We want to take you through something that is driving your daily computerized living- real-time individualized recommendations.
Have you experienced the way that Netflix always knows what you want to watch next? Or how Amazon simply has you with those uncannily accurate recommendations?
In yet another coincidence, that is. It is real-time AI that happens behind the scenes to make you feel as though it is made specifically for you.
So what is actually going on under the hood?
Now let us put it in simple, human terms.
Personalized recommendations in real-time are immediately generated suggestions based on the user's current behavior, preferences, and history.
The goal?
Deliver the right format, product, or message at exactly the right time.
Modern platforms rather analyse the actions by the users on the fly, instead of waiting till the batch of data is processed during the night. Basically, it is similar to having an electronic assistant that keeps on learning to adapt to your demands.
You probably have witnessed this on:
This is merely a couple of personalized recommendation cases that we go about our everyday life with.
Now, how about getting specific?
Some examples:
Growth and flawless performance are designed into these systems by giving value instantly, enhancing user experience, and boosting conversion.
Here comes the interesting part.
Off the radar, these systems fuse:
Data about every click you make and search you do, and scroll you make all make a plot. AI systems are used to market to you based on their observation of these interactions to determine your interests, preferences, and your intent. Over time, this creates a digital profile to help platforms predict what you are most likely to respond to next.
It is not what you do but how, when, and where you do it. The time of day, the device you are on (mobile, desktop, tablet), and even geography can give a layer of context to help define what is suggested to best fit your immediate circumstance- say lunch spots at lunchtime or mobile-friendly content when you are on the road.
The ML algorithms serve as the proverbial engines of smart recommendations. They use big user data sets and analyse them to identify patterns, make predictions, and adapt to emerging behaviours. The recommendations will be more accurate and personalized in case they consume more data.
Content-based is one of the oldest forms of recommendation engines. Collaborative filtering: Collaborative filtering is one of the oldest forms of recommendation engines. In Content-based filtering, items that are treated like those you have liked in the past are recommended, whereas in collaborative filtering, it looks at what similar behavior users have enjoyed. Their combined efforts assist in providing you with suggestions that feel specific to you, even when you have never engaged with said material.
The models are not only able to work on repetitive data but also understand the data when instantaneous interaction takes place. In case you develop a new interest, the AI will make suggestions that instantly change on the fly to keep up with your new interests.
All of this is part of a broader structure called the real-time recommendation system architecture. The keyword here is real-time. The faster your system reacts to user input, the more relevant the experience feels.
And one could not possibly discuss this topic without examining the case study of the Amazon recommendation system.
Amazon doesn’t merely provide recommendations based on previous purchases; they look at browsing history, wish lists, what products other people with similar habits may have purchased, and more. They have a very effective algorithm to the extent that Amazon's personalized recommendations are reported to generate 35 percent of its revenue.
They have a combination of real-time data processing, retrospective user data, and predictive algorithms in their system. With each click, the suggestions become adjusted. That is the scale of the power of personalization.
You may run a small online store or a global platform; whatever your use case, recommendations as a service can change your user experience. Not only does real-time personalization mean more sales, but it also means that you make your customers feel understood.
It is because, come on, we do not pay attention to such blanket recommendations of fitting everything to one size. This matters to us when something is created specifically to appeal to us.
This is what the businesses are to gain:
Of course, now, when you likely think: “It all sounds amazing, but do I need a whole team of data scientists and engineers to make it a reality?”, the good news is: not necessarily.
Plug-and-play solutions to generate personalised recommendations in real time are available on modern platforms such as Aingage. It amounts to thinking of an opportunity to give Netflix-quality without the need to build Netflix-scale infrastructure.
You give it your customer data to train, and the system will learn and develop: delivering custom content, product recommendations, and so much more, at web, mobile, email, and chat touchpoints.
It's like having an AI-based recommendation engine running in the background all day, every day, to turn data into smart and revenue-generating experiences.
Highly customized is no longer the right, but the alignment of the expectations. And you can bet that unless you are giving people relevant experiences on-the-fly, they will somewhere.
Thus, be it the technology or the final product, the one thing that can be agreed upon is that real-time individualised recommendations are the way things are in digital engagement.
And now, when you know some details about the technology behind it, you have more chances to make the most of its potential.
When you are ready to learn more about how this can make a part of your customer engagement strategy, tools such as Aingage are available to support you in making this process easier (and smarter) than ever.