Talent Strategy Modernization for a $100B+ Minneapolis Based Global Retail Enterprise

Discover How Draup Enabled Finance Shared Services Transformation for a $100B+ Minneapolis Based Global Retail Leader

400,000+

Employee Count

$106 Bn

Revenue

Retail

Industry

Minneapolis

Headquarters

About the Company

A large, multinational retail and consumer goods enterprise with complex global finance operations and a strong presence across merchandising, supply chain, and corporate functions. The organization operates at a significant scale, supporting diverse business units and geographies, and continues to invest in modernizing its finance capabilities through shared services, digital tools, and process standardization.

Before Draup

1

Limited Market-Wide Visibility into Finance Talent

The organization lacked a comprehensive, data-backed view of finance talent availability, skill maturity, and growth dynamics across Indian cities.

2

Unclear Location Trade-offs

Decisions around expanding or diversifying SSC locations were challenged by fragmented insights on attrition, hiring difficulty, and long-term scalability.

3

Evolving Skill Requirements

Rapid adoption of automation and digital finance tools made it difficult to assess readiness of existing and future talent pools.

4

Insufficient Peer Benchmarking

There was limited clarity on how leading employers and industrial automation peers structured, scaled, and retained finance talent in India.

The Core Challenges

Finance talent demand outpaced supply in key hubs, increasing competition and attrition.

Location decisions required balancing talent depth, cost, stability, and growth potential.

Digital and automation skills were becoming critical, but adoption varied widely across roles and cities.

Leadership needed a defensible, data-driven framework to guide long-term Finance SSC investments.

The Solution

Finance Manager

PROFILE

LOCATION ANALYSIS

TALENT MARKET INSIGHTS

CAREER PATHS

DATA INTEGRATION

TECH STACK

Finance Job Role Taxonomy

Defined a clear, standardized taxonomy across eight core finance functions to consistently evaluate shared services roles and workforce mix.

Skill Cluster & Digital Readiness Analysis

Identified core, digital, and emerging skills across finance roles, highlighting growing demand for automation, analytics, and finance platforms.

Location Capability & Favorability Scoring

Created a 0–10 capability index assessing cities on talent depth, SSC presence, growth, attrition, and hiring difficulty to enable objective location comparison.

Employer & Peer Benchmarking

Benchmarked leading consulting, BFSI, IT services, and automation firms to compare hiring trends, attrition levels, and talent maturity.

Outcome

Optimized Location Strategy

Identified Delhi and Bengaluru as top hubs while validating Pune, Chennai, Hyderabad, and select Tier 2/3 cities for scalable growth

Talent Risk Mitigation

Enabled proactive planning around attrition, hiring difficulty, and competition in mature hubs.

Future-Ready Skill Planning

Aligned workforce strategy with increasing demand for digital, automation, and analytics-driven finance roles.

Peer-Aligned Decision Making

Benchmarked SSC structure, growth, and retention against top global employers and automation leaders.

Powered by unparalleled global labor & market data

19K+

Skills

850M+

Professionals

5700+

Locations

1.6M+

Peer Group Companies

1B+

Job Descriptions

4M+

Career Paths

Data Trust & Methodology at Draup

Draup uses a structured methodology to reduce inaccuracies and prevent misleading information, with a focus on transparency and responsible data practices.

1

Data Transparency and Coverage

Draup sources data from diverse, vetted global inputs including public datasets, labor information, professional ecosystems, and industry research. Coverage varies by region and role, and we supplement limited areas with alternative sources and analyst-led modeling.

2

Profiles Behind the Numbers

All aggregate insights related to buyers, roles, skills, and locations are grounded in structured, anonymized human and organizational profiles rather than pure extrapolation. This ensures context, reliability, and protection of personal identities.

3

Data Hygiene and Integrity

We maintain a clean, accurate data environment by removing duplicates, outdated entities, and invalid records. Automated checks powered by ML models and analyst reviews work together to ensure clarity and accuracy for every entity, such as buyer, role, skill, technology, or company. Learn more

4

AI Governance and Bias Mitigation

Our governance framework combines automated evaluations with Human-in-the-Loop reviews. We use statistical checks, audits, and cross-source comparisons to reduce demographic and structural bias before insights reach the platform.

5

Compensation Guardrails

Compensation insights use blended, directional inputs such as aggregated ranges, benchmarks, modeled distributions, and market signals. We distinguish clearly between modeled and reported data and apply guardrails to prevent over-interpretation.

6

Hourly and Frontline Workforce Coverage

For hourly, frontline, and blue-collar roles where digital visibility is limited, we incorporate government datasets, localized labor information, and specialized partners to build a more complete and balanced view of workforce supply and demand.