IGIGI Deploys AgilOne Enterprise Edition to Enhance Marketing Efforts Through Personalized, Meaningful and Relevant Customer Engagement
MOUNTAIN VIEW, Calif. (PRWEB) July 19, 2013
AgilOne, the cloud-based predictive marketing company today announced that it will be working with plus-size women’s clothing company, IGIGI to further engage its customers through relevant, meaningful marketing. IGIGI has selected AgilOne’s data science approach to marketing to personalize its marketing efforts and more directly reach its target customers.
IGIGI will be employing the AgilOne Enterprise Edition because of its simple user interface and the company’s deep expertise in predictive marketing across channels, including email and direct marketing. With AgilOne, IGIGI will be able to selectively engage its customers with relevant, meaningful marketing.
“At IGIGI, we are on a mission to transform the world's view of beauty through clothing,” said Alex Brodsky, CEO of IGIGI. “We are thrilled to use AgilOne’s unique predictive marketing technology is yet another tool to reach our customers and clarify the increased misperceptions surrounding beauty today.”
With AgilOne, IGIGI will be able to better segment and reach their customers based on purchase history, browsing behavior, dress size and even ethnicity. A majority of IGIGI’s transactions are done through partner channels eliminating the direct-to-consumer relationship. AgilOne Enterprise Edition will allow IGIGI to now reach their customers personally using direct mail and encourage increased transitions from its website.
“We pride ourselves on being able to provide our customers, like IGIGI, with an application that will help them figure out how to delight their customers,” said Omer Artun, CEO of AgilOne. “Through the use of techniques like data science and machine learning, companies no longer need to resort to ‘batch and blast’ methods to reach their customers.”
AgilOne’s unique technology – based on the science of machine learning – allows marketers to not only understand their customers better based on the massive volumes of data they collect, but to predict what customers will do next based on their behavior.