Many of our clients, especially in retail and eCommerce, have seen the tremendous value of surfacing Artificial Intelligence (AI)-powered product recommendations as they engage with consumers day-to-day. However, we would be remiss to undersestimate the need for such a capability across many other industries that we service; companies that may not necessarily be in the business of selling “products” but rather, content or other types of services.
Introducing: Text-Based Algorithm
This week, we announced the launch of a text-based algorithm as part of the Smart Content functionality within our AI-powered engine, Marigold Recommendations. This new capability leverages Natural Language Processing (NLP) to enable clients to deliver recommendations based on text.
The text-based similarity engine actively looks at a customer’s content consumption behavior and recommends similar content. 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.
This is a truly powerful and game-changing capability that will empower companies to drive deeper engagement, and deliver relevant and personalized experiences for a wide-range of industries, including publishing and media, travel and hospitality, as well as financial services, among many others. A few examples:
- An online news publisher could suggest broader news articles for readers that have shown interest in a specific topic. For example, leading publisher Hearst UK quintupled new readers with tailored subscription offers through a personalized communication flow. Now, with this text-based algorithm, media publishers could use triggers and combine them with keyword analytics to accurately hone consumer interest data and deliver uniquely tailored content.
- A marketer in the travel and hospitality industry can further engage with a customer who recently booked a trip, providing tailored content such as “Top 10 Activities for Families,” or “Best Local Places for a Business Dinner” based on other factors. For example, Selligent client Kimpton Hotels & Restaurants recently used an improved data strategy to activate its loyalty program. Hospitality companies could continue to drive loyalty by capturing what type of content guests research on its website and offering tailored tips so they can plan trips in advance.
- A financial services institution could further educate its customers on the programs they offer, empowering them to make well-educated financial decisions suited to their unique needs and situation. For example, Selligent client Alliant Credit Union recently deployed an education-based Home Equity Line of Credit (HELOC) campaign, where they re-engaged customers who had opened a HELOC fund but failed to use it. A text-based algorithm could help take engagement one step further to understand the customer’s current financial questions and needs, offering additional content linked directly to their previous searches, interests or consumed content.
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