From Spend-Based Estimates to Primary Supplier Data: A Migration Playbook

Every enterprise that has gone through a first CSRD Scope 3 filing has started in the same place: spend-based proxies. It is the right starting point. Spend-based estimates using Exiobase or EEIO coefficients can be calculated from procurement data that already exists, they produce a plausible first-year baseline, and they are methodologically defensible as an initial measurement approach under the GHG Protocol. The problem is staying there.

ESRS E1 does not mandate primary supplier data in year one. But it does require disclosure of data quality — which categories are based on primary data, which are based on activity models, and which are still using spend-based proxies. Auditors read that section. Over time, a disclosure that remains entirely spend-based for high-materiality Scope 3 categories raises questions about whether the reported figure is improving or just bouncing with procurement spend levels. The migration toward primary data is both a data quality obligation and a credibility investment.

What follows is the playbook we recommend to enterprise customer success teams preparing their Scope 3 data migration across two reporting cycles.

Year One: Build the Spend-Based Baseline and Identify Priority Suppliers

The first-year objective is not primary data — it is a defensible baseline and a shortlist of priority suppliers. These are two distinct deliverables, and conflating them creates confusion.

The baseline is built from procurement data. Pull all Scope 3 Category 1 (purchased goods and services) spend from your ERP, map it to EEIO or Exiobase industry sectors, apply spend-based emission coefficients, and sum to a total tCO2e figure. This is your year-one Cat 1 baseline. Document the emission factor source, version, and date. The figure will be revised in year two as primary data comes in — that is expected and acceptable, as long as the methodology is consistent and revisions are documented.

Simultaneously, run a supplier segmentation analysis. You are looking for the 20% of suppliers that represent 80% of your Cat 1 spend. These are your Wave 1 engagement targets. For most manufacturing enterprises, this list will be 30–60 suppliers out of a total roster that may run into hundreds or thousands. The 80/20 rule holds remarkably well across industries.

"The migration from spend-based to primary data is not an all-or-nothing transition. It is a progressive substitution, supplier by supplier, tracked transparently in your data quality disclosure. Starting is more important than starting perfectly." — Selin Yilmaz, VP Customer Success, Carbonkindle

Wave One Supplier Engagement: Framing the Request Correctly

The most common mistake in first-wave supplier engagement is asking for too much. A generic request for "your Scope 1 and 2 emissions" delivered via email to a supplier's procurement contact will be ignored or misunderstood by the majority of respondents. Supplier engagement works when the request is specific, the burden is low, and the supplier understands why their data matters.

A well-designed Wave 1 request should:

• Identify the specific products or services you are asking about (not the supplier's entire business)
• Specify the unit of measure you need (tCO2e per tonne of product, per unit, or per EUR of spend — whatever matches your activity data)
• Provide a benchmark figure derived from your current spend-based estimate, so the supplier can see the plausibility range
• Give a clear deadline — typically 6 weeks is the minimum reasonable window for a supplier that needs to retrieve the data
• Explain the reason (CSRD obligation, ESRS E1 disclosure) in plain terms

Response rates for well-designed first-wave requests typically fall in the 25–40% range among Tier 1 suppliers. That is not 100%, and it should not be expected to be. The goal of Wave 1 is primary data from the suppliers that represent the largest share of your Cat 1 footprint. Even 30% response from your top-20 suppliers can shift 40–50% of Cat 1 spend from proxy to primary data.

Data Validation: What to Do With Supplier Responses

Receiving data from suppliers is only half the challenge. Validating it is the other half. Supplier-reported emission figures vary enormously in quality — from ISO 14064-verified product carbon footprints to rough estimates from energy bills divided by production volumes. Accepting all figures at face value without a plausibility check creates disclosure risk.

A practical validation approach uses your spend-based estimate as a plausibility corridor. If a supplier reports an emission intensity that is less than 30% of your spend-based proxy for the same product category, that is a signal to investigate — not necessarily to reject, but to ask for supporting documentation. Similarly, if the reported figure is more than three times the proxy, the same question applies.

For suppliers submitting verified product carbon footprint data (PCF) from a recognized methodology such as ISO 14067 or the PACT framework, the data can typically be accepted without further challenge. Document the verification body and standard in your calculation workbook.

For unverified supplier data, assign a confidence tier:

• Tier A: Third-party verified PCF or science-based target alignment evidence — accept directly
• Tier B: Supplier-calculated figure with supporting calculation methodology — accept with documented review
• Tier C: Supplier estimate without supporting methodology — use as directional input, flag in data quality disclosure

In your CSRD disclosure, you are required to characterize the data quality of each Scope 3 category. Having a documented tiering system for incoming supplier data makes this characterization straightforward and defensible.

Year Two: Substitution and Confidence Score Improvement

In year two, the primary objective shifts from collecting primary data to substituting it into the calculation and measuring the improvement. Every supplier that provided Tier A or B data in Wave 1 should have their spend-based proxy replaced with the verified figure in year two. The base-year figure for Cat 1 may need to be restated if the primary data differs materially from the initial proxy — document this clearly.

Wave 2 engagement expands to the next tier of suppliers by spend. By this point, your Wave 1 experience will have given you response rate data, typical lead times, and an understanding of which supplier segments are more likely to respond. Use that data to improve your outreach approach for Wave 2.

Track your category-level confidence score over time. A simple metric is the percentage of Cat 1 spend covered by Tier A or B primary data versus spend-based proxy. In year one, a typical enterprise might be at 5–15%. After two reporting cycles with systematic engagement, 40–60% primary data coverage is achievable for the top-spend suppliers, which translates to substantially improved overall data quality for the category.

What Assurance Teams Look For in the Supplier Data Section

When a sustainability assurance provider reviews your Scope 3 Cat 1 figures, they are not trying to verify every individual supplier submission — they are testing whether your process for collecting and validating supplier data is systematic and documented.

Specifically, assurance teams will look for:

• Evidence that you engaged suppliers proactively, not just accepted whatever happened to be available
• A documented validation process for incoming data
• Consistent substitution rules: once primary data is accepted, it replaces the proxy in all subsequent calculations and the substitution is noted in the audit trail
• Data quality disclosure that accurately represents the mix of primary versus proxy data in the reported figure

The migration from spend-based to primary data is not a one-year project. It is a multi-cycle capability build. The enterprises that will have credible, assurance-ready Scope 3 Cat 1 figures by FY2027 are the ones that started the supplier engagement process in FY2025 — before the auditors arrived.