MRV Data Accelerators
2025
Climate tech
UX Enhancement
Rapid Delivery
A rapid one-month delivery that reduced the time for farmer data entry by 70%, satisfied urgent customer needs, and set the stage for scalable, AI-driven solutions.
Irene L - Design; Vivian D - Product; Lionel H - Eng Manager; Rebecca H, Madeline O, Nan W - Fullstack Eng
Context
Data accelerators make MRV possible
Agribusinesses depend on farmers to supply extensive data to measure, report, and verify (MRV) their GHG emissions and reductions. Farmers must locate their fields on a geospatial map and, for each one, provide detailed records on crops, tillage, fertilizer, and irrigation going back up to five years.
Busy and not usually tech-proficient, farmers struggle with the volume of data entry, relying on various ‘data accelerators’ to make the process more efficient.
Balancing an overhaul, expectations, and urgency
One of our biggest customers, Cargill, was preparing to launch its 2025 program, spanning 1.4 million acres of emissions measurement.
At the same time, we were wrapping up a major data infrastructure overhaul paired with a massive UX/UI revamp — still scrambling to deliver essentials like data validations, satellite-based pre-fills, and other features customers had come to expect from the legacy platform.
With just a month until launch, it was critical we release a key data accelerator: Duplicate Data. Our initial plan was to reimplement the legacy version, but further analysis revealed it left key use cases unaddressed, posing a major risk to our relationship with Cargill.
The legacy Duplicate Data UX
Challenge
Where the legacy UX fell short
From past programs, we knew customers expected a Duplicate Data feature to support four distinct use cases. However, the legacy experience only addressed one of the four, leaving the others unmet and creating significant gaps for customers.
Duplicate all events
Ideal for newly enrolled users with many empty fields that share the same historical records.
Duplicate a specific event
Useful when fields already contain data, but a single year’s events need to be replicated.
Overwrite specified events
The legacy experience overwrote all existing data; customers needed the ability to add to existing records instead of wiping them.
Bulk-edit event attributes
In the legacy platform, we'd provided a 'bulk-edit' tool, which was no longer compatible with our data infrastructure changes.
Facing constant requests from Cargill for updates on the feature’s release, we knew we had to pivot quickly and deliver an improved experience — while acknowledging that the most holistic solution couldn’t be shipped in such a short timeframe.
Approach
Audit and prioritize
We reviewed legacy accelerators and customer feedback to map every JTBD — then zeroed in on the ones critical for Cargill’s launch.
Adapting the legacy UX, then pivoting
We first tried adapting the legacy Duplicate Data UI with extra steps for flexibility. But adding complexity to a modal made it more confusing for users.
Table-based selection
Inspired by products with complex duplication flows, we tested selecting events directly from the data tables.
Expanding on bulk overwrite
Previously, copied events replaced existing events by default. We introduced the option to keep current events while appending new data.
Feedback and Iteration
We iterated with Cargill and our customer support team to adequately support Cargill's use cases, as well as those of other customers. Notably, we offered an additional option for conflicting events: to overwrite events that overlap by date.
Solution
Seamless copying, flexible overwrite
We launched an alpha of the new Duplicate Data feature to Cargill for active use and immediate feedback. It allowed users to copy events across stages (crops, tillage, etc.) directly from a field’s data table, choose target fields, and decide how to handle existing data.
The new Duplicate Data UX
Duplicate all events
The new UX lets users copy every stage’s events from one field, with clear visibility into which events are being duplicated.
Duplicate a specific event
This net-new functionality lets users duplicate a specific event across fields, retaining full visibility into what’s being copied.
Overwrite specified events
When conflicts are detected, users now have two new options on top of overwriting all target field data: add copied events as-is and overwrite events that overlap by date.
Bulk-edit event attributes
We deferred this functionality. Beyond the data validation complexity, we believed it belonged within a broader bulk-management suite to be tackled later.
Outcomes
Average time to complete enrollment Decreases by 70%
This feature has now shipped to all customers. For our largest program, Cargill US, which spans 1.1M acres, we saw a 70% decrease in the average enrollment duration when compared to the previous year's program.
With shrinking sustainability budgets, agribusiness teams must do more with less. Duplicate Data and other Regrow data accelerators make that possible, differentiating our product from competitors.
Next Steps
Thinking critically about what to tackle next
With Duplicate Data launched, product and design paused to reflect — using design thinking to determine which accelerator would deliver the most strategic impact next.
AI as the ultimate accelerator
One opportunity we see is leveraging AI. We prototyped a visionary experience to show how it could save time and further differentiate Regrow from competitors.