Recommendation engines use customer data to provide tailored product and service recommendations in line with things they’ve previously bought, searched for, or viewed.
They’ve become a ubiquitous part of the digital experience, showing consumers things that are likely to interest them. When recommendations provide contextual and relevant results, they can improve customer experiences and boost sales. But when they’re bad, customers can find them frustrating or even invasive.
A system that offers product and content recommendations by looking for commonalities between what’s available and what customers and cohorts have engaged with before.
Increased sales and revenue, higher customer engagement, greater retention, and fewer abandoned journeys.
Recommendations can be unwanted; where they feel invasive they tarnish customer relationships.
Recommendation systems are being used throughout our digital world to connect people with products, services and content they’re likely to enjoy.
What is it?
A recommendation engine is a system that offers product, service, and content recommendations by looking for commonalities between what's available and what a customer or user might engage with. They’re most commonly used in digital retail, where product suggestions are continuously made available based on a customer’s past purchases and browsing history.
They’ve become a ubiquitous part of our digital lives, and supplement search functions in ways many of us don’t even realize. From the autocompleted results we see when we begin typing a URL into our browser, to the ‘watch next’ content on our favorite streaming services, recommendation engines are constantly working to serve us with relevant recommendations.
What’s in for you?
By serving customers and visitors with relevant recommendations in the right places at the right time, you can increase sales, improve experiences, and keep people engaged with digital services for longer.
Recommendation systems can even be integrated with digital advertising, enabling you to show customers tailored ads based on products they’re either likely to be interested in or that they’ve shown a direct interest in previously.
What are the trade offs?
From a customer perspective, recommendation systems are divisive. When recommendations are good, and appear in the right places at the right time, customers appreciate them and go on to spend more.
But when recommendations aren’t well-matched, or when they appear in places that customers don’t expect to see them, that can deflect customers away from your brand. Targeted ads, for example, often make people feel like they’re being watched or followed — not qualities that many companies want to be associated with their brand.
How is it being used?
Recommendations are ubiquitous across our digital world today, but the most well-known and long-standing uses of the technology are in digital retail. Most major digital retailers like Amazon use recommendation engines to serve customers with tailored product and service suggestions continuously.
Digital content providers — from YouTube and Netflix to all the major social media platforms — use recommendation engines to show us content, pages, and even people likely to interest us.
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