SAN MATEO, CA -- (Marketwire) -- 07/29/09 -- Quantivo, the leader in on-demand Behavioral Analytics, today announced that its SaaS service is now hosted on Amazon Elastic Compute Cloud, also known as Amazon EC2. Quantivo changes the expectations of customer analytics by offering a high-performance, highly scalable and highly reliable computing infrastructure for a fraction of the cost of traditional advanced analytics solutions. Now any company or department can quickly answer the most difficult customer behavior questions in minutes and immediately capitalize on all sales, marketing and customer service opportunities.

"The need for speed and flexibility in customer analytics has been amplified by the proliferation of social media and the mobile web, shrinking the window of opportunity to capitalize on behavioral trends to just hours or even minutes," said Paul O'Leary, CTO at Quantivo. "To help companies keep up, we didn't just move traditional BI technology into the cloud. Instead, we combined our revolutionary analytics approach with a cloud-based architecture for an entirely new vision of Business Intelligence. Quantivo customers can throw billions of records into the cloud and begin analyzing their data in ways not previously possible and at a fraction of the TCO of tired relational data warehouse solutions based on decade old technologies."

Until now customer analytics has been affordable to only an elite group of companies that have the resources to support the high-cost, high-overhead computing power required to conduct superficial customer analysis. By combining Quantivo's unique Affinity Analytics technology with SaaS built upon Amazon EC2, companies can act on the latest customer behavioral patterns for as little as $5,000 per month.

"We're pleased that Quantivo can extend the benefits of their analytics tools to more businesses by leveraging Amazon Web Services," said Steve Rabuchin, General Manager of Developer Relations and Business Development, for Amazon. "AWS has been working to deliver powerful, cost-effective web infrastructure solutions to help create the type of innovation that Quantivo can now offer to their customers."

The performance and affordability of the Quantivo Behavioral Analytics solution is possible by leveraging the auto-scaling, elastic load balancing and service reliability of Amazon EC2.

--  Auto-Scaling - Automatically scale individual customer capacity up or
    down according to conditions that each customer defines. This ensures that
    the number of instances scales up seamlessly during demand spikes to
    maintain performance, and scales down automatically during demand lulls to
    minimize costs.

--  Elastic Load Balancing - Automatically distributes incoming
    application traffic across multiple instances. It enables users to achieve
    even greater fault tolerance and seamlessly provides the amount of load
    balancing capacity needed in response to incoming application traffic.
    Elastic Load Balancing detects unhealthy instances within a pool and
    automatically reroutes traffic to healthy instances until the unhealthy
    instances have been restored.

--  Service Reliability - The highly reliable and scalable technology
    infrastructure provided by AWS enables Quantivo to provide high
    availability Service Level Agreement commitments to all customers.
    

About Quantivo

Quantivo is pioneering a fundamentally new approach to Behavioral Analytics. For the first time, corporate decision-makers can quickly uncover behavioral patterns across any aspect of their customer interactions and act on these timely insights to increase customer acquisition, retention, up-sells, cross-sells and web monetization. By combining large-scale analytics with rapid SaaS delivery, Quantivo is the only company that puts impactful revenue-generating insights directly into the hands of a wide set of executives and analysts for applications such as and loyalty analysis, merchandising and marketing optimization and online analytics. For more information, visit www.quantivo.com.

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