It’s Not Magic, It’s a Recommendation Engine

Software, Software Everywhere!

Some of us start our day by speaking with voice assistants. “Alexa, play Jazz music on Spotify”, or “Hey Siri, what’s the weather like today?”. After a while, when we forget to ask Siri, the iPhone suggests opening the weather app. Or when you open Spotify in the morning, the playlists that it shows are the ones you usually play when you wake up. You go to the bathroom, the reels that appear in front of you on your Instagram feed, or the videos on TikTok, are related to your past activities. When you are at work and searching for something, what Google recommends is related. During your working break, while you are buying clothes on Amazon, it suggests a t-shirt with the logo of the group that you listen to every morning. When you arrive home to watch something on your TV, what Netflix suggests is related as well. The list goes on… Software is evident in every part of our lives. Most of the biggest companies in the world are software companies. So, as Marc Andreessen said in 2011, “Software is eating the world”. Recommendation engines have had a huge impact on the process up to this point and beyond.

Though most of the use cases given above relate to unimportant issues, those are not all. The same systems can sometimes literally save lives. In cities with traffic issues, apps like Yandex or Google Maps that show the fastest route help a lot. Thanks to these, some ambulances know the fastest routes and they can reach the hospital in a shorter time when seconds can be the difference between life and death.

These technologies have more potential than that. Within the employee augmentation blogpost, I wrote about how engineers use AR glasses for predictive maintenance and repair with suggestions rather than using thick guideline books. Think of the same for surgeries. Doctors use glasses that scan the body and suggest alternative approaches by analyzing the problem of the body. Even though it sounds a bit different from how we use recommendation engines in our daily lives, the technology behind it is the same. Even though this use case is a more complex one, it does not mean it is not possible and it is something different.

Being Simple is Complex

To explain how these mechanisms work and affect, I want to use Google as an example. The first picture that comes to mind when Google is mentioned is its home screen. It is an empty white screen with a logo on it. Pretty simple. But how did it come to market and be the top search engine, even the ones with all those cool gadgets and stuff? It is ease of use.

Google has data centers in 23 different countries. It has multiple billions of users and multiple petabytes of data. They collect the data of your YouTube searches, likes, comments, Google Maps searches, locations, time spent in that location, apps that you use on your Android device, Google searches… The list goes on. In addition to that, they provide all users with a 15GB cloud for free. They collect a lot more data than their competitors. By using everything that they know about you, they suggest more accurately, helping you to quicken the process of finding what you are searching for. So, it gives you an easy appearance, creates real value behind the curtains, and provides a smooth experience.

This is quite similar for all successful software companies. As mentioned in the beginning, there are lots of software companies that we engage with in our daily lives. Spotify, Amazon, Meta, Netflix, Uber, Google, Apple, TikTok… All of these companies are successful thanks to the data that they own, simple interfaces, and algorithms that work with high accuracy.

Stickiness is the Key for Businesses

There are lots of benefits of recommendation engines for companies. But the whole process starts with data. Data helps companies to understand their customers better. After they have understood, recommendation engines help to grow their revenues. Long tail sales are one of the best outputs that recommendation engines provide since it is the most accurate suggestion mechanism. With the suggestions, customers can easily find niche products that they need.

Underestimating the revenue growth that recommendation engines could cause would be a big mistake. For example,  35% of Amazon’s revenue comes from its recommendation engine (1). Even though Amazon is the best in class and this much revenue growth is rare, understanding the customer and being able to suggest what they want or need also has some intangible value. It creates ease of use and customer loyalty.

Especially for social media platforms, the time that users spend is highly important. The more time the user spends, the more data the company collects. The more data a company collects, the more accurate advertisements that they show. This increases the revenue and starts the cycle from the beginning by taking even more time. This is called stickiness. Stickiness is important for value generation, and the best way to do that is by recommending accurately.

Ease of Use Attracts the Customer

Not only businesses, but recommendation engines help customers as well. Spending time trying to find something can be quite boring and it is a bad experience for customers. Because they will be spending time to spend some money. Sometimes they are even impatient and leave without bothering to find. To prevent missing out on such opportunities, ease of use is quite important. Search bars and recommendations should show the right directions, in a way that avoids customers from wasting time. It creates more revenue by selling more, and a better customer experience by spending time on the things that you want to do, rather than wasting time trying to find the thing that you’d like to do.

Spending Time, Rather Than Wasting It.

Recommendation engines are providing a higher quality time on the digital platforms that we use. They play an important role in our lives, probably more than we realize. They already have different use cases such as e-commerce, routing, media, health, etc., and different benefits for both customers and providers. Recommendation engines already generate a significant amount of added value, yet they are promising even more.

Just like recommendation engines, there are lots of benefits of being a company that focuses on digital. Companies that actively use the blessings of digital are clearly ahead of their competitors with such mechanisms. Is your company using them enough? What is your digital maturity level? What about your competitors? What should be your next step to be more digitally mature? If you wonder about the answers to these questions, you should check our Digital Maturity Index.