Transforming a growing cargo company's fragmented operations into a connected, human-centered ecosystem

This case study represents the driver app and call center interface portions of a larger ecosystem transformation. I also led redesign of customer-facing platforms and collaborated on internal logistics tools—creating a complete connected experience that reduced operational chaos and improved service quality across all touchpoints.

Overview

Gazelkin is a Russian cargo transportation company specializing in apartment and office moves, cargo taxi, and regional deliveries. Despite rapid growth, its day-to-day operations still relied on manual phone calls, Excel files, and a legacy portal.

I led UX design across the complete service ecosystem: a driver mobile app, logistics dashboard for dispatchers, call center interface, and customer-facing website and mobile app for booking and tracking.

The ambitious goal: Create a connected, human-centered ecosystem where information flowed seamlessly between all stakeholders, making work smoother, faster, and more reliable for drivers, operators, dispatchers, and customers—while ensuring adoption among users who were skeptical of change and had varying levels of technical comfort.

The Challenge

The biggest obstacle wasn't just building new tools—it was replacing entrenched habits while solving critical operational gaps:

  • 40% of orders required multiple callbacks due to incomplete information

  • Drivers averaged 12 calls per shift to dispatch for basic job details

  • Manual price calculations led to 15% error rate and revenue loss

  • No real-time visibility into order status or driver locations

  • Fragmented communication across WhatsApp, phone calls, and Excel files

The deeper challenge: Drivers had never used work apps and feared complexity. Call center agents calculated prices from memory and improvised on every call. These informal workflows couldn't support continued growth.

We needed to replace fragmented processes with seamlessly integrated tools—without overwhelming users with low tech literacy or deep skepticism toward change.

My Role

As the UX Lead and Interaction Designer, I was responsible for shaping the product experience across Gazelkin’s entire logistics ecosystem. My work included:

  • Field research — Shadowed drivers, dispatchers, and call center agents to map real workflows and uncover friction points

  • End-to-end design — Created interaction flows and interfaces for the driver app, logistics dashboard, and call center tool

  • Customer experience — Designed both the customer-facing website and mobile app, handling all UX and UI work

  • Cross-platform consistency — Coordinated with external designers and photographers to elevate the brand across all touchpoints

  • Strategic alignment — Bridged business goals and user realities, ensuring tools reflected actual needs and built trust with users new to digital systems

The Driver App

The problem: Drivers received job details through scattered WhatsApp messages, had no clear task prioritization, and constantly called dispatch for updates—creating bottlenecks and frustration across the system.

Understanding the users

To understand what drivers actually needed, I rode with them for full shifts, sat in their trucks, and asked questions between jobs. At first, they weren’t sure why I was there — but once they saw I was listening, not judging, they told me everything.

Methodology:

Field observation: shadowing 6 drivers during complete shifts

Contextual interviews: Informal conversations during breaks to gather honest input

Workflow mapping: Documented delivery flow from task assignment to completion

Constraint documentation: Captured real challenges like weak mobile coverage, noisy environments, and difficulty accessing information while driving

Pain point analysis: Identified friction moments causing delays and miscommunication

Key Insights

Job details came through WhatsApp chaos Drivers received delivery information as unstructured text messages from multiple operators and logistics team members. No standardization, no confirmations, massive room for error. Critical details were missed, misread, or buried in chat history—especially problematic with 6-8 jobs per day.

Drivers weren’t tech-savvy and didn’t want complexity. Many had never used work-related apps. They were worried about crashing phones, complex interfaces, small buttons. Simplicity wasn't nice-to-have—it was essential for adoption.

They just wanted a clear path — not flexibility. They didn't want options or multitasking features. They needed to know: what's next, how to get there, and how to mark it complete.

Blame without context. When internal miscommunication led to delays or wrong addresses, drivers faced angry customers despite having no control over the root cause.

Design Solution

I designed the entire driver experience from scratch, prioritizing speed, clarity, and real-world work conditions:

🧠 Smart task prioritization: Time-ordered, color-coded job list that reduced cognitive load and made priorities immediately clear.

📦 Complete job context: Location, weight, loader requirements, floor access, and special notes displayed upfront—no more digging through messages.

🚚 Driver autonomy: Ability to browse and accept jobs instead of just following dispatch orders, giving drivers more control over their workday.

💬 Structured communication: Built-in delay reporting, question submission, and client communication—no more random phone calls.

📎 Integrated documentation: Photo uploads for delivery proof, incident reporting, and paperwork handling directly in the app.

💰 Transparent earnings: Real-time breakdown of payouts, bonuses, and adjustments with clear, color-coded explanations.

🛰️ Offline functionality: App remained usable without signal, with SMS backup for critical updates.

  • Looking back with more product experience, I wouldn’t just focus on making things work — I’d design to give drivers more control, reduce risk, and keep them engaged long-term.

    • Job preview tags. Icons for stairs, heavy loads, long-distance, or urgency — visible before accepting.

    • Map-based route preview for the full day. Drivers see all jobs on a single map with distance, order, and estimated time.

    • Quick feedback after each job. One-tap issue reporting to flag delays, wrong addresses, or customer problems.

    • Performance insights. Weekly job count, bonus progress, and helpful stats to stay motivated.

    • Fine dispute flow. Built-in form to contest fines with reason.

      Emergency support button.
      One-tap alert to contact dispatch in case of unsafe or urgent situations.

Results

As soon as it reached the hands of real drivers, the app started solving real problems — and the results were clear.

↓ 45% reduction in dispatcher-driver calls. Drivers stopped calling for instructions or status updates, having everything they needed in the app.

↓ Reduced missed deliveries. Structured job info and delay reporting led to clearer routes and better timing.

↑ 92% adoption rate among drivers within 3 weeks of launch. Drivers with minimal smartphone experience learned and consistently used the app.

↑ Improved job satisfaction. Clearer instructions, fewer miscommunications, and reduced blame for systemic issues made daily work less stressful.

↑ Increased transparency. Photo proof, visible earnings, and clear issue tracking gave drivers more control and reduced disputes.

Call Center Interface

The problem: Operators manually typed order details into messenger chats, calculated prices from memory, and often required multiple customer callbacks to confirm bookings—creating delays and errors throughout the system.

Understanding the users

Before this tool, operators took orders over the phone and typed everything manually into a plain messenger chat with dispatch — addresses, timing, special requests, and price estimates. They calculated costs by memory or guesswork and had to call clients back after confirming with logistics. The new interface replaced that chaos with structure, speed, and confidence.

Methodology:

Shadowed call center operators during live customer calls and order processing

Call simulation: Answered 10+ real customer calls to experience pressure

Process mapping: Documented full order flow from initial call to job confirmation

Simulated edge cases to test pricing logic, timing conflicts, and promo handling

Conducted interviews with operators to surface common issues and workarounds

Key Insights

Overwhelming responsibilities. Operators weren't just taking orders—they were handling confused customers, chasing driver updates, and translating between broken systems.

Ecosystem issues landed on their shoulders. Delays, wrong addresses, or unclear job details caused by dispatch or drivers became the operator’s problem — and their calls often turned into long and difficult support conversations.

Every call was high-pressure. With no real-time visibility or system support, they had to improvise: estimate prices, recall promotions, double-check logistics — all while the customer waited on the line.

Error-prone manual entry. Typing order details into blank messenger threads led to lost information, formatting errors, and downstream confusion.

Inconsistent customer experience: Pricing, discounts, and policies varied depending on which operator answered and how well they remembered current rules.

Reactive instead of proactive. Too much time resolving existing issues meant missing new incoming calls, reducing overall efficiency.

Design Solution

I created a unified interface that transformed order-taking from a stressful improvisation into a confident, guided process:

📋 Unified order form: Single screen for creating any order type with all necessary fields and context.

💸 Dynamic pricing preview: Real-time price calculation as operators added address, timing, and service details—eliminating guesswork and callbacks.

🧠 Built-in guidance: Context-aware prompts showing available discounts, policy reminders, and helpful suggestions based on order details.

📦 Integrated logistics details: Floor numbers, heavy item flags, and access requirements built directly into the job specification.

📝 Custom notes and preferences: Operators could record entry codes, gate info, or reminders like “call on arrival” directly in the order.

💰 Live pricing and discounts: Immediate coupon application and price adjustments with instant total confirmation.

  • Looking back, I'd focus on giving operators even more control and reducing the mental load of complex customer situations.

    • Customer history preview. Quick view of past orders, preferences, and notes before the call even starts—no more starting from scratch with repeat customers.

    • Smart pricing suggestions. AI-powered recommendations for discounts or upsells based on order details, location, and customer history.

    • Real-time driver status. Live view of driver locations and availability so operators could give accurate time estimates instead of guessing.

    • Post-call quick notes. Fast way to tag calls as "difficult customer," "pricing complaint," or "service issue" to help improve operations over time.

    • Stress indicators. Simple daily mood tracker to help management identify when operators need support or breaks.

Results

↓ Eliminated manual pricing errors. From 15% error rate to under 1%. Real-time pricing and structured inputs removed guesswork — no more recalculating or calling clients back.

↑ 40% faster call resolution. Average reduced from 4.2 to 2.5 minutes.

↑ Increased operator confidence. Built-in prompts and automated pricing replaced memory-based calculations, reducing stress and improving accuracy.

↓ 65% reduction in customer callbacks for order confirmation. Operators could quote, confirm, and dispatch in a single conversation — instead of two or three.

↑ Enhanced customer experience. Clients got faster answers and smoother service — reinforcing reliability across the whole experience.

↑ Better workplace satisfaction. Structured workflow reduced daily stress, making operators feel supported rather than overwhelmed.

This case study covers the driver app and call center interface, but represents just part of a larger transformation. In parallel, I also led the redesign of the customer-facing website and mobile app—making it easier for people to book services, track deliveries, and get help without calling support.

I also collaborated with developers on refining the internal logistics tool—improving task visibility, fixing major UX blockers, and helping dispatchers work with less chaos.

I'm still organizing those flows, decisions, and results, and will be updating this case study soon with the full story.

Project Impact & Learnings

This project gave me the opportunity to design complex, multi-sided logistics systems in real-world conditions. From driver tools to customer interfaces, I helped create a connected experience that made work easier and more reliable for everyone involved.

Measurable Results

  • Faster order handling: The new call center dashboard reduced order entry time by 40% and eliminated manual pricing errors.

  • Improved coordination: Real-time job monitoring cut dispatcher-driver calls by 45% and eliminated reliance on phone calls across the operation.

  • Successful user adoption: 92% driver adoption within 3 weeks and 100% operator adoption on day one—even users with low tech literacy quickly embraced the connected system.

  • Connected ecosystem impact: Seamless information flow between all tools reduced delivery errors by 78% and eliminated data silos across the entire operation.

  • Visual consistency: Despite legacy brand constraints, coordinated external resources to create a more professional, cohesive experience across all customer and internal touchpoints.

What I Learned

  • Field research changed everything. Observing users in their actual work environment revealed insights no survey could capture and directly shaped more grounded design decisions.

  • System thinking was essential. Designing interconnected tools for different roles taught me how to create seamless experiences that connect separate functions into a unified whole.

  • Ownership enabled better outcomes. Leading UX across the entire system allowed me to connect user needs with business goals and ensure consistency across all touchpoints.

  • Constraints drove creativity. Working with legacy visuals and low-tech users pushed me to find practical, accessible solutions that prioritized usability over aesthetics.

Professional Growth

This experience taught me to approach complex B2B systems by:

  • Including business model analysis in early research phases

  • Designing for edge cases and system failures from the start

  • Balancing user needs with technical constraints throughout iteration cycles

  • Measuring success through behavioral change, not just feature delivery

The most important lesson: Even the best-designed interfaces require supportive business practices to create lasting impact. Great UX can improve how things feel and work, but sustainable success depends on alignment between user experience and organizational culture.