Today, personalization is critical in learning and understanding exactly what a customer wants in order to enhance their customer experience and increase the bottom line. For example, when we want to watch a movie, we can easily log in to Netflix and there are movie suggestions waiting for us. Similarly, Amazon is at our fingertips when we need to make a quick purchase. While most people take both services for granted, both are powered by recommendation engines that enhance the user experience.
Recommendation engines are designed to customize and personalize browsing and suggestions to make the journey for the user simple, seamless and functional. Many recommendation engines are powered by algorithms that select and show users items or products similar or complementary to users’ preferences. Amazon and Netflix both use recommendations to guide users to find both what they need and what else they might need to ensure complete satisfaction. Recommendations enhance the user’s overall experience because they serve to help users find similar products–whether they are more affordable, more effective, or simply a better value. Creating a winning user experience and showing consumer need for new products leads to more subscribers for subscription services such as Netflix or Amazon Prime, the popular two-day delivery service.
The Amazon user experience includes recommendations throughout the entire process–from browsing through purchasing and checkout. The user’s browsing and purchasing history is factored into the recommendation algorithm, which is designed to promote similar or complementary items of interest to the user. For example, someone searching for pants might see recommendations to purchase a belt to complete the look. In addition, the algorithm is able to sort the search terms so that obscure or irrelevant items are left out of the recommendations. Purchase history is also one of the biggest components of the algorithm, which infers that users are interested in products that are similar to past purchases. As part of their overall e-commerce strategy, Amazon has been able to elevate their brand, reputation, and overall business by employing the recommendation engine model along with easy delivery options, such as Amazon Prime.
Netflix is widely considered the king of recommendation engines. The popular TV and movie-on-demand site uses recommendations to promote content geared toward the viewer and their previous selections. Content discovery ranges from movies to television shows and everything in between. The recommendations served up by Netflix’s algorithm are not only based on prior selections, but genres of interest as well. To date, the Netflix recommendation system has shown a strong ROI, and consumers are often satisfied with the selections provided by the on-demand service. The recommendation engine has soared in popularity since Netflix moved away from mailing DVDs weekly to an on-demand service.
Recommendation engines have improved the user experience for the better. Customers no longer have to seek out complementary or similar products for comparison–companies like Amazon and Netflix have taken care of this for them by building robust recommendation engines tailored to their businesses. While Amazon is able to guide users from browsing to checkout, offering suggestions along the way, Netflix uses its recommendations to promote movies and TV shows of interest to the consumer based on previous selections. Because these companies and many others take care of the thinking for the consumer, the consumer now can make more informed choices while enjoying an easy user-friendly experience.