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Home / Case Studies / Rebuilding a Multi-Location Automotive Service Marketplace for Scale, Speed, and Trust
Automotive / Marketplace

Rebuilding a Multi-Location Automotive Service Marketplace for Scale, Speed, and Trust

In late 2025, a fast-growing automotive service marketplace was hitting its limits. Search latency had doubled, onboarding new locations took weeks, and trust signals between buyers and mechanics weren’t consistent. Webskyne editorial partnered with the product and engineering teams to redesign the data pipeline, modernize the web and mobile experience, and introduce a new operational backbone for job dispatch. This case study covers how we turned a fragmented platform into a high-performing, transparent system across 40+ cities. We walk through the challenge, goals, architecture choices, migration strategy, and the deployment plan that kept the marketplace live while we rebuilt it. Results included a 58% faster search experience, a 31% increase in booking conversion, a 46% reduction in failed dispatches, and 2.3x faster onboarding for new service partners. The lessons focus on phased migrations, instrumentation-first engineering, and designing trust signals into every workflow. The outcome: a marketplace ready for enterprise growth without compromising customer experience.

8.1s → 1.8s
Search latency (P95)
14% → 18.3% (31% lift)
Search-to-book conversion
9.5% → 13.1%
Mobile conversion
12.4% → 6.7% (46% reduction)
Failed dispatches
18 days → 7.8 days
Partner onboarding time
1,120/month → 670/month
Support tickets related to disputes
+22% within two quarters
Revenue per active city
38 → 56
Customer NPS (target cities)
Rebuilding a Multi-Location Automotive Service Marketplace for Scale, Speed, and Trust

Challenge

The marketplace faced a layered set of challenges, each compounding the other: 1. Performance bottlenecks: Search queries performed multiple expensive joins in the primary relational database. As the mechanic network grew and availability data became more granular, response times increased. Peak-hour search times exceeded 8 seconds on mobile. 2. Fragmented data pipelines: Listings, availability, service areas, and pricing were managed across separate services and spreadsheets. There was no unified source of truth, which led to mismatched search results and price disputes. 3. Inefficient onboarding: Bringing a new partner garage online required 12–15 manual steps. It often took 2–3 weeks to complete setup, delaying revenue and stretching support teams thin. 4. Limited trust signals: Customers could not easily see mechanic certifications, job photos, or verified reviews. Mechanics could not assess job scope before accepting. This led to high cancellation and dispute rates. 5. Dispatch reliability: The dispatch module lacked geofencing and time-window enforcement, causing mechanics to accept jobs they couldn’t meet. The result: failed appointments, low NPS, and operational churn. 6. Operational reporting gaps: Teams lacked clear visibility into conversion funnels, regional performance, and service quality. Decision-making was slow and often based on incomplete data. These issues were not isolated; they created a feedback loop of slow growth, rising support costs, and declining customer trust.

Solution

We adopted a phased, metrics-driven approach to replatforming. Instead of a risky big-bang rewrite, we introduced a modular architecture alongside the existing platform and migrated workflows incrementally. The approach included: 1. Instrumentation first: Before changing systems, we implemented comprehensive telemetry using event tracking, structured logging, and APM. We established baseline conversion and latency metrics, which became the north star for success criteria. 2. Domain-driven redesign: We separated the domain into clear bounded contexts: Partner Management, Search & Discovery, Dispatch, and Payments. Each context had its own service layer and API contracts. 3. Single source of truth: We consolidated partner data into a normalized schema with strict validation rules. This allowed consistent representation of service areas, pricing tiers, and availability windows. 4. Search optimization: We introduced a search index tailored to geospatial queries and availability scoring. This removed the burden from the core transactional database. 5. Workflow automation: We replaced manual onboarding tasks with guided setup flows, automated document verification, and checklist-based approvals. 6. Trust-focused UX: We introduced profile completeness indicators, verified badges, and job scope previews to reduce uncertainty for both customers and mechanics. This approach balanced engineering risk with measurable improvements at each phase, and allowed the product team to continue shipping features without disruption.

Implementation Highlights

  • 1) Data Model & Services
  • 2) Search & Discovery
  • 3) Dispatch & Workflow Orchestration
  • 4) Partner Onboarding & Trust Signals

Outcomes

8.1s → 1.8s
Search latency (P95)
14% → 18.3% (31% lift)
Search-to-book conversion
9.5% → 13.1%
Mobile conversion
12.4% → 6.7% (46% reduction)
Failed dispatches
18 days → 7.8 days
Partner onboarding time
1,120/month → 670/month
Support tickets related to disputes
+22% within two quarters
Revenue per active city
38 → 56
Customer NPS (target cities)
Tech stack
MarketplaceAutomotiveProduct StrategyPlatform EngineeringOperationsGrowthUser Experience
Timeline: Not specified
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