Service Project Analyst - Subaru

The Aftersales Data Analyst is responsible for analyzing operational, financial, and customer data to support business development within the aftersales department. This includes driving growth in Value-Added Services (VAS), extended warranty programs, service campaigns, maintenance packages, and customer retention initiatives. The role supports strategic decisions through dashboards, reporting, opportunity identification, forecasting, and performance tracking. K ey Responsibilities: Data Analytics & Reporting Business Development & Service Projects Support development and rollout of new aftersales services: Extended warranty programs Service contracts / maintenance packages VAS (detailing, accessories, roadside assistance, etc.) Conduct commercial feasibility studies and cost-benefit analysis. Benchmark market competition for pricing, service programs, and product positioning. Assist in vendor coordination with insurance/warranty partners. 3. Customer Experience & Revenue Growth Use data to enhance customer touchpoints and conversion funnels. Segment customers for targeted marketing campaigns. Recommend pricing strategies and promotional plans. Track revenue performance by branch, model, advisor, and product line. Technical: data analysis, BI dashboards, Excel/Power BI/Tableau, CRM/DMS systems, KPI modeling Business: Feasibility studies, pricing models, forecasting, commercial understanding Aftersales: Workshop process knowledge, warranty flow, spare parts basics, CSI/VOC Soft Skills: Communication, presentation, problem solving, stakeholder coordination Qualifications: Bachelor’s degree in business, Engineering, Data Analytics, or related field. 1–3 years of experience in aftersales, automotive, or service industry preferred. Experience with BI tools (Power BI, Tableau) and advanced Excel. Understanding of warranty processes and service operations is a plus. KPIS (Performance Indicators): Revenue growth in VAS & Extended Warranty programs. Service retention and repeat customer rate. Improvement of branch performance KPIs. Reporting accuracy and timely delivery. Cost optimization through data-driven decision making.