MUJI Uses Treasure Data to Merge Online and Offline Behavior Data to Improve Loyalty, In-Store Visits

MUJI Uses Treasure Data to Merge Online and Offline Behavior Data to Improve Loyalty, In-Store Visits

Treasure Data sees data warehouse augmentation as an emerging use case for its big data managed service

Mountain View, Calif. — May 7, 2014 — Global retailer Ryohin Keikaku Co., Ltd., better known to shoppers around the world as MUJI, deployed Treasure Data to support a new loyalty program, analyze customer behavior on its websites and ultimately, increase in-store visits. With its new MUJI passport mobile loyalty app, brick and mortar locations and a thriving online store, MUJI has customer data from many channels. MUJI deployed Treasure Data, a cloud-based managed service for big data, to store and analyze massive volumes of mobile and web clickstream data coming from its Japanese loyalty app and online store. With Treasure Data, MUJI processes logs to do behavioral analysis on 4.3 million registered web users and 1.4 million MUJI passport mobile app users. MUJI then aggregates and exports a valuable subset of this clickstream data and integrates it with other data sources in Amazon Redshift. MUJI was able to add these new capabilities within weeks without hiring additional resources or purchasing additional hardware. With its improved access to the mobile and clickstream data, MUJI is able to send data-driven coupons and “shopping points” to its customers via its loyalty app, connecting the dots between online browsing and offline buying. As a result, MUJI’s in-store visits grew within weeks, a key metric for success with the program.

“We care deeply about providing our shoppers with the best experience and we needed an efficient solution to analyze clickstream data and provide targeted promotions on our new MUJI passport loyalty app. Treasure Data was the most cost-effective solution that allowed us to deploy our program quickly and leverage the resources and systems we already had, while capturing and processing new types of big data that were critical to our new programs,” said Mr. Takashi Okutani, General Manager of Web Business Division at MUJI.

“MUJI wanted a more complete view of its customers to target promotions with their new loyalty app. Treasure Data helped them achieve that goal by bringing to life new sources of customer data. The Treasure Data solution can complement existing infrastructures quickly and easily, taking the friction out of a big data implementation and alleviating pain for IT and business analysts who just want a complete picture of their data,” said Hiro Yoshikawa, CEO of Treasure Data.

Augment the Data Warehouse

MUJI quickly realized that its incoming data was outpacing its data warehouse storage on every metric. Like MUJI, Treasure Data has seen companies use its cloud-based managed service as an extension of existing data warehouse implementations. The data warehouse model is a critical piece of infrastructure, but is constrained as a big data solution by its expense, complexity and specialized skills required to manage the system. In a 2014 big data survey by Enterprise Management Associates (EMA), 32 percent of respondents, the majority in the category, said they are deploying cloud-based implementations because current infrastructure could not scale economically to meet their company’s needs.

There is often great value in combining big data with traditional data warehouse data. For example, customer clickstream or behavior data is significantly enriched with transactional information about their purchase history. However, clickstream data, like other types of streaming big data, is created very rapidly, adds up quickly and can be difficult to even ingest. It requires preliminary staging, aggregation and analysis to prepare smaller, structured subsets of the data that can be added into the data warehouse for combined analytics. In the same EMA survey, 38 percent of those surveyed cited the utilization of streaming data as the main use case for implementing a cloud-based technology to augment the data warehouse.

Now, with Treasure Data, you can cost-effectively, quickly and easily add big data capabilities to any data warehouse. The company recently released a new capability making it easy to export big data aggregates and query results from Treasure Data directly to the Amazon Redshift cloud data warehouse.

Treasure Data is the first cloud-based service to offer a big data managed service solution that complements the data warehouse and leverages the existing SQL ecosystem. Constraints around expense, speed and skillsets are absolved by Treasure Data, as the solution can be implemented in weeks not months, works alongside existing data warehouse implementations and other business intelligence tools, and leverages existing SQL knowledge without requiring additional Hadoop or MapReduce skills to process big data.

“Raw big data is unlike any other source; adding it directly to a data warehouse can introduce new challenges and disrupt the ecosystem. The data warehouse isn’t going away, but cloud-based solutions that augment and extend its capabilities are effective and helpful when combining both structured and unstructured data,” said Shawn Rogers, vice president of research, Enterprise Management Associates.

About Treasure Data

Treasure Data was founded in 2011, with the mission of building the first end-to-end managed service in the cloud for data acquisition, storage and analysis. Since the service launched in 2012, thousands use its free Starter version and its 100+ corporate customers include Toyota, NTT Docomo, Gree, Viki and several Global Fortune 500 companies. In 2014, Gartner selected Treasure Data for the “Cool Vendors in Big Data” report.