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

~400000

Employees

$106B

Revenue

Retail

Industry

Minneapolis

Headquarters

About the organization

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.

The Core Challenges

Rising Attrition Pressure
Finance talent demand outpaced supply in key hubs, increasing competition and attrition.
Unbalanced Hub Selection
Location decisions required balancing talent depth, cost, stability, and growth potential.
Uneven Digital Adoption
Digital and automation skills were becoming critical, but adoption varied widely across roles and cities.
Undefined SSC Strategy
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
The image is for representational purpose only.
01
Finance Job Role Taxonomy
Defined a clear, standardized taxonomy across eight core finance functions to consistently evaluate shared services roles and workforce mix.
02
Skill Cluster & Digital Readiness Analysis
Identified core, digital, and emerging skills across finance roles, highlighting growing demand for automation, analytics, and finance platforms.
03
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.
04
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.
6,100+​
Locations
4M+​
Career Paths​
1.7M+​
Peer Group Companies​
28K+
Skills
3,500+​
Roles
1B+​
Professionals​
55K+​
Universities​
1B+​
Job Descriptions​
200K+​
Courses​
180K+​
Technology Solutions​
1.6M+​
Account Priorities​
Labor Market Data Contextualized with Business Intelligence for Every Decision

Analyzing 1B+ data points
daily from 100K+ sources

6,100+​
Locations
4M+​
Career Paths​
1.7M+​
Peer Group Companies​
28K+
Skills
3,500+​
Roles
1B+​
Professionals​
55K+​
Universities​
1B+​
Job Descriptions​
200K+​
Courses​
180K+​
Technology Solutions​
1.6M+​
Account Priorities​
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Ethical AI, Backed by Global Standards

Draup, an AI-first company, upholds global standards like SOC 2, GDPR, and ISO 27001. As an EAIGG member, we audit for bias and build trustworthy AI.

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.

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.

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.

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.

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.

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.

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.

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