Three Advantages of Implementing a Recommender Engine
Recommendation engines are a great tool for established companies to enhance product discovery and increase customer lifetime values. They are awesome for creating automated personalisation services for businesses that have an existing customer base, constant traffic, and massive inventory.
If you ever purchased a book on Amazon or watched a movie on Netflix, listened to a track on Spotify, Last.fm or Pandora, or if you have a Facebook or LinkedIn account, then you have most certainly interacted with a recommendation engine.
The recommender systems are the core of the technology that shows you “customers who bought this item also bought,” “suggestions for you,” or “people you may know.” Fundamentally, recommender systems work by finding relations between your existing products and services based on what your customers purchased or browsed.
With recommender systems spreading to an ever-increasing number of web applications, we started thinking what can a recommender system that a marketing department cannot.
A fine tuned recommendation engine enhances product discovery
It’s not a surprise that we live in a world where new products are produced with a lot shorter times to market. In this environment, it is natural for customers only to discover the most popular items. However, a fine-tuned recommendation engine will curate your products and services in such a way that your customers do not see only the most popular things, but the most relevant ones.
That is the real power of a recommendation engine because they use previous user behaviour and details about the available items, they are capable to surface products that lie to the right-hand side of the long tail distribution. According to Business 2 Community, companies that implement recommendation engines recorded an increase in the number of items per order by 20-40%.
Streamline user journeys and increase customer loyalty
Nowadays, there are alternatives to almost any product, which makes gaining customer loyalty a real pain for marketers. However, recommendation engines display only similar or relevant items to a user which in term reduces the friction in their journey, all while facilitating their conversion.
High levels of personalisation are inherent benefits for using recommender systems, and users recognise the added value it provides to the companies from which they are purchasing products and services. Several studies reported that more than 15% improves user engagement and that visitors are two to four times more likely to become loyal clients because of the help recommender systems is providing to them while browsing and making purchasing decisions on recommender systems.
Increase customer lifetime value
Recommender engines work by accessing large database with user purchases, browsing histories, and information about the available products and services. Inherent in the way they are constructed is their ability to provide marketers with relevant content for campaigns, email blasts, or help them understand cart abandonment. These features directly contribute to increasing the customer lifetime value because they provide you with a solid foundation on which to design future customer retention, targeted marketing and advertising campaigns with a minimum manual effort from your content management team.
Recommendation engines are slowly transitioning from being a niche technology only for the most tech-savvy to a core part of any business. We believe that as the technology behind recommendation engines matures and the amount of information available to companies increases, the only way you can offer a personalised, customer-centric experience is by augmenting your business with a recommendation engine.
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Enterterprises should create networks of specific AI solutions and intelligent agents. These agents adapt to their environment and are autonomous. In other words, they continually learn from situations and each other about how to best perform their tasks. Soon, intelligent agents augment the whole enterprise and support employees. As they grow and evolve, these systems become ubiquitous, from internal systems to monitor and control processes to client facing applications and services.
voice interfaces and video content will soon become the primary way in which customers interact with businesses. Customers will interact with a brand in real time about their products and services. Voice interfaces like Alexa, Siri, or Cortana will enable customers to talk to a brand. Video and speech processing algorithms will further support that interaction either via intelligent chatbots or other interfaces that understand consumers’ pains, wants, and needs. As far-fetched as it sounds, this is not a sci-fi scenario. It will happen in the next 3-5 (maybe ten years). Thus, for brands to maintain and establish new long-lasting relationships, they need to support audio and video conversations with their clients as soon as possible.
AI is a collection of methods that initiated a new paradigm in business, one where we solve problems without knowing the steps to take in advance. Using AI, we develop systems that mimic natural intelligence and in some cases even surpass it. Thus, AI creates systems capable of reasoning, solve problems, acquire and use knowledge, make decisions, and communicate in natural language.