Skip to content
At LC Waikiki, our guiding philosophy is simple: “Everyone deserves to dress well.” What began in France in 1988 has transformed into a massive global retail footprint. Today, we bring accessible fashion to customers through more than 1,200 stores across 59 countries, powered by a family of over 57,000 employees.
With such growth, our scale has also introduced operational complexity. In an inflationary retail environment, prioritizing unit volume alone is not a viable path to sustainable growth; rather, value management, margin defense, and profitability have become the most critical performance indicators. To address this, our CEO set a mandate: prevent global price imbalances and protect margins using Artificial Intelligence.
Our response was the development of LCWIQ, our proprietary AI-powered price intelligence and anomaly-detection engine. At the latest Dynamic Pricing Community event, we showed how we built it, the academic science behind it, and the lessons we learned about scaling pricing globally. Below are the key takeaways.
Why plentiful data breeds pricing errors
A common misconception in modern retail is that pricing errors occur because teams don’t have enough data. In reality, the opposite is true. Retailers are drowning in data.
Historically, we set initial prices using a foundational cost-plus formula. Once these baseline prices were set, our Store Product Managers (SPMs) had to manually adjust them to account for local purchasing power, regional competitor activity, and shifting margin targets.
With over 50,000 active products moving through vastly different supply chains, manual filtering was a bottleneck. When you are processing millions of data points manually, anomalies inevitably slip through:
- Underpriced items: High-demand, basic goods priced far below their local category average, resulting in silent margin loss.
- Below-cost sales: Items retailing at negative margins due to sudden, uncoordinated spikes in regional customs, freight, or local exchange rates.
The challenge is building an automated filter capable of scanning thousands of products and markets to isolate these anomalies before they affect business performance.
The science of the LCWIQ Engine
To build a system our business teams could actually trust, we bypassed “black-box” forecasting in favor of a robust framework built on four peer-reviewed academic methodologies:
Academic methodology | How we apply it to pricing |
Multi-dimensional anomaly detection (Isolation Forest – Liu et al., 2008) | We analyze price, cost, stock levels, and sales velocity simultaneously to isolate multi-variable outliers. |
Local density-based LOF (LOF – Breunig et al., 2000) | We identify localized anomalies across distinct product classes that carry vastly different standard price ranges. |
Fast & consistent price prediction (LightGBM – Ke et al., 2017) | Operates 20x faster than XGBoost with the exact same accuracy, allowing us to re-price thousands of SKUs in seconds. |
Ensemble model voting (Outlier Ensembles – Aggarwal, 2013) | We combine four distinct model families in a voting system to catch hidden anomalies that a single model might miss. |
The five-stage processing pipeline
To convert these theories into daily, automated actions, LCWIQ processes our global assortment through a highly optimized pipeline in less than a minute:
- Data preparation: Cleanses raw retail data, handles missing values, and unifies stock, sales, and inventory cover metrics.
- Parallel anomaly detection: Runs four models (Robust Z-Score, Isolation Forest, LOF, and an Autoencoder network) simultaneously.
- Ensemble voting & rule engine: Classifies anomalies by severity based on how many models flag them, while enforcing hard constraints (e.g., automatically flagging any price below landed cost).
- Price optimization: Leverages LightGBM to establish a baseline price, applies inventory cover correction (raising prices to slow down stockouts, or dropping prices to clear slow-movers), and rounds to local psychological price bands.
- Interactive output: Delivers visual dashboards, simulators, and exportable reports for immediate action.
Using LLMs for plain-language explainability
One of the quickest ways to stall an AI initiative is a lack of trust. If a pricing manager does not understand why an algorithm flagged an item or recommended a specific price, they will simply ignore it.
To bridge the gap between complex mathematical models and real-world execution, we integrated a Large Language Model (LLM) AI Assistant directly into the LCWIQ platform.
Instead of forcing pricing managers to parse raw databases, they can query the data using plain, natural language:
User: “Which products are currently selling below cost?”
AI: “I found several. The most critical is a Women’s Knit Sweater retailing at a -3.8% margin.
This was flagged because localized freight multipliers spiked last week.
I recommend correcting the price to $12.90, restoring a +36.4% margin.”
This natural language interface instantly translates the ensemble model’s outputs into explainable business logic. It tells the manager exactly why an anomaly exists, whether due to an abnormal sales-to-stock cover ratio, high landed costs, or a mismatch in localized exchange rates.
While we currently utilize cost-effective cloud APIs, our long-term roadmap focuses on transitioning to locally hosted, open-source models to ensure absolute data protection for our proprietary retail and financial metrics.
AI as a Decision-Support Tool
A common point of discussion in pricing circles is the fear of handing complete autonomy to an algorithm. At LC Waikiki, our philosophy is clear: AI should act as a powerful co-pilot, but the human remains the pilot.
Our Store Product Managers (SPMs) are responsible for pricing across multiple countries. They possess localized market context that an algorithm simply cannot see, such as:
- Geopolitical volatility: Sudden economic shifts or trade disruptions in regions like the GCC.
- Localized competitor entrances: A discount fashion rival opening ten physical stores in a specific region, requiring us to temporarily accept lower margins to protect market share.
- Inventory lifecycles: Deciding to deliberately take a margin hit on terminal stock to clear warehouse space for the next season’s arrivals.
To support this, LCWIQ features a What-If Simulation dashboard. If an SPM disagrees with an AI recommendation, they can manually simulate alternative price points to instantly view the impact, the nearest psychological price bands, and projected weekly revenue before committing to a change.
Two examples of human-AI collaboration
- The under-priced men’s basic t-shirt: Flagged by our Z-score and Isolation Forest algorithms, a high-volume basic t-shirt was retailing at an abnormally low price of $4.99 with a weak 27.9% margin. The AI proposed a corrected price of $7.99. The SPM approved the change, instantly restoring the margin to 54.9%.
- The below-cost women’s knit sweater: Our rule engine flagged a knit sweater selling at a negative margin of -3.8% ($7.90 retail price) due to an uncoordinated landed cost update. The system recommended $12.90. The SPM validated the cost error, approved the recommendation, and restored a healthy 36.4% margin while keeping the price highly competitive.
Finding the right governance balance
Scaling pricing across 60 distinct countries is not merely a technical challenge; it is a governance challenge.
To prevent conflicting strategies across diverse regions, organizations must establish clear operational boundaries. If pricing roles between corporate headquarters, regional merchandisers, and local store managers are ambiguous, results will quickly suffer.
Our governance model strikes a balance between centralized control and localized needs:
- Centralized AI standards: We utilize a centralized team and the LCWIQ platform to define baseline pricing strategies, establish minimum-margin boundaries, and clean incoming global data.
- Localized strategic autonomy: Our SPMs use their regional expertise to adjust these boundaries. Meanwhile, local commercial store managers act as our eyes on the ground, reporting physical competitor entries or localized trends that feed directly back into our strategy.
By designing our AI to act as a decision-support tool rather than an autonomous black box, we have successfully scaled our pricing operations to support global expansion while aggressively defending our regional margins.
Key takeaways from the DPC session
- It’s not a data problem; it’s a filtering problem
Retailers rarely suffer from a lack of data. The real challenge is isolating margin-eroding anomalies across thousands of products and markets, amid constant price updates, before they impact the bottom line.
- AI is a decision-support tool, not an autonomous driver
The most effective systems detect errors and propose optimal price points, but human pricing managers must retain final strategic control over the ultimate decision.
- LLMs translate math into plain logic
Large Language Models are shifting from experimental tools to critical drivers of explainability, turning complex mathematical anomalies into intuitive, trusted insights.
- Global scale requires local guardrails
Scaling a pricing strategy across dozens of diverse countries requires more than raw algorithms; success lies in finding the right balance between corporate governance and localized market expertise.
About the authors
Emre Aslan
Regional Product Leader, LC Waikiki
With an academic foundation in Economics, Emre has spent his career navigating complex global retail environments, including professional chapters in the United States and Dubai before joining LC Waikiki in 2019. As a Regional Product Leader, he oversees allocation, planning, and in-season commercial actions across multiple markets. Emre is highly passionate about pioneering AI-driven retail innovations, championing internal projects from sales forecasting and stock clustering to smart price optimization.
Remzi Demirkıran
Store Product Manager, LC Waikiki
Remzi works at the exact intersection of commercial retail strategy and advanced data science. After graduating as the top student in his Material Science and Engineering program, he went on to complete both an MBA and an MSc in AI Engineering. He is currently continuing his academic journey with a PhD in Artificial Intelligence. In his role as Store Product Manager, Remzi focuses on product management and designing the next generation of AI-driven pricing engines to protect global margins.
