Top Loyalty Provider Adds Customers by Performing Transactions in Real Time
Product
- BigMemory Max
Tech Stack
- Java
- Hibernate
Implementation Scale
- Enterprise-wide Deployment
Impact Metrics
- Productivity Improvements
- Customer Satisfaction
Technology Category
- Application Infrastructure & Middleware - Data Exchange & Integration
Applicable Industries
- Retail
Applicable Functions
- Sales & Marketing
Use Cases
- Real-Time Location System (RTLS)
Services
- System Integration
About The Customer
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.
The Challenge
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.
The Solution
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.
Operational Impact
Quantitative Benefit
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