A PE-backed global sports marketing company faced rising delivery costs across the UK and Europe, along with challenges in scaling specialized technology talent. The company partnered with Aeries to establish its own India entity and create a more cost-efficient operating model.
Impact at a Glance
Margin Improvement
Improved project margins versus UK/Europe delivery
Roles Hired
Specialized technology roles hired within 5 months
Ownership Transition
Client-owned India entity established and operational
About Client
PE-backed mid-market Portfolio company
Industry: Sports Marketing
Headquarters: London, UK
Revenue: $100 M (estimated)
Employees: 2800+
Enabling Scalable Growth Through an Owned India Entity
Challenge
- High delivery costs across UK and Europe impacting margins
- Limited access to specialized technology talent at scale
- No client-owned India operating base to support growth
- Need to move fast without locking into a vendor-dependent model
Client lost $46 M to churn and lacked a way to identify high-risk accounts.
High delivery costs across UK and Europe impacting margins
Aeries’ Solution
- Immediate India hiring enabled while the client-owned entity was set up in parallel
- Senior, specialized high-end engineering talent hired across key roles
- Ownership transition completed within 3-4 months
- HR, payroll, IT, and compliance continuity ensured
- Steering committees and KPI-driven performance reviews established
Results
- ~30% improvement in project margins vs UK/Europe delivery
- ~25 specialized roles hired within 5 months from top -tier organizations
- Client-owned India entity operational within 3-4 months
- Scalable India operating model established
Functions Set Up in India
Engineering & Development
Platform Engineering
Implementation Roadmap
The 13-week project was delivered 100% remotely during COVID-19 in three iterative phases.
Iteration 1 (Weeks 1–4)
- Objective: Build a comprehensive data snapshot for initial analysis.
- Key Activities: Filtered data by brand, product type, revenue, geography.
- Outcome: Established a baseline model with 84% accuracy.
Iteration 2 (Weeks 5–8)
- Objective: Incorporate time-based behavior & retention outreach signals.
- Key Activities: Added engagement history features & support touchpoints.
- Outcome: Precision jumped to 92%, recall to 88%.
Iteration 3 (Weeks 9–13)
- Objective: Add product usage to identify high-intent users.
- Key Activities: Integrated domain traffic and user activity signals.
- Outcome: Achieved 97% accuracy, 98% precision, and 97% recall in the deployed model.