Best B2B Attribution Tools in 2026: MMM, Incrementality, and the New Stack

The attribution tool market in 2026 is unrecognizable from where it was three years ago. Bizible quietly stopped being a meaningful product after the Marketo acquisition. The 'multi-touch attribution' category collapsed when the data underneath stopped working. New categories grew in the gap: automated marketing mix modeling, incrementality testing platforms, and self-reported attribution layers that did not exist as standalone products in 2023.

Most of the comparison content out there still references vendors that pivoted, repositioned, or shut down. Here is the honest 2026 picture for B2B teams.

The four categories of attribution tool

There is no single attribution tool. There are four categories of tool, and a mature stack uses one from at least three of them. The categories: marketing mix modeling, incrementality testing, self-reported attribution, and CRM-native pipeline attribution.

The framework for picking tools comes out of the cookieless attribution stack. The summary: no single tool gives the truth. You triangulate.

Marketing mix modeling platforms

MMM uses aggregated spend and outcome data to model channel contribution. It is the only attribution layer that is fully robust to cookieless. It is what every team needs as the backbone of measurement in 2026.

Recast

Best fit: B2B teams in the 5 to 100 million revenue range that want a managed MMM with weekly refresh. Recast was the early entrant that defined the always-on MMM category.

Pricing: 4,000 to 12,000 dollars a month depending on data volume and number of markets. Implementation is two to four weeks.

Strengths: Bayesian model, weekly automated refresh, good handling of long-windowed B2B conversion. Includes scenario planning for budget reallocation.

Weaknesses: requires reasonably clean historical spend data (24 months minimum). Smaller teams without that data history get less reliable models in the first six months.

Lifesight

Best fit: teams that want MMM, incrementality, and identity resolution in one platform. Lifesight is closer to a measurement suite than a pure MMM.

Pricing: 2,500 to 8,000 dollars a month. Lower entry point than Recast.

Strengths: integrated incrementality testing reduces calibration friction. Good geo-experiment workflows. Identity layer adds CRM and CDP integration.

Weaknesses: MMM is competent but less mature than Recast for B2B-specific lag patterns. Some teams find the integrated suite is more than they need.

Northbeam

Best fit: D2C-heavy mix with B2B side. Northbeam originated in D2C and is still strongest there, but the B2B coverage has matured.

Pricing: 4,000 to 15,000 a month. On the higher end of the category.

Strengths: best-in-class creative-level attribution for paid social, strong incrementality features.

Weaknesses: B2B teams without significant paid social spend pay for capability they will not use.

Mass Modeled and Prescient AI

Smaller MMM vendors targeting the 5,000 to 10,000 a month range. Worth evaluating against Recast and Lifesight if budget is the primary constraint. Both shipped credible products in 2025 and have references in B2B SaaS.

Open source: Meridian and Robyn

Google's Meridian and Meta's Robyn are the open-source MMM frameworks. Building on either requires a full-time analyst (or fractional) with Python and Bayesian model fluency.

Best fit: teams with strong in-house data engineering that want to avoid vendor lock and have the resources to maintain. Most B2B teams under 200 million in revenue are better served by a managed platform.

Incrementality testing platforms

Incrementality testing is the calibration layer for MMM. It also stands alone as a single-channel measurement when an MMM is overkill.

Haus

The category leader for managed incrementality. Best fit: teams running enough paid spend to justify the platform (1 million plus per year). Designs and runs geo-experiments, handles statistical analysis, integrates with major ad platforms.

Pricing: 4,000 to 10,000 a month.

Strengths: removes the statistical-design overhead that stops most teams from running real incrementality tests. Excellent reporting.

Weaknesses: cost-prohibitive for teams under 500k in annual paid spend. The test design is also possible (with more friction) in a spreadsheet.

Lifesight and Northbeam (incrementality features)

Both platforms include incrementality testing as part of their suite. For teams already using one of them for MMM, this is the simplest path. Standalone capability is less mature than Haus but adequate for most B2B test designs.

Build it yourself

For teams with the analyst time, a basic matched-market geo-test can be designed in a spreadsheet. The harder part is convincing the paid team to actually pause the channel in the control geographies. Discipline, not tooling, is the bottleneck.

Self-reported attribution tools

The fastest-growing attribution category in 2025 and 2026. The premise: ask the prospect 'how did you hear about us' and treat the answer as a primary signal.

HockeyStack

Currently the most-used standalone self-reported attribution platform in B2B. Integrates with HubSpot, Salesforce, Marketo. Handles the survey UX, response analysis, and stitches answers to CRM contacts.

Pricing: 1,000 to 4,000 a month.

Strengths: makes self-report production-grade. Good dashboarding. Lightweight to deploy.

Weaknesses: self-report alone is one signal, not a full attribution model. HockeyStack is positioning toward broader MTA, which has the same data problems as the legacy MTA tools.

Common Room and Dreamdata

Both include self-reported attribution as part of broader account-level intelligence. Worth considering if the larger feature set fits.

Just ask the question on your forms

A multi-select question on every form (with the analysis done in a CRM report or BI tool) captures most of the value. Not as polished, but the cost is zero. Many teams start here and add HockeyStack later.

CRM-native attribution

Both HubSpot and Salesforce ship multi-touch attribution as part of their core platforms.

HubSpot Marketing Hub Enterprise

Includes campaign-level and contact-level attribution. The 2026 version supports configurable multi-touch models. Quality varies by how clean the campaign tagging discipline is.

See the HubSpot vs ActiveCampaign comparison for broader fit analysis.

Salesforce Marketing Cloud

Native attribution via Datorama and Marketing Cloud Intelligence. Strong for teams already on the Salesforce stack. See the Salesforce vs HubSpot comparison.

Treat as one input, not the answer

CRM attribution credit campaigns that were tagged. It systematically undercounts demand generation that happened months earlier. Use it for the late-stage view. Use MMM and self-report for the rest.

What to avoid

Any vendor still selling 'unified marketing measurement' that is actually pixel-based MTA in new packaging. The category collapsed for a reason.

Probabilistic device matching vendors who promise to reconstruct cross-device journeys without cookies. The match rates that justified the cost in 2023 are not there in 2026.

Single-channel attribution dashboards inside ad platforms reported as the truth. Always self-serving. Always one input among many.

Putting it together

A typical mature B2B attribution stack in 2026 looks like:

Recast or Lifesight for MMM. HockeyStack or a built-in form question for self-report. Haus or in-platform features for incrementality. HubSpot or Salesforce native for CRM pipeline attribution.

Total monthly cost: 6,000 to 18,000 dollars across vendors, plus analyst time. For most B2B teams above 10 million in revenue, this is the new floor for honest measurement.

For teams under that, the prioritized stack is: free self-reported attribution question this month, CRM-native attribution next quarter, MMM platform in year two when the data history is long enough to model well.

Pair this stack with the ROI measurement strategy and the marketing audit template to validate the numbers are actually changing how budget gets allocated. Tools are only useful if they change decisions.