Outset Media Index (OMI) is a new media intelligence platform designed to bring clarity to decisions around where and when to place content, and what those choices are likely to lead to.
Those decisions tend to blur into each other. Where something runs affects when it can go out, and both shape what the budget actually delivers in the end. The challenge is that they’re usually handled separately – figured out at different stages, often with different inputs.
OMI brings together data on traffic, SEO strength, audience engagement, content distribution, and operational factors into one standardized view. It covers more than 340 crypto-native, fintech, finance, and general news outlets that regularly report on cryptocurrencies.
Within the index, media and marketing people, researchers, publishers and everyone working with content can analyze publications using 37 signals and two summary scoring frameworks that reflect both how an outlet brings results and how it works in practice.
Built by a recognized crypto PR agency Outset PR and powered by its analytical infrastructure as well as data from providers like Similarweb and Moz, OMI applies consistent benchmarking across all outlets, which makes it easier to navigate differences that would otherwise be hard to spot.
These same signals also feed into Outset Data Pulse (ODP) that helps map how crypto coverage is distributed and evolves across different markets.
OMI indicates:
how consistently an outlet delivers results,
how audiences engage with content,
and how far that content continues to circulate after publication.
This is exactly where the index tends to come into the “where to publish” decision. Theoretically, a lot of media outlets can look interchangeable. Traffic is there, SEO looks strong, and everything suggests the placement should perform. However, once a story goes live, the outcomes start to separate – some carry on beyond the initial publish, while others don’t really move any further.
That’s where the uncertainty usually sits. It’s not always obvious upfront which way a placement will go, because those differences don’t show up clearly in surface metrics. What tends to matter instead is how stable the audience is, how readers engage with the content, and whether it extends beyond the initial post.
OMI breaks that down into metrics that could reflect what’s actually happening, some of which include:
Unique Score, which separates outlets with a consistently fresh audience from those driven by short bursts,
Reading Behavior, described to show how actively readers interact with content once they land,
And Reprints, used to track how far a story continues beyond the initial post.
Once it becomes clearer which media outlets tend to keep working after a placement is there and which do not, the focus usually shifts from understanding the details to actually making a call. At that point, going through each signal one by one becomes less practical.
OMI consolidates these signals together into two summary scores: General Score and Convenience Score. One reflects how consistently an outlet tends to deliver, while the other gives a sense of what it’s like to work with in practice, from turnaround time to editorial flexibility and pricing.
From there, attention typically narrows to a smaller set of options, and eventually to a single outlet. OMI reflects that through its Media Profile view, where all relevant signals are pulled together in one place.
OMI reveals how usable and decision-ready a media outlet is and also how it fits into planning, execution, and budget decisions.
Traffic and SEO tools are built to describe site-level activity: visits, rankings, session depth, and referral flows. This becomes more noticeable when trying to understand how an outlet actually functions within a campaign.
There is also a limitation in how this data is sourced. Third-party estimates, even from providers like Similarweb, don’t always match first-party analytics, and while relative rankings may align, absolute values can vary depending on methodology and timing.
More importantly, standard tools don’t account for how an outlet operates as a working partner. They don’t reflect how flexible editorial processes are, how long content typically takes to get published, or how pricing aligns with actual reach. A placement can look decent at the planning stage, but once the process starts, timelines start changing, content gets reworked, or requirements turn out to be stricter than expected. Most of the time, these risks are managed informally, based on past experience with specific outlets.
OMI turns that into something more concrete by breaking those factors into measurable signals:
Editorial Rigidity shows how flexible the content submission process tends to be,
Turnaround Time reflects how long content usually takes to go live,
and Price Score puts cost in relation to actual reach.
Taken together, these make it easier to anticipate how a placement is likely to play out before committing to it.
The same signals also tend to shape when a placement actually makes sense. Timing decisions come down to constraints: how quickly something needs to see the daylight and how much control there is over the final version. Teams usually handle this based on experience with different outlets. By making those patterns visible through Turnaround Time and Editorial Rigidity, OMI helps bring more structure to decisions that are often made on instinct.
That’s the point where OMI helps with shaping the “when” – in terms of when a placement makes sense within a rollout, rather than just how it performs on its own.
Teams use OMI to make trade-offs between reach, reliability, cost, and execution visible before a decision is made, and then narrowing down options directly within the platform.
Poor picks rarely come from a single factor being off. It usually comes down to trade-offs such as:
Reach vs consistency
Speed vs flexibility
Price vs actual return
An outlet might offer strong visibility but come with slow turnaround or rigid editorial constraints. Another might be easy to work with but struggle to deliver consistent exposure. These are the kinds of compromises that tend to shape how a campaign actually plays out.
OMI brings those factors into one view, and from there, the list becomes easier to narrow down based on what the campaign actually needs.
Inside the OMI interface, teams can filter outlets by combining conditions:
traffic ranges,
reading behavior,
reprints,
domain authority,
GEO,
pricing,
turnaround time,
and so on, depending on what matters for that specific campaign.
OMI helps teams allocate media budgets more efficiently by allowing them to analyze how an outlet’s cost translates into actual value.
Budget decisions usually come down to where spend actually holds up. A higher-priced outlet can bring scale, but that doesn’t always translate into lasting impact. Meanwhile, a smaller outlet can hold up better if the attention it brings is steadier or continues to circulate.
There’s also a practical side to how budgets get used. Turnaround Time, Editorial Rigidity, and coverage formats influence how quickly spend turns into live coverage and how much effort is required to get there. In many cases, these factors end up shaping efficiency just as much as reach itself.
By structuring all of this within a consistent dataset, OMI makes it clearer to analyze budget allocation across outlets by focusing instead on where spend is more likely to translate into durable visibility and smoother execution.
Where to publish, when to run, and how to spend tend to get figured out separately, often using different tools and a mix of experience and guesswork. Even when they’re connected, they’re rarely looked at that way in real time. OMI brings those pieces into the same view, so media people are not constantly stitching them together.
The index doesn’t make the decisions for its users, but it does make it easier to see how they fit.
OMI entered a soft launch in March. The leadership behind it sees the current version as a starting point rather than a finished product. As founder Mike Ermolaev puts it, this is “the foundation for a larger ecosystem of products focused on how teams work with media outlets.”
Product manager Sofia Belotskaia points to the more practical side of the next phase. As she explains, the focus now is on “more convenient benchmarking,” better historical data visualization, more accurate categorization and filtering, and expanding the dataset itself. In simple terms, that means making OMI easier to compare side by side, easier to read over time, and more useful when teams need to move from research to an actual decision.
Taken together, those two perspectives make the direction of OMI much clearer. The soft launch version already helps teams analyze outlets more systematically, but the roadmap suggests something even bigger: a platform designed to make media planning even more structured, more transparent, and less dependent on guesswork.