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Doing More by Doing Less Lessons Learned in Customer Insight
Technology Category
- Analytics & Modeling - Big Data Analytics
- Application Infrastructure & Middleware - Data Visualization
Applicable Industries
- Retail
Applicable Functions
- Sales & Marketing
- Business Operation
Use Cases
- Inventory Management
- Supply Chain Visibility
Services
- Data Science Services
- System Integration
The Challenge
GameStop, a $9B Specialty Retailer with operations in 14 countries, was facing several challenges. The demand for analysis was greater than the throughput, and the complexity of analysis requests or business questions was increasing. IT resources were constrained for reporting and data manipulation. The company needed to increase throughput, capability, and decrease dependency. They needed to reduce the need for complex coding (SQL), reduce time spent on repetitive tasks, and share analysis products and procedures across the team. They also wanted to focus on the problem, not manipulating data, provide insight versus analysis results, and increase analysis capability and skillset. Lastly, they wanted to remove IT from basic tasks such as reporting and data loading.
About The Customer
GameStop is a family of retail brands that makes the most popular technologies affordable and simple. They are a dominant leader in the global video game category with over 6,600 video game and technology brand stores. They operate in 14 countries and are a Fortune 500 & S&P 500 company. Their operations span across video games, publishing, digital/mobile gaming, and wireless technology. They have a PowerUp Rewards program that engages customers, with over 30 million US members, making up 70%+ of their sales. The company's team consists of customer analytics and direct channel management, with partners and clients across marketing, finance, the executive team, real estate, and vendors.
The Solution
GameStop implemented several solutions to address their challenges. They automated reporting using Tableau, reducing the time spent on reporting from half a day to less than 1.5 hours. They also automated routine tasks and contingency/succession planning using Alteryx apps. This included franchise and affinity SKU finder, console ownership definition & export, model export, audience file export, and zip code finder. They reduced dependency on IT to load and join external data. They loaded external data without IT assistance and matched device and member data in Alteryx. They also utilized the Market Basket function to analyze multiple combinations versus only frequency. They focused on providing business insight, new analysis capabilities, and interpreting and delivering implications.
Operational Impact
Quantitative Benefit
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