Recommendation Engines: Making Better Choices


One of the vital telling traits of the businesses of the brand new digital period is the flexibility to ship actionable suggestions. As a rule, the aggressive benefit of those digital-first firms is correlated to the accuracy of advice engines.

Take into consideration firms which have revolutionized their respective industries. Whether or not it’s Netflix, Spotify or Amazon, all of them have initially made advice engines as their primary instrument for buyer engagement and loyalty. The way in which Spotify can uncover new music that you’ll get pleasure from with unprecedented accuracy and with none seen enter of yours is what made it stand out from the competitors. As time goes by, it turns into even smarter and extra correct, enabling a endless cycle of worth creation.

In some ways, advice programs enable for sustaining an ever-increasing circulate of recent info, merchandise, and companies. With 60,000 songs uploaded every day on Spotify, greater than 500 hours of content material uploaded on YouTube each minute, and tens of thousands and thousands of pictures uploaded on Instagram each day, it turns into considerably tougher to make knowledgeable choices about what to purchase, watch, and devour subsequent. Having the ability to navigate this abundance of content material in a significant manner seems like a superpower, and that is precisely why prospects are inclined to desire these companies over others.


Advice manipulation

Numbers are very telling as nicely. For instance, Netflix claims that three out of 4 motion pictures that individuals watch comes from customized suggestions. In such a case, the advice system has an unparalleled affect on peoples’ selections. An inevitable and infinitely difficult query arises: how a lot of these suggestions are honest and unbiased? Given that almost all of predictive analytics consultants and machine learning engineers agree that it’s close to impossible to eliminate bias from AI entirely, how fair those recommendations truly are? And even more importantly, can recommendations be manipulated?

In essence, manipulating the output of a recommendation system is easy for engineers. In 2018, the release of Drake’s new album broke single-day streaming records on Spotify. While Drake’s immense popularity is out of the question, many attribute his success to the ‘in-your-face’ promotion orchestrated by Spotify. Not only the artist’s new songs were placed in seemingly every playlist including ‘Ambient Chill’ and ‘Best of British’ (Drake is a Canadian artist), but many users reported an overly increasing presence of Drake in their recommendations. While we will never know what Spotify really did behind the curtain, there are many factors suggesting that this was a paid promotion. While there is nothing wrong with advertising, disguising it as a recommendation engine output feels unfair.

Can we do something about it? This seems unlikely. Despite the huge user backlash and extensive media coverage of Drake’s shady tactics, Spotify’s user base is steadily growing year after year ever since.

Making better choices across the board

Recommendation engines use goes far beyond alluring customers to a certain platform; it is also about making better business decisions. In the coming years, the job of marketers, brand managers, HR professionals, UX designers, and copywriters will become increasingly augmented by different types of recommendation systems. Essentially, these systems are your conventional data-analytics platforms redesigned into much more convenient and user-friendly digital advisors.

Instead of analyzing data to manually determine what type of marketing campaign will be the best for a particular target audience, this process will resemble exploring suggestions on Amazon. Something along the lines of: ‘The group of customers that positively reacted to this type of advertisement also clicked on this ad’ or ‘This target group is 80% more likely to be attracted by premium offers than 2-for-1 promotions’, and so forth.

Similarly, tools like IBM’s Watson Tone Analyzer can review an executive’s company-wide email regarding changes in the organizational structure, and suggest certain revisions to make it more clear, transparent, and encouraging. Based on the selected target group, copywriters will receive recommendations on word choice. UI designers will receive data-based recommendations on what font will suit a particular type of app better. Again, there are many such tools available on the market today, it’s just that they need to provide a more consumer-friendly experience.

It’s about time we stop associating recommendation systems solely with e-commerce. Recommendation engines can be used not only to suggest products but to provide data-based advice that can help streamline decision-making. With the ongoing innovations in ML and AI, it’s certain that recommendation systems will continue taking over both consumer and employee experiences.

Supply hyperlink

Leave a Reply

Your email address will not be published.