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Canada’s Iconic Food Chain Uses Big Data to Boost Sales and Feed Hungry Customers
Technology Category
- Analytics & Modeling - Big Data Analytics
- Analytics & Modeling - Real Time Analytics
Applicable Industries
- Food & Beverage
Applicable Functions
- Business Operation
- Sales & Marketing
Use Cases
- Demand Planning & Forecasting
- Inventory Management
- Supply Chain Visibility
Services
- Data Science Services
The Challenge
Pizza Pizza, a Canadian food chain, was struggling with managing and making sense of the vast amount of data generated from its operations. The company operates over 750 locations and fulfills over 20 million orders annually, generating a database with billions of fields. The company used to rely on static reports and spreadsheets to analyze its operations, which lacked flexibility and interactivity. The reports were generated daily and if a different perspective was needed, it would take days or weeks for IT to generate a new report. The company was unable to access real-time data and lacked the ability to drill down into the details of its operations.
About The Customer
Pizza Pizza is a Canadian food chain that has been in business for over 50 years. The company opened its first location in Toronto in 1967 and has since expanded coast to coast, operating over 750 locations. The company fulfills over 20 million orders annually, generating a vast amount of data from its operations. Pizza Pizza operates several data and call centres that process orders, a centralized system for all customer information, a customer satisfaction survey operated by a third party, Google Analytics for its digital and ecommerce channels, a complex ERP system, and a network of regional warehouses that supply its franchises.
The Solution
Pizza Pizza decided to adopt a business intelligence (BI) tool to better manage and analyze its data. The company chose Qlik, a BI tool that provides real-time data and interactive dashboards. The company built a knowledge management strategy that incorporated demographic and behavioural data about customers and consumers. They created a business intelligence architecture that consolidated data sources, provided point-and-click data mining, data analysis, and KPI monitoring functionalities. This allowed the company to move from static reports and spreadsheets to real-time data and dashboards. The company built several dashboards for different aspects of its operations, including inventory analysis, call centre performance, social media initiatives, and delivery analysis.
Operational Impact
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