Marketing

G2 User Satisfaction in B2B Attribution: Why RevSure Outperforms HockeyStack, Dreamdata, Adobe Marketo Measure, CaliberMind, and Factors.ai

RevSure Team
December 16, 2025
·
7
min read
G2’s User Satisfaction Ratings (November 2025) evaluate leading B2B attribution platforms across more than 30 criteria, from usability and support to attribution depth and time to value. The data shows RevSure consistently leading competitors like HockeyStack, Dreamdata, Adobe Marketo Measure, CaliberMind, and Factors.ai in trust, speed to ROI, and reporting reliability. The result is clear: RevSure isn’t just adopted; it becomes trusted revenue infrastructure teams rely on when decisions matter.

B2B attribution platforms usually don’t fail because of missing features. Instead, they fail when teams lose trust in their results, especially when important decisions are on the line. As confidence drops, attribution becomes just a reporting tool that people use less, question more, and eventually avoid.

That’s why user satisfaction data is more important than just comparing features. It shows how platforms actually work after they’re set up, when teams like marketing, sales, RevOps, and finance all rely on the same system to explain results, justify spending, and defend their choices.

As of November 9, 2025, G2’s User Satisfaction Ratings evaluate leading B2B attribution platforms, including RevSure, Adobe Marketo Measure (Bizible), CaliberMind, Dreamdata, Factors.ai, and HockeyStack, across 30+ evaluation criteria. These criteria span usability, partnership quality, attribution depth, reporting reliability, integrations, and time-to-value.

Viewed holistically, the data shows that RevSure consistently leads or ties for the top spot in most important areas. These are the areas that determine whether attribution becomes a trusted part of revenue operations or just a tool teams avoid.

The Question G2 Data Actually Answers

Most reviews of attribution platforms focus on features and models. G2’s user satisfaction data, however, answers a more practical question:

Which platform do teams continue to trust once attribution is under pressure?

This pressure shows up fast in B2B settings. Marketing has to prove ROI. Sales wants to trust the pipeline. RevOps needs systems to be consistent. Finance needs numbers that hold up to board review. If any group loses trust, attribution stops helping decisions and starts causing arguments.

That’s why G2’s wide survey is important. By looking at over 30 different criteria, G2 shows not just what platforms promise, but how they actually perform from setup to daily use. Since attribution systems are central to a complex GTM stack, any weak spot can hurt trust in the whole system.

RevSure leads or ties in about two-thirds of these areas, showing a level of consistency that Adobe Marketo Measure, CaliberMind, Dreamdata, Factors.ai, and HockeyStack don’t match.

Attribution Has Shifted From Models to Trust

Traditional attribution talks often focus on models like single-touch, multi-touch, or algorithmic. But in reality, trust fails more often than the models themselves.

The latest G2 data makes this visible. RevSure records:

These aren’t just surface-level wins. They show how a platform performs when teams deal with real challenges like missing data, tough integrations, reporting disagreements, and changing needs. Platforms that lack strong partnership and support might not lose customers right away, but people use them less over time.

On the other hand, high scores for partnership and support show something lasting: teams trust the platform enough to keep using it when it matters most.

Speed to Value Is a Structural Advantage

One of the clearest signals in the G2 data is how quickly teams see value.

RevSure customers report an average go-live time of approximately 2 months and an average time to ROI of approximately 6 months. In a category where implementations frequently span 3-11 months and ROI can take 9-16 months to materialize, this difference is meaningful.

A faster time to value changes how teams see attribution. Instead of viewing it as a risky analytics project, they see it as a reliable part of revenue operations. When teams gain confidence early, they’re more likely to use it, expand it, and discuss it with executives.

Where RevSure Consistently Leads in the Latest G2 Data

Of all the platforms compared, RevSure stands out in the areas that matter most for long-term use and executive trust:

  • Ease of administration: 90%
  • Ease of use: 82%
  • Good partner to do business with: 100%
  • Likelihood to recommend: 95%
  • Product direction (positive): 100%
  • Quality of support: 100%
  • Integrations: 99%
  • Marketing channel coverage: 96%

These scores show operational reliability. That’s the difference between using attribution once in a while and relying on it every quarter.

Attribution Depth vs. Attribution Adoption

Many platforms advertise lots of attribution features. G2 data points out what really matters: which features customers actually use and rate highly.

RevSure posts 90%+ satisfaction across core B2B attribution capabilities, including algorithmic attribution (93%), B2B attribution (94%), multi-touch attribution (98%), single-touch attribution (98%), offline attribution (88%), and custom attribution (92%).

By comparison, several competitors, most notably Adobe Marketo Measure and Factors.ai, show “Not enough data” across many advanced attribution categories. This typically indicates limited adoption or narrow applicability, rather than mature, broadly used capabilities.

Reporting Is the Moment of Truth

The real test for attribution is in reporting. Dashboards need to match up, metrics should stay consistent across teams, and explanations must hold up to executive questions.

RevSure’s G2 ratings remain strong across reporting-related criteria, including custom reporting (94%), dashboards (92%), data visualizations (92%), and ROI tracking (92%). While some competitors perform well in isolated analytics workflows, the data shows less consistency across the full reporting stack.

This matters because reporting is where attribution earns or loses credibility. If numbers need lots of explanations, people get defensive. When reporting is solid, attribution becomes a trusted source for everyone.

Why G2 Badges Matter in a Fragmented GTM Stack

In a GTM world full of tools and dashboards, being easy to work with and having good support aren’t just nice to have; they’re essential. RevSure’s 21 badges in the Winter 2025 G2 Report, like Easiest to Do Business With, Best Support, and several High Performer awards, show this.

These outcomes emerge when:

  • Data issues are resolved instead of escalated.
  • Support operates as an extension of the customer’s team.
  • Product decisions reduce complexity rather than introduce it.

Badges like these signal that a platform remains easy to use and dependable, even when operating conditions become challenging.

For buyers evaluating attribution platforms, the latest G2 data highlights clear differences across the market. Adobe Marketo Measure remains widely deployed but falls short in modern attribution depth and speed-to-value. CaliberMind aligns well with RevOps use cases but typically requires longer setup cycles. Dreamdata offers solid attribution modeling but shows less consistency in partnership and support. Factors.ai focuses primarily on top-of-funnel insights, with more limited adoption deeper in the funnel. HockeyStack performs well in specific analytics workflows but is less consistent in enterprise-grade reporting and in the depth of attribution.

The data doesn’t say these platforms don’t work. Instead, it shows that RevSure is more consistent in the areas that matter most once attribution is up and running.

What the Latest G2 Data Ultimately Signals

When a platform leads across dozens of areas, it’s not simply because it offers more features. It’s because teams can count on it in real operating conditions.

Based on the G2 User Satisfaction Ratings (November 2025), RevSure builds trust faster, delivers value sooner, scales more effectively across teams, and holds up under executive scrutiny. That is the difference between attribution software teams experiment with and the attribution infrastructure they depend on.

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