SAP, Vendavo Deepen Partnership For Profit Optimisation Solutions

SAP AG and Vendavo, the provider of enterprise solutions to analyse, negotiate and close profitable deals, today announced a significant expansion of the development road map and partnership based on the SAP HANA platform aimed at providing customers with new real-time profit optimisation solutions.

Vendavo plans to run the next release of its Profit Analyzer application on the SAP HANA database. The solution will harness companies’ enterprise Big Data to identify opportunities to help sell more profitably. The power of SAP HANA will help make the solution a more powerful margin analytics application designed to give pricing analysts guided insight into high value profit improvement opportunities and is planned to offer superior performance and lower total cost of ownership (TCO) for both existing and new customers

In this Profit Analyzer solution, planned for release in early 2014, use of SAP HANA is planned to replace Vendavo’s current proprietary in-memory technology, which runs on a traditional Oracle or DB2 database. Vendavo’s integration of SAP HANA has been facilitated by collaboration with the SAP Co-Innovation Lab, where SAP and Vendavo have worked together to deliver the industry’s first profit optimisation solution integrated with SAP HANA. Vendavo Profit Analyzer joins previously released Profit Finder as the first two of Vendavo’s profitable selling solutions to transition to run on the real-time SAP HANA platform.

“Our experience working with SAP and developing on the SAP HANA platform has greatly exceeded our expectations, giving us confidence in transitioning our whole enterprise product line to running on SAP HANA,” said Neil Lustig, Chief Executive Officer and President, Vendavo. “Customer response has been unprecedented and we expect rapid uptake due to the benefits SAP HANA delivers, such as lower total cost of ownership, faster data uploads, greater accessibility to enterprise Big Data, lightning-fast performance and the ability to easily scale to billions of rows of data.”