Lotte Department Store scales retail analytics with Strategy AI

Lotte’s Transformation at a Glance

400% increase in advanced analytics usage across business teams

9,500+ AI‑powered analysis cases completed in the first six months

90% efficiency improvement, delivering insights 10× faster

8 purpose‑built AI agents supporting specific business scenarios

The Challenge

Lotte, one of the largest department store chains across South Korea and Southeast Asia, faced three core challenges limiting the impact of its data analytics.

First, employees worked with varying levels of familiarity with underlying data structures, making it difficult for non‑technical users to confidently make merchandising decisions.

Second, analytics relied heavily on internal data, limiting visibility into broader market positioning. Without external context, strategic decisions lacked a clear view of customer behavior and competitive trends.

Finally, data accessibility remained inconsistent. While analysts could navigate complex systems, business users struggled to get timely answers to strategic questions without technical support.

Together, these challenges created a gap between data availability and decision‑making across the business.

"Instead of starting with technology, we started with the needs on the ground. We talked to business users about their pain points, then found Strategy as the solution.”

Injun Hwang

Technology Manager, Lotte Shopping

The Strategy: Scaling AI‑Driven Analytics Across Teams

The Foundation

To address these challenges, Lotte integrated Strategy’s AI agents into its analytics environment. Built on a governed semantic layer, these agents could consistently apply shared definitions, metrics, and business context across every department.

The Solution

Lotte's metadata‑driven business logic enabled the AI agents to generate governed, consistent responses, significantly reducing the risk of hallucination. Both technical and non‑technical employees could now access complex data simply by asking questions in their native language, removing the dependency on specialized analytics skills.

The Differentiation

Strategy’s AI agents also allowed Lotte to expand beyond internal data by integrating external sources such as credit‑card transactions and public market data. This broader context helped teams better understand customer behavior, refine merchandising strategies, and strengthen competitive positioning across locations.

The Application

Today, Lotte has improved data accessibility for non‑technical users, streamlined operational efficiency across more than 300 dashboards, and scaled strategic governance to drive widespread adoption. With Strategy AI, Lotte is making complex data accessible and actionable for business users across the organization.

"Having an OLAP-based, metadata-driven architecture gives us the ability to overcome the biggest issue of generative BI, which is hallucination.”

Injun Hwang

Technology Manager, Lotte Shopping

Faster Insights, Broader Adoption, Stronger Decisions

The Impact

Six months after implementation, Lotte saw advanced analytics usage increase by 4×. Individual productivity improved by nearly 90%, as tasks that once required deep technical expertise were now handled instantly through natural‑language queries powered by AI.

The Future

With AI agents operating on a governed data foundation, Lotte now delivers faster, more consistent access to insights. The organization is shifting from fragmented analysis to more aligned, enterprise‑wide decision‑making.

The Long-Term Vision

LOTTE plans to continue expanding its AI analytics capabilities by incorporating more external data sources and developing specialized agents. The goal is to maximize competitive advantage through deeper intelligence, broader automation, and smarter decision support across 54+ locations.