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Big-data analytics boost retail revenues and accelerates suggestions via e-commerce

Read how Accenture big-data analytics helped boost a large global retailers's revenues by accelerating suggestions via e-commerce channels


A retailer sought big data analytics to improve its recommendation engine for sales through multiple channels, including mobile devices. Accenture project teams worked to improve the speed and effectiveness of systems that suggest products and services most likely to appeal to individual customers. After implementation in one channel, the analytics-powered solution helped the retailer enjoy a multimillion-dollar boost in e-commerce revenues in only three months.

This large global retailer operates a robust website for e-commerce, offering products and related services through online and mobile channels, with product pickup available at physical store locations.

Consumers have many choices, and e-commerce systems must provide appealing suggestions before impatient customers defect to competing websites. Due to increasing data volumes, diverse information sources and complex processing requirements, the retailer struggled to deliver speedy online computing.

Accenture previously assisted the client with application development and support, as well as with several other projects. Leaders of the retail company asked Accenture to start developing a solution that could optimize e-commerce results in line with high performance in the digital age.


Being familiar with the client’s IT systems, Accenture quickly identified underused features and began to design a big data solution to distribute data across multiple systems to enable faster performance.

With huge volumes of data to be processed in real time, Accenture’s ongoing work provides data services and support for more than 40 relational source systems, more than a hundred daily extracts and an approximately 100-terabyte data warehouse. The warehouse supports more than 10,000 daily batch jobs and tens of thousands of users.

Members of the Accenture project team experienced in machine learning and data infrastructure built the architecture for the recommendations engine. A web programming team worked simultaneously to integrate the data infrastructure for an end-to-end solution.


In the first three months after implementing the solution in the first e-commerce channel, this global retail giant has seen the effectiveness of its recommendations result in a multimillion-dollar boost in revenues. The client has gained greater capacity to track data at an accelerated daily rate that exceeds 100 gigabytes.

Solution highlights include recommendations as a service layer and log processing, along with large-scale machine learning for data services. The automated system incorporates new data continuously to keep refining the quality of recommendations.

As implementation continues, the client will be able to compute with a much larger dataset, and enhance the recommendation engine for performance at scale and improved business outcomes. The big-data solution builds on the success of prior Accenture work for the client, such as expanded website functionality and enhancement of web channels to increase sales, all of which are aligned with high performance.

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