ROW (Rest of World) Archives

Introduction

In the entertainment industry, growth is increasingly driven by the ability to understand and respond to rapidly evolving consumer preferences. While large volumes of customer data exist, many organizations struggle to convert that data into actionable insights that directly impact revenue.

A global entertainment company faced this exact challenge—data was available, but not effectively connected to decision-making, limiting its ability to target high-value customers and drive growth.

Customer

A global entertainment company with diverse content offerings, struggling to translate fragmented customer data into actionable growth strategies.

Business Objective

Identify and target high-value customer segments
Improve content relevance and cross-sell opportunities
Embed data-driven decision-making into business operations
Accelerate revenue growth through personalized offerings

Scope of Services

Customer Segmentation & Value Identification

Developed a micro-segmentation model to identify high-value customer cohorts and their behavioral patterns.

Data Integration & Insight Layer

Connected point-of-sale and customer interaction data to create a unified view of customer behavior.

Advanced Analytics & Affinity Modeling

Built models to identify relationships between content consumption patterns, enabling smarter bundling and recommendations.

Test-and-Learn Framework

Established experimentation capabilities to continuously refine content offerings and marketing strategies.

Embedded Analytics in Operations

Integrated analytics tools into day-to-day decision-making processes, ensuring sustained adoption and impact.

Technology Used

Advanced Analytics & Predictive Modeling
Customer Data Integration Platforms
Experimentation Frameworks (A/B testing)
Data Visualization & Insight Tools
Cloud-based Data Processing

Key Challenges Addressed

Disconnected customer and transaction data
Limited visibility into high-value customer segments
Ineffective content targeting and bundling strategies
Lack of experimentation culture
Analytics not embedded into business workflows

Benefits of Entertainment Analytics

Targeted Customer Engagement

Enabled precise targeting of high-value customer segments

Smarter Content Monetization

Improved bundling and recommendation strategies based on affinity insights

Continuous Optimization

Shifted from static strategy to iterative, data-driven decision-making

Operational Adoption of Analytics

Ensured analytics became part of everyday business processes

Impact

500% increase in sales of related content in pilot programs
Improved customer targeting and engagement effectiveness
Stronger alignment between content strategy and consumer demand
Accelerated growth through data-driven experimentation

Introduction

In the gaming industry, early success is often driven by a single breakout title. However, sustaining growth requires transitioning from a product-centric organization to a scalable, multi-product entertainment business.

A fast-growing gaming company faced this exact inflection point—strong commercial success from a flagship game, but an organizational structure that could not support expansion, innovation, or cross-product scalability.

Customer

A rapidly growing video game company with a successful flagship title, struggling to scale into a multi-product entertainment business due to structural and operational limitations.

Business Objective

Transition from single-product success to a multi-product portfolio
Enable scalable game development and publishing capabilities
Improve collaboration across teams and business units
Establish a structure that supports long-term growth and innovation

Scope of Services

Enterprise Operating Model Redesign

Defined a new organizational structure aligned to product portfolios and enterprise functions, enabling scalability and clarity.

Product-Centric Organization Design

Reorganized teams around multiple product lines instead of a single flagship offering, enabling parallel development and innovation.

Role & Accountability Realignment

Redefined responsibilities across a large portion of the workforce to ensure clear ownership and reduce execution friction.

Governance & Execution Framework

Established a centralized transformation office to manage execution, track progress, and drive adoption across teams.

Collaboration & Integration Model

Created mechanisms to improve coordination between product, publishing, and enterprise functions.

Technology Used

Organizational Design Frameworks
Workforce Planning & Role Mapping Tools
Performance Tracking Dashboards
Collaboration & Workflow Platforms

Key Challenges Addressed

Overdependence on a single successful product
Organizational structure not designed for scale
Lack of clarity in roles and responsibilities
Limited cross-team collaboration
Inability to efficiently launch multiple products

Benefits

Scalable Product Development

Enabled parallel development of multiple gaming experiences

Improved Organizational Clarity

Clear accountability reduced execution delays and confusion

Stronger Collaboration

Aligned teams across product, publishing, and enterprise functions

Faster Innovation Cycles

Improved ability to launch new products and experiences

Impact

50%+ increase in B2B commercial revenue within the first year
Increased volume and frequency of product launches
Improved ability to deliver cross-product player experiences
Strengthened foundation for long-term growth

Introduction

Telecom operators often struggle with declining customer satisfaction due to inconsistent service quality, fragmented experiences, and legacy operational models. This case highlights how a telecom provider transformed its business by placing customer experience at the core of its strategy, enabling improved loyalty, retention, and revenue growth. The transformation focused on aligning operations, customer journeys, and performance metrics around a customer-centric approach.

Customer

A telecom operator facing declining customer satisfaction and competitive pressure in a mature market. The organization managed multiple customer touchpoints and services, requiring a more consistent and personalized experience strategy to improve market positioning.

Business Objective

Improve customer satisfaction and loyalty across services
Reduce churn and increase long-term customer retention
Strengthen competitive positioning in a mature telecom market
Align business strategy with a customer-centric operating model

Scope of Services

Customer experience strategy definition and planning
Implementation of Net Promoter System (NPS) framework
Customer journey redesign across key touchpoints
Organizational alignment around customer experience metrics
Performance tracking and continuous improvement initiatives

Technology Used

Customer experience measurement platforms and NPS systems
Customer analytics and feedback management tools
Data platforms for customer insights and reporting
CRM and interaction tracking systems

Key Challenges Addressed

Poor customer experience compared to competitors
Weak network and service perception among customers
Lack of customer-centric business strategy
Fragmented service delivery across touchpoints

Benefits

Improved customer satisfaction and loyalty
Better alignment between services and customer expectations
Stronger competitive positioning in the market
Increased customer advocacy and engagement

Impact

Companies with strong CX achieve 4–8% higher revenue growth compared to market averages
Improved Net Promoter Score (NPS) across customer segments
Reduced churn and increased customer retention
Higher lifetime value per customer through improved engagement

Introduction

Transportation Management System (TMS) consolidation enables global logistics enterprises to unify fragmented systems, improve visibility, and reduce operational complexity. Organizations operating across multiple geographies and transport modes often struggle with disconnected platforms, inconsistent data, and limited decision-making capabilities. This case study highlights how a global shipping and transportation company transformed its complex landscape into a unified operating model. By harmonizing data, standardizing processes, and defining a future-ready roadmap, the organization established a scalable foundation for efficient and integrated transportation operations.

Customer

A leading global shipping and transportation company operating across multiple geographies and managing complex, multi-modal logistics networks.

Business Objective

Consolidate fragmented transportation systems into a unified model
Achieve global visibility across transportation data and operations
Define a future-ready transportation capability roadmap
Reduce IT and operational costs through rationalization
Enable centralized and data-driven decision-making

Scope of Services

Enterprise transportation landscape assessment
Business and IT capability discovery
Evaluation and selection of TMS platform
Solution blueprinting for unified operations
Definition of transformation roadmap
Data harmonization and process standardization strategy
Integration and synchronization framework design

Benefits

Single Transportation Management Platform for unified governance
End-to-end visibility across all transportation modes and regions
Standardized processes across geographies and business units
Improved decision-making through harmonized data
Scalable foundation for future transportation growth

Impact

7% reduction in forecasted IT costs
$9M estimated total cost savings
Unified TMS platform across ADM
Improved global visibility and governance
Foundation for scalable and future-ready transportation capabilities

Introduction

Following a large-scale merger, a global media organization faced increasing pressure to improve cash flow visibility and financial efficiency. While revenues remained stable, poor working capital management limited liquidity and reduced the organization’s ability to reinvest and respond to market dynamics.

The challenge was not growth; it was unlocking trapped cash within existing operations.

Customer

A multinational media enterprise undergoing post-merger integration, dealing with fragmented financial processes and inconsistent cash management practices across business units.

Business Objective

Improve cash conversion cycle and free cash flow visibility
Identify and unlock working capital trapped in operations
Standardize financial processes across merged entities
Strengthen control over receivables and payables

Scope of Services

Working Capital Diagnostic

Conducted a structured assessment of accounts receivable and payable processes, identifying inefficiencies across the cash cycle.

Process Deep-Dive (Order-to-Cash & Procure-to-Pay)

Analyzed end-to-end financial workflows to uncover delays in collections and inefficiencies in vendor payment structures.

Opportunity Identification & Prioritization

Identified multiple high-impact levers to improve liquidity, including customer payment delays and suboptimal vendor terms.

Financial Visibility Framework

Designed a centralized tracking and reporting mechanism to monitor working capital performance across business units.

Transformation Roadmap & Governance

Established a structured execution plan supported by a program management office (PMO) to drive adoption and ensure accountability.

Key Challenges Addressed

Lack of visibility into real-time cash flow performance
Delayed customer payments impacting liquidity
Vendor payment terms below industry benchmarks
Fragmented financial processes post-merger
Absence of standardized working capital governance

Benefits

Improved Cash Visibility

Enabled leadership to track free cash flow and working capital performance in real time

Optimized Financial Processes

Standardized receivables and payables management across business units

Stronger Vendor & Customer Management

Improved control over payment cycles and contractual terms

Structured Financial Governance

Introduced accountability through centralized monitoring and execution frameworks

Impact

Identified opportunities to unlock $800M+ in cash benefits within two months
Improved cash conversion cycle across business units
Reduced delays in receivables and optimized payables structure
Strengthened financial control in a post-merger environment

Introduction

Product portfolio transformation is critical for global software companies managing multiple acquired products and fragmented offerings. Disconnected product lines often lead to inconsistent customer experiences, slower innovation cycles, and missed revenue opportunities. This case study highlights how a global software product company restructured its portfolio, modernized products with cloud-native and AI capabilities, and established a scalable innovation model. By aligning product strategy with customer needs and market opportunities, the organization improved monetization, accelerated releases, and strengthened its competitive positioning.

Customer

A global software product company with multiple acquired products, operating across diverse geographies and customer segments.

Business Objective

Consolidate and rationalize fragmented product portfolio
Monetize existing IP and software assets
Accelerate product innovation and time-to-market
Improve customer retention and engagement
Enable scalable product engineering and delivery

Scope of Services

IP acquisition and portfolio restructuring
Product modernization using cloud-native architectures
Infusion of AI/ML capabilities into product offerings
Product roadmap definition and execution
Customer success and lifecycle enablement
Channel and partner ecosystem enablement
API and integration framework standardization

Benefits

Increased monetization of existing product assets
Expanded solution portfolio and revenue streams
Improved customer retention and renewal rates
Faster innovation cycles and release velocity
Scalable product engineering and delivery model

Impact

Clear and scalable product roadmap established
Improved customer engagement and retention
Faster time-to-market for new product releases
Stronger cross-sell and up-sell capabilities
Sustainable innovation and engineering foundation

Introduction

Global operating model transformation enables enterprises to standardize processes, improve governance, and reduce operational costs across complex, multi-region environments. Organizations operating in regulated industries often face fragmented processes, inconsistent compliance practices, and high run-the-business (RTB) costs. This case study highlights how a global enterprise transformed its IT and operations landscape by implementing a standardized, automation-led operating model. By combining AI-driven automation, process rationalization, and strong governance frameworks, the organization achieved scalable efficiency, compliance excellence, and financial optimization.

Customer

A global enterprise operating across multiple regions with a complex IT and operations landscape and strict regulatory and compliance requirements.

Business Objective

Transform global operating model across IT and operations
Standardize processes across portfolios and geographies
Improve operational efficiency and governance
Reduce RTB costs at scale
Strengthen audit and compliance posture

Scope of Services

Global IT and operations delivery transformation
Process standardization and SOP rationalization
Setup of Automation Center of Excellence (CoE)
Implementation of AI-driven operational automation
Risk, audit, and compliance governance frameworks
Global delivery hub and proximity support enablement

Benefits

Enterprise-wide standardized processes
Strong and consistent compliance posture
Improved agility and faster transformation cycles
Predictable and scalable operating model
Reduced dependency on external vendors

Impact

Reduction from 395 SOPs to 25 standardized processes
100% green audit compliance sustained over multiple years
Zero upfront financial commitment for transformation
Multi-market automation rollout across regions
Improved operational consistency and governance

Introduction

Cloud transformation is critical for cold chain logistics providers where infrastructure reliability directly impacts time-sensitive supply chains such as food and pharmaceuticals. Legacy data centers often limit scalability, increase operational risk, and hinder responsiveness to dynamic demand. This case study highlights how a global cold chain logistics provider adopted a cloud-first strategy to modernize its infrastructure. By leveraging hybrid cloud architecture and automation-driven migration, the organization improved resilience, ensured near-zero disruption, and created a scalable foundation for future digital operations.

Customer

A global cold chain logistics provider supporting temperature-controlled logistics, warehousing, and distribution for food and pharmaceutical supply chains.

Business Objective

Enable a cloud-first IT strategy
Improve infrastructure resilience and scalability
Reduce dependency on legacy data centers
Ensure zero disruption to critical operations
Support future digital and operational growth

Scope of Services

Migration of production and DR workloads to Microsoft Azure
Implementation of hybrid cloud operating model (MCOD)
Extension of cloud infrastructure across EMEA and APAC regions
Data center consolidation and migration
Infrastructure automation and digital twin modeling
Testing, deployment, and stabilization of cloud environments

Benefits

Improved infrastructure reliability and resilience
Faster response to operational and business needs
Standardized global infrastructure operations
Reduced operational risk for time-sensitive logistics
Scalable foundation for digital transformation

Impact

70% of workloads migrated to Azure
97% virtualization achieved
Near-zero downtime during migration
Near-zero data loss across systems
Improved operational continuity across global operations

Introduction

AI-driven customer service optimization enables logistics organizations to reduce support costs, improve customer experience, and uncover hidden operational inefficiencies. Logistics providers handling large volumes of shipments often rely heavily on call-based customer support, leading to rising costs and inconsistent service quality. Limited visibility into the root causes of customer queries further restricts optimization efforts. This case study highlights how a logistics major leveraged analytics and AI to transform customer service operations, identify inefficiencies, and establish a scalable foundation for AI adoption across shipping workflows.

Customer

A logistics organization operating large-scale shipping and customer service operations with high dependency on call-based support and service desk interactions.

Business Objective

Reduce customer service support costs
Improve customer satisfaction and experience
Identify hidden inefficiencies in operations
Enable data-driven decision-making
Scale AI adoption across logistics processes

Scope of Services

Analysis of customer service call data and shipping operations
Correlation of customer interactions with operational events
Identification of inefficiencies and bottlenecks
Root cause analysis of customer dissatisfaction drivers
Identification and prioritization of AI use cases
Continuous analytics and insight delivery
Experimentation and validation of AI-driven solutions

Benefits

Reduced dependency on live customer service agents
Improved understanding of cost and inefficiency drivers
Faster identification of operational bottlenecks
Data-driven prioritization of automation initiatives
Continuous improvement through analytics insights

Impact

13% reduction in customer calls through IVR and conversational AI
30+ analytical reports delivered to stakeholders
5+ AI use cases and POCs successfully implemented
Improved visibility across customer service and shipping operations
Established foundation for scalable AI adoption

Introduction

Incident analytics–driven IT automation enables banking institutions to improve resilience, reduce incident volumes, and enhance customer experience. Large-scale banking environments often face high volumes of IT incidents, especially during peak business hours, impacting users and customers. Reactive support models lead to SLA breaches, delayed resolution, and operational inefficiencies. This case study highlights how a banking institution leveraged data-driven incident analytics and automation to identify patterns, reduce manual intervention, and build a proactive, self-healing IT operations model.

Customer

A banking institution operating large-scale IT environments with 24×7 support requirements and high incident volumes impacting business users and customers.

Business Objective

Improve IT resilience through automated healing
Reduce incident volumes during peak business hours
Minimize SLA violations in response and resolution
Shift from reactive to proactive IT operations
Enhance end-user and customer experience

Scope of Services

Incident data analysis using heat maps and ticket analytics
Identification of peak-hour incident patterns
Classification of incidents based on type and automation potential
Analysis of high-volume incident drivers (password, account, connectivity, configuration)
Identification of duplicate and related tickets
Design and enablement of automation and auto-healing workflows
Establishment of a 24×7 integrated command center

Benefits

Faster incident response and resolution
Reduced dependency on manual support processes
Improved SLA adherence across operations
Better prioritization of critical incidents
Reduced operational noise and duplication
Enhanced productivity of IT support teams

Impact

~75% of incidents during business hours optimized for automation
Up to 30.7% automated resolution potential identified
High automation potential across key categories:
- Password issues (22%)
- Account issues (19%)
- Connectivity issues (17%)
- Configuration issues (16%)
Reduced manual intervention in repeatable incidents
Established foundation for scalable, self-healing IT operations