Are Recommendation Engines Restricting The Customer’s Choices?

By
Yash Raaj
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Diverse suggestions often lead to better conversion rates for online firms

Did you know as much as 35% of Amazon.com’s revenue is generated by its recommendation engine? And close to 80% of all content consumed on Netflix is driven by its recommendation systems. These figures testify to the indispensability of integrating recommendations in a company’s working model for consumers. 

The recommendation systems are employed to process huge quantities of data to suggest relevant items to consumers. However, the challenge is tailoring the systems to impart a memorable customer experience that leads to higher conversion rates.  

Recommendation algorithms optimize customer experience based on similarities. So, for instance, if you keep on looking for a particular genre of movie to buy on Amazon Prime, it will later suggest you watch movies in that category only. Thus, there is a veritable risk of algorithms restricting a customer’s worldview. These repetitions could lead to a customer falling into an echo chamber – conditioned by the constant recommendation of one type of product. Thus, many firms take time to construct a recommendation system algorithm that strikes a good balance between accuracy and diversity. 

Machine learning powers recommendation systems

Machine learning (ML) models use several innovative algorithms to solve problems while improving results for an online audience. Recommendation systems with ML rake in users’ behavioral, historical purchase, interest, and activity data to predict preferable items to buy. Essentially, it thrives on data that works on identification, segregation, classification, and even prediction of fresh output based on inputs. For these systems, consumer data is pulled out from social media, websites, ecommerce portals, apps, and other channels.

There are some recommendation systems with ML that convert more than others. While some work on similarities, others try to give intuitive suggestions to maintain diversity. Each comes with its set advantages. 

  • Collaborative filtering: Collaborative filtering (CF) matches online users with similar interests to personalized items, people, or feeds. CF works on the idea that a user who brought a string of products will also buy more similar products in the future. CF, however, has promising accuracy and is widely employed by Facebook and Amazon.
  • Content-based filtering: It considers purchased items as input data to suggest similar items to users. ecommerce platforms and video streaming apps use content-based filtering. Amazon makes recommendations under the banners like “frequently bought together” and “based on your viewing history”. The goal of “frequently bought together” recommendations is to increase average order value. The “based on viewing history” system will produce a catalog of products similar to what you bought.
  • Knowledge-based recommendation system: This system uses the data or knowledge that is stashed in the company’s storage. When a user buys something, the knowledge-based systems pair their queries by understanding its context. It then recommends something complementary or similar to the user. These systems also have an advantage – they can be improved by interacting with the system. Users can leave feedback, which can be used to elevate search results.
  • Candidate-generation network: This system scrutinizes users’ likes, comments, and frequently-perused digital content. Based on the scrutiny, it predicts what the user may like by using Google’s TensorFlow – a free and open-source software library for machine learning and artificial intelligence. When paired with the ranking network, the candidate-generation network extracts important features for each content to rank the recommendations. Youtube employs it for recommendations.

Where does AI fit in the recommendation system?

Artificial intelligence (AI) pairs up with ML to deliver personalized recommendations that match customers’ preferences across all touchpoints like website, mobile experience, email, and contact center. 

A good program can save a lot of work.  For instance, Google’s cloud product recommendation system, Recommendations AI, saves the effort to preprocess data, train or hypertune machine learning models, load balance, or manually provide infrastructure to handle unpredictable traffic spikes. The system integrates data, manages models, and serves recommendations. It allows you to connect data with existing tools like Google Analytics 360, Tag Manager, Merchant Center, Cloud Storage, and BigQuery. 

An AI plug-in can also optimize all data, including unstructured metadata like product name, description, category, images, product longevity, and more. Also, one cannot downplay the global value. Google’s Recommendations AI even supports international product catalogs and multiple geographies. Thus with just one movie, recommendations can be delivered across the world. The diversity achieved through globalizing can give spectacular results.

Google claims that it has achieved massive success through Recommendations AI – its click-through rate is 90% and almost 40% of the suggestions lead to conversions. Its stalwart clients include IKEA retail and garment brand Hanes. 

Conclusion

Better recommendation systems can attract more traffic, engage shoppers through diversity, improve customer experience and retention, amplify conversion rates, and increase average order value. Improved ML and AI recommender systems can promote diversity of thought.

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