8 industries you didn’t expect to be driven by recommendations!
Everyone who loves watching films, listening to music, traveling, or shopping online will agree that there is nothing quite as satisfying as finding new products or destinations that are “just right”. But not everybody is aware that behind pretty much every online recommendation hides a set of carefully crafted algorithms that provide us with all the clickable suggestions. Recommendations solutions provide a hip new way of seeping through tons of existing content and personalizing suggestions corresponding to your established tastes. Users have a choice of either actively participating in helping the system gather enough data to give you a more personalized experience or to miss out completely on an amazing discovery! No recommendations are the same. That’s why we’ve compiled a list of eight industries where game-changing recommendations solutions are available today.
VIDEO STREAMING SERVICES
Any YouTube or Netflix user knows that recommendations are the sweet part of the deal when watching a video. Now think, how do millions of video providers keep their users from switching from their site to a competing provider? You got it – a recommendations solution. And not just any solution, but the system that brings the best value for their money. Clickable suggestions mean user retention, and therefore income.
One of the most intricate recommender systems is in the music industry. Being able to understand your online listening behavior and suggest artists or songs that you might potentially fall head-over-heals in love with is as much as science as an art. With many systems, you must have seen that recommendations are generated and changed organically, depending on your listening behavior and the factors you input when signing up to the service.
NEWS AND MEDIA PROVIDERS
How did you find this article? Most likely through one of the recommendations solution, which means they work well! But the fact remains, every piece of news information you get through your online sources are directly linked to your reading behavior. Ever notice how facebook suggests articles that are similar in nature from the different sources that you have liked say ted talks or mashable? Yes, you guessed right! They also use a gathering of hard data of user behavior and apply it to the existing pool of information to generate recommendations.
Hockey, football, baseball – you name it! Staying in the know is important for all sports fans that read virtually through every piece of information on their favorite teams. But what about the rest of us, who are passing by or trying to find something about a new favorite team? That’s where advanced recommendations are of help – suggesting you news and video related feeds, live games or a historical rundown depending on your preferences and what you may find interesting!
Some online travel sites that use recommendations engine are a great place for getting terrific suggestions. Starting from destination details to users’ personal preferences there is a vast array of possibilities for recommendations that those sites provide. With heaps of data contained in reviews alone, a proper recommender engine can do wonders. It can provide the users with the information about other hotels within the desired area, a cleaner beach, preference-tailored cultural heritage sites, additional destinations of interest, and that’s only the tip of the iceberg!
Finding a foodie hotspot, exotic menu from a new restaurant, or a hidden gem local pastry shop could not be easier thanks to the recommender system that knows your cravings. Chances are if you use a restaurant review website with a recommendations system in place, you will always be able to deliver interesting suggestions. Recommendations solution can be quite simple and analyze just single-component data such as it may only consider ratings of the restaurants or dishes. With more sophisticated engines, however, you may find the most succulent of diners for your budget within the shortest distance, depending on your unique taste.
Tired of the “same old thing”, dates that lead you anywhere but a place you want to be, or you’re simply not generating the desired response? You’re not alone. Anyone who’s ever been on a dating site knows the daunting fill-out forms, compatibility charts, match-ups, or “personalized” search for extra money. Dating sites business flourishes with every new subscription, what remains is the ability to create compatibility among the millions of signed up users. Recommender systems depend on user profile being accurately completed or associated with a legitimate social network. This way it can gather more accurate information about the user’s preferences and by using advanced technologies analyze the possible compatibility with one another.
Ever bought a laptop and were immediately suggested to buy another one at the bottom of the page? Well, that’s a sign of a poor recommendations system. Easiest industry in which to determine the excellence of suggestions is the e-commerce. More often than not, a person choosing the said laptop completes at least a partial research prior to the purchase. What the system can do to increase sales for the company is to catch attention of the customer by offering complimentary products and substitutes. For a sophisticated recommendations solution, it is not difficult to analyze anonymous user’s purchasing behavior and deliver predictions not so different than for a registered user. Web services without a proper recommendations solution often make mistakes that could easily be avoided. From providing users with a hotel that closed down years ago, to the wrong address of an eatery, or a terrible match to date, these are some examples that you have probably encountered. While it looks simple, a recommendations engine actually uses many complex algorithms in its core. Present advances in Artificial Intelligence and Data Mining technologies allow the systems to become uncannily precise in delivering suggestions that evoke true aha moments.