MIG Fashions Higher Profits with Blue Yonder’s Pricing Solution
Company Size
1,000+
Region
- Europe
Country
- Poland
Product
- Blue Yonder’s lifecycle pricing solution
Tech Stack
- Artificial Intelligence
Implementation Scale
- Enterprise-wide Deployment
Impact Metrics
- Productivity Improvements
- Revenue Growth
Technology Category
- Analytics & Modeling - Predictive Analytics
Applicable Industries
- Retail
Applicable Functions
- Sales & Marketing
Use Cases
- Demand Planning & Forecasting
Services
- Data Science Services
About The Customer
Marketing Investment Group (MIG) is a leading retailer of footwear and clothing in Central and Eastern Europe. The company has been in operation for 30 years and has more than 400 stores and over 20 ecommerce platforms. MIG’s complex sales model includes multiple retail brands, including regular-price stores and outlets, in 11 countries. The company was struggling to optimize pricing across all its channels, regions, products, and brands using manual methods and consumer-grade tools.
The Challenge
Marketing Investment Group (MIG), a leading retailer of footwear and clothing in Central and Eastern Europe, was struggling with the complexity of optimally pricing thousands of items across multiple countries, currencies, and channels. The company operates more than 400 stores and over 20 ecommerce platforms, with multiple retail brands, including regular-price stores and outlets, in 11 countries. The manual methods and consumer-grade tools they were using were not sufficient to optimize pricing across all these variables. The process was complex, tedious, and error-prone, leading to a lot of markdowns and inability to change prices frequently.
The Solution
To automate the pricing process, drive sales, and optimize margins, MIG partnered with Blue Yonder to implement its lifecycle pricing solution, enabled by artificial intelligence (AI). The solution ingests diverse data like sales history, past and future promotions, local demand, and current stock levels, then defines optimal pricing proposals. MIG can review these proposals and see, in advance, how they will impact consumer buying behaviors, sales, and margins. The solution leverages AI to support a faster, more granular decision-making process than humans are capable of. It translates data into profitable pricing plans, with the goal of maximizing revenues and margins while minimizing excess inventory. The solution considers consumer buying behavior, internal sales data, and external data feeds such as weather when making its calculations.
Operational Impact
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