John Deere Data Adoption and Trust Enhancement Project

Driving platform-wide data adoption across 500K+ users without destroying trust—enabling $3.8B in connected services revenue

John Deere's future depended on connected services, automation, and AI—but those capabilities required high-quality customer data. The existing model relied on optional data sharing across a fragmented ecosystem of legacy systems, which resulted in low adoption, incomplete profiles, and limited downstream value.

This problem sat at the intersection of business risk, user trust, and organizational complexity:

User diversity: Customers ranged from highly technical operators to users without email access

Data sensitivity: Sharing triggered deep concerns around surveillance, ownership, and value exchange

System fragmentation: Decades of acquisitions created inconsistent data models and experiences

Relationship complexity: Dealers—not Deere—owned many customer relationships

Global scale: 12 languages across diverse regulatory environments

Instead of "forcing" adoption directly, we reframed the problem as progressive value exchange. Our strategy was built on four principles:

Value Before Friction: Users should experience tangible benefits before being asked to share more data

Transparency Builds Trust: Make it clear what data is requested, why, and how it's used—no dark patterns

Multiple Paths, One Outcome: Support adoption through digital flows, dealer-assisted onboarding, and in-field support

Graceful Degradation: Users who didn't complete profiles immediately still retained access—urgency was created through value, not lockout

Instead of a single blocking form, we designed contextual prompts tied to moments of value: predictive maintenance, equipment insights, automation features. Each step answered one question: "What do I get if I do this right now?"

Dealers became UX multipliers, not just support. Clear dealer workflows for assisting customers, shared visibility into completion state, and consistent language across touchpoints reduced friction while preserving trust in local relationships.

We replaced abstract legal language with plain-language explanations, explicit benefit statements, and clear reassurance around data use and control. This dramatically reduced resistance during onboarding.

Profile completion increased from 34% to 87%

Enabled $3.8B in connected services, automation, and AI-driven revenue

Reduced support tickets related to account setup by 16%

Improved dealer efficiency by 28%

Established a reusable adoption framework used across multiple Deere platforms

This work didn't just solve onboarding. It created:

A scalable foundation for AI-driven services

A repeatable model for introducing "forced" change without backlash

A trust-based approach now reused across Deere's digital ecosystem