Top Loyalty Provider Adds Customers by Performing Transactions in Real Time
产品
- BigMemory Max
技术栈
- Java
- Hibernate
实施规模
- Enterprise-wide Deployment
影响指标
- Productivity Improvements
- Customer Satisfaction
技术
- 应用基础设施与中间件 - 数据交换与集成
适用行业
- 零售
适用功能
- 销售与市场营销
用例
- 实时定位系统 (RTLS)
服务
- 系统集成
关于客户
The customer is a top loyalty program provider in the marketing services and loyalty industry. They communicate with retailers through a point-of-sale application that manages activities such as adding points to a customer’s account, subtracting redemptions, and presenting relevant marketing offers — all in real time while the customer waits. The company was looking to win new business from larger retailers, who required the company to commit to an aggressive end-to-end Service Level Agreement (SLA) of 500 milliseconds for every transaction.
挑战
The company, a top loyalty program provider, was facing a challenge in meeting the aggressive end-to-end Service Level Agreement (SLA) of 500 milliseconds for every transaction, as demanded by large retail prospects. The company's data center took an average of 20 percent longer than that — 600 milliseconds — just to query upwards of 32GB of customer data in a central, disk-bound database. Furthermore, Java-related garbage collection pauses caused unexpected spikes in response times that would have resulted in financial penalties for failure to meet the SLA, and in unhappy customers. In order to win new business from larger retailers, the company realized it had to move its data into fast machine memory.
解决方案
The company considered Terracotta BigMemory and Oracle Coherence as options for achieving its performance and scale targets. After a successful proof-of-concept implementation, the company chose Terracotta for its full support for the Ehcache API, better Hibernate support, simpler deployment, a smaller server footprint, and motivated, knowledgeable engineers. The company brought the first retailer into production with all 32 GB of customer information in BigMemory, and immediately experienced an 83 percent drop in data access times — from 600 to 100 milliseconds. At the same time, transaction throughput jumped by a whopping 400 percent. Garbage collection is no longer an issue, allowing the company to meet the 500 millisecond end-to-end SLA demands for the first time.
运营影响
数量效益
Case Study missing?
Start adding your own!
Register with your work email and create a new case study profile for your business.
相关案例.
Case Study
Improving Production Line Efficiency with Ethernet Micro RTU Controller
Moxa was asked to provide a connectivity solution for one of the world's leading cosmetics companies. This multinational corporation, with retail presence in 130 countries, 23 global braches, and over 66,000 employees, sought to improve the efficiency of their production process by migrating from manual monitoring to an automatic productivity monitoring system. The production line was being monitored by ABB Real-TPI, a factory information system that offers data collection and analysis to improve plant efficiency. Due to software limitations, the customer needed an OPC server and a corresponding I/O solution to collect data from additional sensor devices for the Real-TPI system. The goal is to enable the factory information system to more thoroughly collect data from every corner of the production line. This will improve its ability to measure Overall Equipment Effectiveness (OEE) and translate into increased production efficiencies. System Requirements • Instant status updates while still consuming minimal bandwidth to relieve strain on limited factory networks • Interoperable with ABB Real-TPI • Small form factor appropriate for deployment where space is scarce • Remote software management and configuration to simplify operations
Case Study
How Sirqul’s IoT Platform is Crafting Carrefour’s New In-Store Experiences
Carrefour Taiwan’s goal is to be completely digital by end of 2018. Out-dated manual methods for analysis and assumptions limited Carrefour’s ability to change the customer experience and were void of real-time decision-making capabilities. Rather than relying solely on sales data, assumptions, and disparate systems, Carrefour Taiwan’s CEO led an initiative to find a connected IoT solution that could give the team the ability to make real-time changes and more informed decisions. Prior to implementing, Carrefour struggled to address their conversion rates and did not have the proper insights into the customer decision-making process nor how to make an immediate impact without losing customer confidence.
Case Study
Digital Retail Security Solutions
Sennco wanted to help its retail customers increase sales and profits by developing an innovative alarm system as opposed to conventional connected alarms that are permanently tethered to display products. These traditional security systems were cumbersome and intrusive to the customer shopping experience. Additionally, they provided no useful data or analytics.
Case Study
Ensures Cold Milk in Your Supermarket
As of 2014, AK-Centralen has over 1,500 Danish supermarkets equipped, and utilizes 16 operators, and is open 24 hours a day, 365 days a year. AK-Centralen needed the ability to monitor the cooling alarms from around the country, 24 hours a day, 365 days a year. Each and every time the door to a milk cooler or a freezer does not close properly, an alarm goes off on a computer screen in a control building in southwestern Odense. This type of alarm will go off approximately 140,000 times per year, equating to roughly 400 alarms in a 24-hour period. Should an alarm go off, then there is only a limited amount of time to act before dairy products or frozen pizza must be disposed of, and this type of waste can quickly start to cost a supermarket a great deal of money.
Case Study
Supermarket Energy Savings
The client had previously deployed a one-meter-per-store monitoring program. Given the manner in which energy consumption changes with external temperature, hour of the day, day of week and month of year, a single meter solution lacked the ability to detect the difference between a true problem and a changing store environment. Most importantly, a single meter solution could never identify root cause of energy consumption changes. This approach never reduced the number of truck-rolls or man-hours required to find and resolve issues.
