work, not by marketing. A listing that names a specific platform, market or compliance regime tells you what is being built before any announcement does. Open role counts by function over time are a reliable investment signal.

Mention data is different, because incompleteness is structural. Private communities, dark social and closed messaging carry a large share of real conversation and are invisible by design. This is worth stating whenever you report mention volume or share of voice: the number describes the sources you can see, and pretending otherwise turns a defensible figure into an indefensible claim.

A competitive programme that covers only the first two still beats most programmes that try to cover everything, because the first two produce decisions and the rest produce activity.

That single habit removes most of the credibility risk in competitive reporting. The failure mode is never a wrong number in isolation, it is a wrong number that nobody remembers was modelled, resurfacing three months later as an input to a plan.

The same discipline explains why two tools report different figures for the same competitor. Usually one of them is estimating something private, or the two define a public metric differently. Before treating a discrepancy as an error, check whether the number was observable at all.

Oppira collects across these nine public channels daily for the competitors you track, and deliberately does not publish figures the sources cannot support, which keeps the reports arguable rather than merely impressive.