Causata Launches Customer Interaction Platform With $4.5M From Accel Partners

Accel Partners has poured $4.5 million into Causata, a San Francisco-based software startup that provides tools companies can use to optimize customer experience and business results.

The Series A round actually closed back in April this year, but the name of the investor has only now been made public through a regulatory filing, reports peHUB. Judging by messages posted this morning on the company’s Twitter account, the software startup has only now gone public with its website and offering.

Causata markets a multichannel customer interaction platform that intends to enable companies to optimize customer experience and business results simultaneously, at any touch point, in real-time. Causata’s software essentially structures enterprise data around customers’ interests, rather than solely on enterprise-centric events and transactions. The company focuses strongly on two verticals – retail and financial services – but also caters to other industries like telecom, media and Internet businesses.

Causata was founded in the beginning of this year by Paul Philips, an entrepreneur who sold his previous business Touch Clarity to Omniture in 2007 for a reported $48.5 million. Gareth O’Loughlin, former General Manager of Skype, is VP of Products.

In a blog post, Philips shares more information about why he started Causata:

For years I had been watching large companies struggling to deliver a high-quality and consistent experience for customers across their various channels. During the same time I had seen many solutions proposed and implemented, most of which seriously failed to address the needs of the customers. When I left Omniture in 2008 I had the luxury of taking some time out (and it was every bit as good as I had hoped). During that time I thought how one might approach this problem starting with a completely clean slate. It had to be something that addressed all communications channels with the customer, of which the web channel is only one, but an important one.

I knew that previous experiences in machine learning and real time targeting of web content had provided a unique foundation for understanding how such a solution should be approached. There had been many lessons about how to deliver real time decisions with high accuracy and very low unit cost, how to process large volumes of unstructured data that had low information density, and how to do so with high efficiency and with low latency.