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A global fast-food chain with more than 30,000 restaurants in more than 100 countries, and serving 68 million customers each day, had plans to expand in China, its second-largest and fastest-growing market. Already operating more than 3,000 restaurants in the country and employing more than 100,000 people, The Brand was looking for a way to maximize profit potential for both existing and new restaurant locations. The key to success for The Brand would be to identify opportunities for creating specialized promotions for specific regions and locations, and delivering those products with fast, consistent service–faster than it was already providing with its ready-to-eat menu and combination meals. How would The Brand do this? By leveraging the data it had accumulated from its fully digitized operations and turning that massive store of data into actionable intelligence.
Because of restrictions enacted in response to the COVID-19 pandemic, fast-food delivery orders became common for home-bound customers in China. As a result, industry reports show that the number of restaurants offering their menus through online delivery platforms in China spiked to new heights. Research firm Statista shows the market for China's online food delivery business in 2020 exceeded RMB 664.62 billion, and the number of active users reached 468 million.
Fast-food delivery had also become an important sales channel for The Brand, processing around 60,000 delivery orders per-day, and generating more than one million lines of detailed data. The Brand wanted to use that data to find a competitive edge and improve their customers’ experience. But any innovations would need to be based on precise business intelligence generated through timely and accurate data analysis.The Brand’s data scientists use Oracle Business Intelligence Enterprise Edition (OBIEE) + Impala to support self-service, multidimensional data analysis. With this solution, data analysts don’t have to worry about the complexity of their underlying data when building multidimensional models and performing ad hoc queries through a web interface.However, the growing volume of data–and a corresponding increased demand for analytics–means The Brand’s data scientists face new challenges. For example, The Brand wants to track its daily sales volume as well as raw materials and packaging costs. To get this information, The Brand’s data scientists need to read and analyze the delivery order data from the day before. But when using its existing technologies during peak query hours (9:00am to 10:00am), the analytics systems slowed to a crawl and often returned time-out errors. Even during non-peak times, when The Brand’s analysts attempted multidimensional queries, response times were protracted.Using traditional approaches and legacy systems, The Brand faced the following challenges:
To overcome these challenges, The Brand turned to Kyligence to integrate data from different sources and build a multidimensional model that includes many key business metrics. What’s more, the Kyligence unified semantic layer helps The Brand to unify business logic across the company. With Kyligence, The Brand was finally able to achieve self-service, multidimensional analysis of detailed data from hundreds-of-millions of historical orders. Now they can run analysis from dimensions like restaurants, products, and channels, and analysis efficiency is greatly improved. Some key advantages gained by The Brand by adopting Kyligence Intelligent OLAP:
The Kyligence solution architecture is as follows:
Kyligence Intelligent OLAP + unified semantic layer analytics provides a unified query entry point and solves problems like inconsistent data processing, and logic and query timeout. With Kyligence, The Brand now gets timely information regarding the profitability of delivery orders, and at a lower TCO than with its legacy systems. That means The Brand can make faster, better-informed decisions, and tailor their marketing strategies based on more precise business intelligence.
According to The Brand’s data product team, with Kyligence multidimensional models and a unified semantic layer, their analysis experience is significantly improved, and they can better serve other teams with their analytics capabilities. With Kyligence Intelligent OLAP in place and delivering significant improvements, The Brand hopes to make even more gains by working with Kyligence in more areas, such as unified query routing and ad hoc analytics.
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