Nogin Unveils Vx Of Intelligent Commerce, The Leading Commerce-as-a-Service Software Platform

Nogin, a leading provider of innovative Commerce-as-a-Service (CaaS) technology, launched a new website unveiling the arrival of machine learning Customer Segmentation and Smart Sort merchandising capabilities to its Nogin Commerce Platform. With these new capabilities, leading direct to consumer (D2C) ecommerce brands will now have the ability to elevate customer experiences and maximize potential profits. As noted in a recent Insider Intelligence report, the 2022 holiday ecommerce season is expected to grow 15.5% to $236 billion over last year’s season — Nogin’s enhanced technology helps brands take advantage of this growth by optimizing customer targeting to improve customer conversion, without added complexity.

“We strive to provide brands with a commerce platform that ensures they will never need to replatform their ecommerce operations to remain on the cutting-edge,” said Nogin co-founder and CEO Jan Nugent. “By delivering a pre-integrated enterprise commerce solution at scale, we allow brands to achieve the benefits of enterprise commerce technologies and best practices, without the up-front and ongoing investments in innovation. As we enter the 2022 holiday season, the breakthrough segmentation and merchandising features being unveiled today help brands stay competitive with the largest online retailers and maximize their profit potential.”

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New Platform Features Drive Intelligent Commerce

Nogin’s all-in-one, cloud-based CaaS platform is purpose-built for brands selling direct-to-consumer (DTC) and through online channel partners. The Nogin Commerce Platform contains the latest ecommerce technology innovation, delivering enterprise-scale results to smaller, growing brands. New features announced today include:

• Machine Learning Customer Segmentation: A robust customer data platform (CDP) uses machine learning optimized across Nogin’s entire customer base to bring data insights that cannot be achieved alone by a smaller brand. Brands can automatically segment customers based on purchase history and behavior, allowing them to dynamically target offers to specific customer segments, export data to popular email service providers, and generate lookalike audiences of their best customers. Customer segments are also automatically updated and segmentation algorithms self-learn to become more accurate over time.
• Smart Sort Merchandising: A dynamic merchandising system leverages machine learning to generate a high conversion-to-traffic ratio. With this feature, a brand’s customers are able to select a smart sort option that dynamically rearranges the product selection according to the latest data so customers can see products that historically experience a high conversion to sales.

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