Natural Language Processing Engine to Activate Text-Based Similarity Algorithm for Improved Relevance Across Industries

Selligent, the intelligent omnichannel marketing and experience cloud platform, announced the addition of a text-based algorithm as part of the Smart Content functionality within its artificial intelligence (AI)-powered engine, Selligent Cortex. Unlike most marketing cloud solutions today that offer only product-based recommendations, Selligent’s new capability leverages Natural Language Processing (NLP) to enable companies to deliver recommendations based on text. Available across web, email and mobile, Selligent’s text-based recommendations will empower marketers to drive deeper engagement, and deliver relevant and personalized experiences for a wide-range of industries that include media, entertainment & publishing, travel & hospitality, and financial services, among others.

“Many of our clients, especially in retail and ecommerce, have seen the tremendous value of surfacing precise AI-powered product recommendations based on rich user profile data and deep customer intelligence,” said Todd McCaslin, CTO of Selligent. “With text-based recommendations, we are extending this capability to empower companies across all industries — beyond those that sell tangible products — to deliver relevance in every customer touchpoint.”

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Selligent’s text-based similarity engine actively looks at a customer’s content consumption behavior and recommends similar content they may be interested in viewing. Leveraging AI-powered content analytics, keywords in previously viewed text are rated and compared to keywords within the company’s larger content catalog. When there is a strong enough match, the relevant content is surfaced and recommended to the consumer in near real-time, not calculated overnight like many solutions.

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Unlike the traditional one-size-fits all approach to recommendations, Selligent Cortex provides a white-box approach, which enables companies to apply their own business rules and logic and have full visibility and control over the resulting recommendations. Algorithms enable companies to display recommendations based on personalization factors such as behavioural metrics (e.g. clicks, page views, etc.) or more statistical based approaches such as Popular, Trending or Top Performing content. This means that the algorithms can be deployed immediately after the feature is activated without months of data collection to train recommendations, providing a faster return on investment.

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