Self-serve Kitting Prep
Company
Flexport
Industry:
Supply Chain Logistics
About Convoy
The Convoy Carrier App connects drivers and fleets to a digital freight marketplace, allowing them to find, bid, and manage loads in real time. My design work focused on improving booking flows, visibility, and feedback loops to reduce driver downtime and increase trust in automated bidding systems.
Project Goal
Kitting & Prep operations allow merchants to pre-bundle SKUs and apply retailer-specific labeling or packaging requirements before fulfillment — streamlining multi-channel shipping and improving delivery speed.
Opportunity
Retention risk mitigated for two of our largest merchants.
50% of 2023 volume achieved in one month post-pilot.
Enabled $140K projected annual growth in kitting revenue.
Reduced AM intervention time per job by 80%.
Results
•Unify prep service selection (kitting + simple prep) in a clear, modular UI.
•Display pricing dynamically with intuitive contextual tooltips.
•Minimize merchant error when mixing kitted/non-kitted SKUs.
•Enhance post-booking visibility for prep status and NC (noncompliance) issues.
"This is the best of the best. In the past, I couldn’t always accept Convoy loads that I had won, because I’d booked another load while waiting for a response. Now, I know right away if I’m going to haul it, so I don’t need to search for another load. This significantly reduces the time it takes me to plan and book my schedule!”
Ghareeb Nawaz, Stryker Trans
Josh Rickards, Rickards Transportation Services LLC
Research
Retention risk mitigated for two of our largest merchants.
50% of 2023 volume achieved in one month post-pilot.
Enabled $140K projected annual growth in kitting revenue.
Reduced AM intervention time per job by 80%.
Testing Method
• 11 moderated interviews via Zoom
• Explored current booking behaviors and pain points
• Tested Instant Auction concept — gathered reactions, expectations, and concerns
Participant Demographics
Sampling Criteria:
Carrier segment (intent-based)
Carrier size (driver → small → large)
Mix of drivers, dispatchers, and fleet carriers representing varied booking goals and behaviors
Key Findings
Solutions
Tradeoff: Negotiation Experience vs. Model Integrity
🎨 Design Perspective
Carriers wanted a natural back-and-forth negotiation, like traditional brokerage — more human, flexible, and fair.
🧠 Data Science Perspective
Multiple counters would destabilize pricing models, creating feedback loops and reducing accuracy.
✅ Decision
Limit to one counteroffer cycle to preserve model reliability. Carriers can rebid manually, but each is treated as a new negotiation.
Acceptance Window
We allowed a 15-minute acceptance window for logistics. We did not provide a timer, but instead adjusted the booked now rate to the counteroffer price and showed expired under counteroffer info item.
Tradeoff:
User Sentiment vs. Market Efficiency
When a carrier’s bid was rejected, the team debated whether to “soften” the rejection message — for example, by adding friendlier language (“Your bid wasn’t accepted this time, but we’d love to see you bid again soon!”) or subtle hints (“Try bidding slightly higher next time”).
The question was: should the system empathize or optimize?
🎨 Design tradeoff: Empathy vs. Clarity
💡 Decision: Chose a firm rejection to encourage realistic bids and reduce confusion.
📊 Impact: Faster market resolution, cleaner data, fewer double acceptances.
Rollout Phase 1- 30%
Measuring Impact & Investigating Friction
📊 Monitored feedback loops: CS calls, in-app feedback, brokerage data.
💬 10 carrier interviews: Explored pain points around rates and trust.
🔍 Outcome: Identified pricing perception as key barrier → informed next iteration vs. rollback decision.
User Research - Key Findings
✅ Carriers prefer Instant Auctions — faster responses and less waiting improved overall satisfaction and planning efficiency.
⚠️ Low counteroffers discouraged re-bids — carriers perceived early offers as unfair or uncompetitive, reducing engagement.
⏳ Urgency gap in counteroffers — without time pressure, even acceptable offers failed to drive immediate action.
💰 Counteroffers often below market — pricing too close to “accept now” rates led to distrust and fewer matches.
📉 Conservative initial pricing — encouraged “last-minute” bidding behavior and slowed early marketplace activity.
⚠️ Lack of bid status transparency — carriers grew frustrated by unclear communication about held bids and wanted control to cancel.
Rollout Phase 2
Design Goal: Simplify the Flow & Re-Engage Carriers
🧩 Removed acceptance timer → Simplified decision flow; fewer expirations.
💬 Revised decline modal → Set clearer expectations, encouraged re-bidding.
📈 Outcome: Increased rebid rate, improved carrier retention metrics.
Validating the Rollout: Quantitative Phone Survey
🤝 Partnered with Data Science → Designed structured survey to test rollout readiness.
☎️ 100 carrier calls via CS team → Quantified preference for Instant vs. Timed Auctions.
📊 Goal: Validate experience quality before 100% launch.
Learnings & Reflections
Designing for both user trust and algorithmic stability required tradeoffs.
Carriers valued clarity and fairness more than unlimited negotiation — proving that simplicity can still feel empowering when expectations are clear.
Thanks for reading 😊
