Big Data fuels reward program Data Science Stories

Easynet runs real time analytical applications to allow for cross-selling and upselling in a customer reward program

For one of its biggest European customers, Easynet Global Services operates a Greenplum 1000 appliance for a customer reward program used by a large European retail chain. The customer needed a solution for the real time analysis of its shoppers’ spending patterns. This requires real time analysis at the point of sales in order to cross-sell and upsell with the help of the customer reward program.

Easynet Global Services is a worldwide provider of Managed Hosting, Managed Networks, Enterprise Cloud and Professional Services including MVM, IPVPN, connectivity, carrier services, and security solutions to international businesses. Easynet Global Services operates six international data centers and over 50 co-location facilities across Europe. These facilities offer carrier-grade services, providing a resilient, managed and secure environment. The datacenters are connected directly to Easynet’s backbone offering the best possible performance for customer applications.

To cater to the needs of its customer, Easynet Global Services performed an extensive market evaluation along with the customer. “In a competitive environment like the retail market, a good knowledge of customers’ spending patterns is essential,” Malte Gosau, Sales and Marketing Director for Easynet Central Europe, says.

“Retailers want to capture the profile of their customers and strive to influence their spending patterns by presenting them with tailored offerings that match their lifestyle.”
Malte Gosau, Sales and Marketing Director Easynet Central Europe

“This is where ‘big data’ comes in. The amount of data collected on spending behaviour of thousands of customers, combined with external data on promotions, weather conditions, time of the day,…. is enormous. If a rewards program wants to be effective, retailers need to be able to analyse all correlations as quickly as possible. That’s why they need both a stable hardware platform and the analytical software that can cater to these needs.”

“Big data applications offer us the opportunity to show off our core competencies: we can host and support these applications in a highly secured environment. Storage of the large volumes of data that are typical for big data is something we specialize in. We offer high availability and maximum uptime, which is especially required of real time analytical applications, along with binding Service Level Agreements. And of course, our datacenters are connected with large bandwidth. A big data application necessitates enough bandwidth to receive these vast amounts of data and to allow for real time queries on these data.”
To decide on what product to use, the end customer commissioned two Proof of Concepts, one with Greenplum at Easynet Global Services, another one directly with another vendor of business intelligence appliances.

The customer had previous experience with Greenplum products running on other hardware platforms, but had had technical issues with the hardware platform. Being able to work with hardware from EMC was perceived by Easynet as an assurance for technical excellence. “EMC is a long lasting partner of Easynet,” says Malte Gosau. “We worked closely with the customer, EMC and Greenplum in this case.  The Proof of Concept made us feel comfortable in taking over responsibility for this platform. It was very important for us to feel comfortable about it, as our performance is measured by a number of Service Level Agreements. We can only guarantee those for solutions that we fully support.”

Big data meets cloud computing

As a result of the proof of concept, the choice was made for Greenplum. The stability of the hardware platform was an important factor that influenced the decision, as well as the performance of the solution. Other aspects were the fault tolerance and the 24x7 availability. “The Greenplum DCA is a system that meets the reliability requirements of the most mission-critical enterprises,” says Gosau. The system offers multi-level, self-healing, fault tolerance which includes automated failover and fully online self-healing resynchronization and multiple levels of redundancy and integrity checking. As the solution requires real time analysis, it is very important to the customer to be able to perform upgrades while the system continues working. Technical issues can also be solved without downtime.

The choice for Greenplum was also in line with the recent Magic Quadrant Gartner published on Data Warehouse Database Management Systems (January 2011) in which Greenplum’s products are positioned in the most favourable upper right hand quadrant with excellent grades on both ‘Completeness of Vision’ and ‘Ability to Execute’.
The customer accesses the data over a secure internet connection. This way big data meets cloud computing in Easynet’s solution for this retail giant.

“Greenplum delivered us a full rack including blade servers, terabytes of storage and the analytical software,” says Gosau. “The Greenplum DCA is a purpose-built, open systems data appliance that architecturally integrates databases, server, and storage into a single, easy-to-implement system that is deployable and expandable in days. For us this was a very transparent solution, easy to integrate in the rest of our datacentre.” At the heart of the Greenplum Data Computing Appliance is the EMC Greenplum Database, with a shared-nothing massively parallel processing architecture that has been designed for business intelligence and analytical processing.

The solution was implemented in the summer of 2011 and both the end customer, its rewards program partner and Easynet are quite happy with their choice. “We are very satisfied with the kind of support that we have been getting from EMC and Greenplum. Together we were able to ensure a seamless operation, straight from the testing phase through to production. We are quite happy to take on other customers who want us to support and operate other Greenplum solutions,” Malte Gosau concludes.

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