The sustainability team replaced a consultant-led process with real-time, audit-ready footprint data.
e.l.f. Beauty replaced a manual, consultant-led emissions process with a real-time, audit-ready carbon footprint. With full visibility into their data, the team can move faster—shifting from manual measurement to decisions that drive impact such as transport and packaging.
Challenge
e.l.f. Beauty's sustainability program was evolving and e.l.f. Beauty was looking for broader visibility into key categories and deeper insights into how emissions were calculated. With limited ability to iterate or validate outputs, the prior footprint couldn't effectively support decision-making, nor could it be reproduced.
Solution
With Watershed, e.l.f. built a complete, activity-based footprint with full transparency into every calculation. Automated data mapping and real-time access replaced days of manual work, giving the team the speed and confidence to move from measurement to action.
Results
Tighter and immediate feedback loop with direct, in-platform editing: With Watershed, the team can adjust inputs, see updated results immediately, and iterate without waiting on external turnaround.
Supplier and financial data mapped in minutes, not days: Auto-mapping eliminated days of manual work, reducing mapping time to minutes.
High-confidence auto-mapping: All auto-mapped emissions factors were accepted without revision; only lower-confidence mappings were flagged for review.
Footprint completeness increased to 95%+: A more comprehensive footprint gives the team greater confidence in their decisions.
Challenge
e.l.f. Beauty is built on accessibility—providing the "best of beauty for every eye, lip, and face"—and its approach to sustainability is inseparable from that. But the company’s global supply chain comes with a complex emissions footprint.
Before Watershed, e.l.f. relied on a third-party to complete its footprint calculation. As Sarah Risom, Senior Manager of Sustainability, puts it, "The calculations were done in a black box, and the process involved constant back-and-forth with no real-time visibility."
With thousands of suppliers and sprawling datasets, measurement required ongoing follow-ups—multiple meetings per week, repeated data checks, and long iteration cycles. Every new footprint variation required multiple feedback loops with limited ability to examine or adjust results. The result was a footprint that didn't give the team a sufficient foundation for strategic decision-making.
Solution
What e.l.f. needed was a complete, activity-based footprint that could capture the actions already being taken across the supply chain—and free the team to focus on strategy rather than mechanics. Watershed delivered on both.
With Watershed, the team can see—and trust—how every number is calculated. The team can now follow a clear trail for every calculation, all in one place.
“The biggest value Watershed brings is the ability to upload data and have a clear trail we can follow—we see the raw file, the transformed input, the emission factor, the assumptions. We know exactly what's happening and can speak to exactly how each calculation was done.”
Izzy Sheridan,
Associate Sustainability Manager
Data work that once took days now happens in hours, thanks to Watershed's AI-powered auto-mapping. When e.l.f. first uploaded its materials, the AI automatically matched every line item to an emissions factor in moments. "I uploaded the data and the AI auto-mapper mapped everything to an emissions factor,” explains Sheridan. “I went through all the mappings, and very few needed to be corrected. It saved us hours and hours and hours." While Sheridan still needed to verify the AI’s outputs, it dramatically reduced the manual burden. The team also valued the flexibility to override and track changes to mappings year-over-year.
Those AI capabilities proved their worth especially when e.l.f. implemented a new ERP system mid-fiscal year—a transition Sheridan had expected to be a challenge with respect to mapping new data fields. Instead, the AI auto-mapper handled changes smoothly and the transition was surprisingly easy.
Results
e.l.f. has largely automated its materials and supplier mapping with Watershed, saving significant time on both ends of the process. On the execution side, the team no longer needs to manually identify each material, find the appropriate emission factor, and complete the mapping. On the review side, they can focus their attention on low-confidence mappings rather than reviewing everything line by line. Beyond the time savings, the shift gave the team greater confidence in their data.
As Sheridan puts it, "Now we can be certain that if we follow Watershed's guidance, our data will be audit ready.”
Real-time footprint access has given Risom, Sheridan, and the team a level of agility they didn't have before. The team can run a footprint after each data upload to catch issues early and course-correct quickly. "We now have the capability to pivot really quickly if something doesn't look right," Sheridan says.
That shift away from the mechanics of measurement and toward the decisions that drive sustainability strategy is something Sheridan captures simply: "I spend less time on reformatting and manual data processing, and more time on the judgment calls that actually matter."
With a more complete and accurate footprint, e.l.f. has been able to make more informed strategic decisions across its operations. This includes continuing to prioritize ocean freight for the vast majority of products and implementing zero-emissions drayage in select markets—the short-distance trucking that moves goods from ports to nearby distribution centers—using hydrogen fuel cell trucks. For the first time, e.l.f. has a centralized tool to capture and track the impact of these initiatives.
On packaging, Watershed's emissions factor data has allowed the team to challenge conventional assumptions. For example, while glass has a lower emissions factor than plastic, its higher weight can increase total impact. The ability to model these tradeoffs directly in Watershed, rather than managing them manually, has opened up a new level of rigor in product footprint analysis that simply wasn't possible before.
For e.l.f., the value of that rigor extends well beyond any single decision. Accurate, transparent data has given the team the foundation to move faster and more decisively—whether that's optimizing shipping routes, rethinking packaging materials, or identifying the next opportunity to reduce their footprint.