Personalization has become the main explanation for why we started to spend more time and money on shopping apps—today, e-commerce platforms and e-stores understand our tastes better than we do.
They know what we might like to buy next, when we’re most likely to shop, and how to tweak suggestions so they match our likes.
In 2023, there was a global survey asking business leaders about their attitudes towards personalization. The results were striking:
- 92% of respondents said they use AI to personalize customer journeys and grow their businesses.
- 82% agreed that customers tend to spend more when their shopping experience feels exclusive.
- 69% of business leaders said they would double their investments in personalization, even during economic crises.
Given this trend, it’s no surprise that by the end of 2024, global income from customer personalization software is expected to exceed $9.5 billion.
Personalized product recommendations have simply become a must for entrepreneurs who deal with assorted audiences and want to please everyone, bar none.
So, if you want to make hay while the sun shines and add personalization to your workflows, move on. Below, we’ll reveal the tiniest details you might need to successfully embed it into your processes.
What are Recommendation Systems? Definition and Basic Principles
Essentially, product recommendation systems are complex machine learning algorithms that suppose what users might favor and propose items based on different parameters.
They look at how users act, what items they put inside their wishlists, and how people interact with them to present personalized suggestions.
In simple terms, they discover coincidences in numerous data records, get to know user intentions, and advise things that go in line with those insights.
Types of Recommendation Systems Machine Learning Offers
Normally, recommendation systems come in different types. The most common one is collaborative filtering.
This method relies on the simplest rules—what other users like is most likely what you will like too. It works by examining the behavior of people who have identical or similar tastes.
For example, if you and your mate enjoy the same shows, the system might suggest performances that they have already watched but you missed.
Indeed, there are two versions of collaborative filtering: user-based, which recommends items picked by similar users, and item-based, which proposes items that are comparable to ones you’ve already enjoyed.
Another class is content-based filtering. This method centers around the details of the items themselves rather than user choices. It analyzes genres, keywords, or plots to introduce equal content.
For instance, if you adore romcoms, the system will recommend other films in that segment.
Some systems go for a hybrid approach, which combines the first two classes. By mixing these methods, hybrid systems can give you better recommendations.
The catch, though, is that it requires more effort to set up since you’re blending different recommendation techniques and figuring out how they work together.
Sometimes, you’ll run into other methods, such as contextual bandits, which memorize what users do and tweak recommendations accordingly, knowledge-based strategies that rely on specific reports about users and items when there isn’t enough data for collaborative filtering, different reminders, name inclusion, and so on.
Applications of Recommendation Systems
Some entrepreneurs believe that recommendation systems shine solely in the sale of goods, but this is not quite true. While retail does benefit from personalization, there are many other domains that can profit from ML:
E-commerce to Retain Customers
For any online seller, AI personalization seems like an obligatory tool to segment buyers and optimize the shopping venture. An effective ML algorithm can not only increase the number of impulse purchases but also retain customers, and save operational time.
Streaming Services to Hook Viewers and Make Them Come Back
You might have already seen how Netflix and Spotify employed these systems to tweak what subscribers see or hear. And it’s true, by looking at what users watch or listen to and suggesting movies, shows, or songs that fit their tastes, you can keep viewers hooked and make them come back for more.
Social Media to Make New Connections
On social media platforms, recommendation systems can assist users find friends, groups, or content they might admire. They can analyze what people skip or follow and show relevant connections and stuff in their feeds.
News and Content Websites to Stick Readers Around Certain Content
News sites and content platforms can apply these systems to recommend articles based on what readers are interested in and what they’ve read before. This way, they can revisit your portal again and again and enjoy content that falls under their appeals.
Other Industries
If you run a firm within other domains (healthcare or travel), you can also add recommendation systems, for example, to propose treatment programs or recommend travel destinations based on priorities and past itineraries.
How to Implement a Recommendation System
Setting up a recommendation system is pretty simple once you break it down.
First, you must preliminarily independently gather data on what users like and how they interact with different products. Then, you clean it up and get it ready to use.
After that, you’ll have to pick the right algorithm based on how much data you’ve got and what outcomes you expect from the implementation. Once everything’s trained, you’ll check how well it works with precision and recall.
When it’s time to launch, you’ll want to make sure it fits with your current setup, usually by connecting it with API or microservices.
From there, it’s all about keeping it fresh—using feedback and A/B testing to keep the recommendations on point.
If your business doesn’t want to plunge into the ocean of machine learning development and implementation, SCAND can totally handle it. We’ll guide you through everything and prove it’s perfectly up and running for your enterprise.
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