Onsocial

OnSocial might be one of the more literal names in influencer marketing software—because what it’s really selling isn’t “influencer marketing,” it’s social data.

Most platforms in this category start with campaigns: briefs, workflows, creator messaging, approvals, payments, and all the operational plumbing that turns a creator into a line item on a media plan. OnSocial comes from the other direction. It treats influencer marketing like a data problem first: who to pick, why they fit, what their audience actually looks like, what they’ve posted lately, what’s sponsored, what overlaps, and how to pull all of that into the systems a brand or agency already uses.

That mindset shows up everywhere in the product. The home experience is a toolkit: discovery, analytics, sponsored post tracking, audience overlap, and a raw API that’s meant to be “always on.”

In other words, OnSocial is built for the teams who are tired of being told “trust the dashboard.” It’s for the agency that needs to justify every creator choice to a client. It’s for the brand that wants audience proof before they ship product. And it’s for the platforms and internal data teams that don’t want a new suite—just high-precision inputs they can embed into their own stack.

Pricing

OnSocial’s public messaging on pricing is intentionally “enterprise-flexible,” because it supports multiple consumption models: dashboard usage, API usage, or both. The pricing page frames it as flexible based on how you use the platform and even emphasizes “pay only for successfully made request,” which is an uncommon positioning in this category.

The starting plan is $5,000 with 6-month validity, including web + API usage and access to all products. Plans can be customized month-to-month, and a demo/trial access is available.

In practice, that pricing posture makes sense given what OnSocial is selling. This is not priced like “a seat in a workflow tool.” It’s priced more like data infrastructure: something you plug in, run queries against, and build internal (or client-facing) value on top of.

Details and Features

OnSocial’s discovery experience is built for one thing: turning creator selection into a repeatable, defensible process that survives client questions, internal scrutiny, and performance volatility. The platform isn’t structured like an “influencer marketing suite” that assumes you’ll live inside it from brief to payment. It’s structured like a data instrument: you define the signal you’re looking for, you narrow until the noise disappears, and you walk away with a shortlist you can justify.

The starting point is scale. OnSocial’s database coverage is positioned as global across Instagram, TikTok, and YouTube, with creator profiles indexed at the “1,000+ followers” threshold. That matters because it tells you how the product thinks about the market: it’s built to surface creators at volume, then let you whittle down with precision. This is a platform that expects you to run dozens of searches, save multiple lists, and iterate quickly—more like audience research than talent scouting.

Discovery that starts with content signals, then proves the audience

Discovery is organized around inputs that map to how influencer marketing actually works day to day: creators show you who they are through what they post, who they interact with, and which brands keep showing up in their feed. OnSocial leans heavily into that reality by letting you search creators using hashtags, @mentions, keywords, and topic-level meaning extracted from recent content. The platform’s content parsing is framed around the last ~150 posts, which makes it useful for “right now” decisions—especially when you’re trying to avoid recruiting someone whose public output shifted six months ago.

The practical effect is that discovery becomes a two-step logic:

1. Find creators whose content is already aligned with what you want to run.
2. Validate that their audience matches the people you’re paying to reach.

That second step is where the platform starts to separate itself from simpler discovery tools. OnSocial’s search is not limited to creator-side metrics like follower count and engagement rate; it pushes you to filter by audience properties early—location, language, age and gender distributions, interest clusters, and credibility signals. If you’re selecting creators for performance, that’s the correct order of operations: audience fit first, creator popularity second.

From a workflow standpoint, you’ll usually start broad with a topic or keyword direction, then tighten with a set of constraints that look like real campaign requirements: geography, language, platform mix, minimum and maximum follower range, and a quality floor for engagement. After that, you move into filters that reduce risk: suspicious growth patterns, low-credibility audience segments, or creators whose engagement profile looks inflated relative to their size.

OnSocial also supports “contact availability” as a filtering dimension. For agencies, this is not a minor convenience; it’s a throughput lever. A shortlist that can’t be actioned turns discovery into wasted time. Being able to prioritize creators where contact data exists helps turn research into actual outreach velocity.

Then there’s the layer that most tools mention but few operationalize well: brand affinity and brand mentions. OnSocial frames discovery so you can angle your search around creators who already show interest, interaction, or proximity to certain brands or categories. That becomes especially useful when you’re planning conquest strategies or competitor intercepts. Instead of building a list off generic category keywords, you can build it around creators who have already demonstrated proximity to the competitive set.

A strong discovery workflow inside OnSocial tends to look like this:

• Start with a topic definition that matches the campaign’s creative angle (category + use case + aesthetic).
• Apply platform constraints (Instagram vs TikTok vs YouTube) based on how the brand actually converts.
• Apply audience constraints (geo, language, demographic composition) so the list is “eligible” before you even look at profiles.
• Apply performance constraints (engagement rate ranges, follower ranges) to avoid extremes—tiny accounts that won’t move the needle, and huge accounts that can be overpriced or saturated.
• Add brand affinity / interest constraints when you need creators who already live in the right cultural pocket.
• Save and export the result as a working set, then iterate into deeper analysis.

OnSocial is built to support that iteration. Exports matter here, and the platform explicitly supports list exports into formats agencies and data teams actually use (spreadsheets for outreach ops, JSON for internal tooling, PDF for clients and stakeholders). The “export-first” posture is a tell: the platform expects to be part of a wider workflow rather than trying to replace everything.

Creator analytics that behaves like due diligence

Discovery gets you candidates. Analytics is where you decide if they’re worth money.

OnSocial structures analytics around three pillars: influencer performance, audience composition, and post-level behavior. That last piece—post-level insight—matters because influencer marketing performance is rarely stable across all content types. A creator can have strong averages but weak consistency; a creator can spike on a format that won’t match your brief; a creator can have high engagement that comes from a niche that isn’t your buyer.

On the performance side, the platform is designed to expose a creator’s baseline in measurable terms. That includes the usual engagement staples, plus format-specific signals such as average plays/views on short-form video, plus interaction signals like comments, shares, and saves. The point isn’t to drown you in metrics; it’s to give you a fast sense of whether a creator’s “typical post” is likely to clear your performance threshold without relying on a single viral outlier.

The audience layer is where OnSocial leans into what most buyers actually fear: paying for reach that doesn’t exist, or reach that doesn’t matter.

The platform’s audience analysis is built to show demographics and geographics, and it also emphasizes credibility and reachability signals. Those two dimensions are the difference between “this creator has 200K followers” and “this creator can realistically deliver a certain amount of human, targetable exposure.” That distinction is especially important for agencies, because clients are increasingly allergic to big follower numbers that don’t translate into believable outcomes.

Audience insights in OnSocial are also framed to include psychographic elements—interests and brand affinities—so you can validate whether the audience behaves like a buyer pool or like a fandom cluster. When you’re running anything beyond awareness, this matters. An audience can be large and real, yet still wrong for the brand. If OnSocial shows that an audience’s dominant interests and affinities live far outside your category, you can catch that mismatch before a shipment goes out.

A practical way to use analytics inside OnSocial is to treat it like a pre-flight checklist:

• Posting cadence and consistency: how frequently the creator posts and whether performance is stable enough to predict.
• Format behavior: whether the creator’s audience responds better to certain media types that align with your brief.
• Baseline engagement quality: not only engagement rate, but the texture of engagement—comment patterns, save/share indicators, and whether interaction looks organic.
• Audience composition: does the audience map to the intended market in geography, language, and demographic profile.
• Audience credibility / suspicious patterns: whether the follower base contains anomalies that make the creator risky for paid partnerships.
• Brand adjacency: what brands show up in mentions and what that implies about monetization behavior and saturation.

The platform also calls out notable followers and engaged users as a signal. That can be useful in two ways: it helps validate that a creator’s audience includes meaningful accounts, and it helps you detect whether engagement is coming from a tight creator pod versus a broader audience. For brands that care about cultural influence as much as conversion, that kind of signal can be valuable.

OnSocial’s reporting outputs mirror its broader posture: shareable stakeholder-friendly reports and machine-readable formats for teams that want to operationalize the data. The fact that it supports JSON report exports is not a cosmetic detail; it’s a hint that OnSocial expects advanced users to pipe analysis into their own scoring models or dashboards.

Sponsored posts: making the market visible

Sponsored content tracking is where OnSocial starts feeling like a competitive intelligence tool that happens to be useful for influencer marketing.

In most influencer platforms, “sponsored content” is something you track inside your own campaign. OnSocial treats sponsored posts as a market signal. If you can detect who is doing paid work for which brands, you can answer questions that usually take weeks of manual scrolling:

• Which creators are actively monetizing in this category right now
• Which brands are increasing paid creator activity
• Which creators have already worked with competitors and how recently
• How saturated a creator is with brand deals, and whether that threatens authenticity

The utility for agencies is obvious: it shortens the time between “client wants competitor analysis” and “here is a creator-level map of what’s happening.” It also helps with recruitment strategy. If you can identify creators who already accept sponsorships in your vertical, your outreach success rate tends to improve because you’re not pitching against their identity—you’re pitching into their existing monetization behavior.

It’s also useful for risk control. A creator who looks perfect on paper can still be a bad bet if they’re stacked with multiple concurrent brand deals. Sponsored post visibility makes it easier to avoid creators who are spread too thin, or creators whose feed has become a rotating billboard.

Audience overlap: planning reach with math instead of hope

Overlap analysis is one of those things that sounds like a “nice to have” until you run your first multi-creator campaign and realize you paid five people to reach the same audience cluster.

OnSocial’s audience overlap feature is built to quantify that redundancy across creators and, importantly, to let you focus on credible overlap instead of raw overlap. That distinction matters because inflated follower bases can distort overlap calculations. If overlap is measured against questionable accounts, you can end up optimizing around noise.

For planning, overlap is useful in two opposite strategies:

1. Unique reach campaigns: You want a set of creators whose audiences minimally overlap, maximizing net exposure.
2. Frequency campaigns: You deliberately choose creators with meaningful overlap to hammer a niche and increase repeated exposure.

Both are valid, but only if you can see the overlap clearly. OnSocial turns overlap from a guess into a measurable planning constraint. When used well, it becomes a budgeting tool: you can decide how much redundancy you’re willing to pay for, and you can reallocate spend toward creators that expand the audience footprint instead of duplicating it.

A practical application here is list hygiene. After you build a shortlist in discovery and confirm fit in analytics, overlap becomes the final pass that tells you whether the group works together as a media plan. It’s the step that helps you avoid the “ten creators, one audience” trap.

The API layer: OnSocial as infrastructure

OnSocial’s API offering is where the platform stops behaving like a standalone product and starts behaving like a component you can build with.

The Social Data API is positioned around high-throughput access and a wide range of endpoints across Instagram, TikTok, and YouTube—covering user/profile info, feeds, media detail, comments and replies, hashtag and challenge tracking, and media-level enrichment like captions and audio signals. It’s also positioned around real-time monitoring use cases: detect new posts, track performance changes as they happen, follow hashtags and mentions continuously, and monitor audio trends.

That matters because it reveals who the API is for:

• Influencer marketing platforms that want to embed creator data as a feature
• Agencies that want to run custom tracking and reporting pipelines
• Creator management tools that need reliable creator and content data at scale
• Market research teams that want creator economy signals for analysis

On the implementation side, OnSocial’s API model is framed around an API key generated from the dashboard, then authenticated requests to retrieve data objects across endpoints. The platform also supports a blended usage model: dashboard + API. That combination is practical because it lets teams do research in the UI, then automate what needs to be automated.

If you’re evaluating this as a technical buyer, the API value is less about one endpoint and more about the breadth of what becomes possible:

• Build automated creator monitoring for active partnerships
• Trigger alerts when creators post content containing campaign hashtags or sponsor mentions
• Pull post-level performance data into internal reporting and client dashboards
• Enrich a CRM or outreach system with creator analytics and audience signals
• Track competitive activity by monitoring sponsored content patterns in the category

The platform also positions itself around scale—high request rates and on-demand scaling. For teams running ongoing monitoring across large creator sets, that promise is central. “Great data” doesn’t matter if it can’t be pulled reliably at the cadence your reporting demands.

For clarity, here’s the kind of endpoint grouping OnSocial is built around, expressed in plain operational categories rather than a raw list: profile and account data, content feeds and media detail, comment/reply trees, hashtag/challenge monitoring, search and resolution utilities, and platform-specific objects like music/audio on TikTok and playlists/channels on YouTube. That’s the footprint you need if you’re treating creator signals as a living dataset rather than a one-time report.

White-label delivery: built for agencies who sell insight

OnSocial also acknowledges a reality that many platforms pretend is secondary: agencies don’t just use software, they resell outcomes.

The platform supports white-label outputs in two ways. First, branded reporting (client-facing PDFs with agency identity). Second, a branded delivery environment via a subdomain. That combination reduces the friction between “we did the work” and “the client perceives value.” It also makes OnSocial usable as a backend engine for agencies that want to present influencer intelligence as their own methodology.

This matters because it’s aligned with what OnSocial is good at. A platform that focuses on data, discovery rigor, and analytics depth naturally becomes a tool agencies can package into retainers, audits, competitive scans, and ongoing creator program management.

How OnSocial feels when used correctly

Used casually, OnSocial can look like an oversized discovery engine. Used properly, it behaves like a system for tightening decision loops:

• discovery gives you the candidate universe
• analytics turns that universe into defensible selections
• sponsored post tracking exposes market behavior and competitor patterns
• audience overlap makes your creator set behave like a coherent plan
• API access lets you operationalize everything at scale

That’s the core appeal. OnSocial is built for brands and agencies who want creator marketing to behave like a measurable, trackable channel—where choices are explainable and monitoring is continuous—rather than a sequence of subjective picks held together by screenshots and hope.