As the COVID-19 pandemic continues, both people and businesses have to adapt to the situation that has occurred. Since brick-and-mortar shops are becoming less of an option for shopping, more and more consumers turn to online stores. As the number of potential clients increases, eCommerce businesses look for ways to make the shopping experience on their website as smooth as possible. One of such ways is, without a doubt, an intelligent product recommendation system.
If you own a Magento online store – you are in luck. The platform has recently released a Product recommendation system powered by their Artificial Intelligence – Adobe Sensei. The system brings various benefits that eCommerce businesses can take advantage of:
- Shopping experience powered by AI. The AI analyzes tons of shopping behavior data and allows you to produce accurate product recommendations and make the right product matches. Besides helping to create a personalized shopping experience for every customer, it also frees up the time you would have to spend doing all of this manually.
- 9 types of recommendations to choose from. The product recommendation system automatically creates lists for your customers. However, you can also choose what kind of lists it creates. There are 9 types in total, as Recommended for you, More Like This, Trending, Most Purchased, Viewed and Bought, etc.
- Convenient user interface and smooth workflow. The system comes with a very convenient user interface that provides a clear and simple workflow. Merchants always have access to the data they need to easily create product lists for their customers.
In addition to all of this, the Magento Product recommendation system automatically tags all of your pages and synchronizes the catalog. This, in turn, ensures that everything is deployed accurately and saves both time and effort for the merchant.
However, probably the best part about this Product recommendation system is that it is completely free on the Magento marketplace. The only requirement is to run your online store on Magento 2.3 or higher.
Main benefits of a recommendation system
There are plenty of ways your business can benefit from having a recommendation system, all of which can be divided into three main categories:
- Increases sales and revenue. Lots of businesses around the world increase their sales and revenue by predicting customer shopping patterns and offering them the right products. Amazon, for example, enjoys a 29% increase in annual sales not in small part because of its intricate recommendation system.
- Increases customer engagement. A personalized experience makes users more engaged and raises the chances of them coming back and interacting with your store again. Moreover, it raises the chances of converting your visitor into a customer.
- Increases efficiency. A recommendation platform powered by machine learning improves the overall efficiency of your business in a variety of ways: from releasing your staff from manual work to making merchandising management more effective. In addition, a recommendation system provides you with accurate reports that you can use to make better decisions and achieve the goals you set for your business.
How personalization is done in eCommerce?
The workflow of a product recommendation system powered by machine learning (an application of AI) usually consists of four steps.
All customer data is collected during the first step. It is assisted by the front-end of the website, which records all visitor interactions. Two types of data are collected: explicit (comments, ratings, feedback, etc.) and implicit (search history, clicks, order history, etc.).
All data is saved in permanent storage, which can be accessed by the machine learning platform. There is also additional storage, provided for the front-end, where it can access recommendations, provided by the platform.
The machine learning platform analyzes the collected data. Based on the particular needs, there are several methods of analysis:
- Real-time analysis. Data is analyzed “on the go” as it is created. This method is used to provide quick suggestions and requires tools that can analyze streams of events.
- Batch analysis. The analysis is performed periodically (for example, daily), once enough data has been collected. This method is particularly useful for e-mail campaigns.
- Near-real-time analysis. This method involves gathering and analyzing data quickly (within a few seconds or minutes) and is best used for providing recommendations during the same online session.
Recommendations are provided to users based on one of the filtering methods used by the system.
What are the different types of recommendation systems?
This method involves analyzing user behavior and providing recommendations based on what they and other similar users have expressed interest in before. In broader terms, this means that, if user A and user B have a few matching interests, the rest of them should probably match as well. Furthermore, there are several recommendation algorithms that are used in collaborative filtering, the most common of which are:
- User-user algorithm. This algorithm is based on finding and matching similar users and offering products that their lookalike has chosen. Although quite effective, this algorithm is also very resource-heavy and requires lots of time and effort to put in place. While smaller businesses may find it useful, for larger enterprises with lots of items and users it may be too burdensome.
- Item-item algorithm. A similar but relatively simpler method of collaborative filtering finds and matches items instead of users. The big advantage of this algorithm is its ability to quickly provide recommendations even to new users that have not interacted with your website before. Moreover, it requires a lot less time and resources to deploy and can be used effectively by both smaller businesses and large enterprises. Amazon serves as the best example of the effective use of this algorithm.
Other algorithms are used in collaborative filtering as well, but they are generally less common and effective.
Content-based filtering is based on matching content such as product descriptions (or, more specifically, keywords used in product descriptions) with users that have expressed interest in similar content in the past.
The effectiveness of this method is based on how well the system can analyze user behavior to find preferences that would match other types of content. Less sophisticated systems are only able to recommend the same types of content that the user has interacted with, which is neither effective nor particularly useful. Advanced systems, on the other hand, can predict user preferences quite well, and recommend different types and sources of content.
Demographic-based recommendation engines
Product recommendation can be determined by the demographic class that a user belongs to. The recommendation engine of this type subdivides users into classes depending on their demographic characteristics. To enable such a system, the algorithm first needs to conduct market research limited to a particular region and detect attributes that characterize local consumers. On the one hand, demographic-based recommendations are easy to use. They are popular in many industries. On the other hand, such recommendations are not personalized enough and can hardly help you outstand the competition. Ecommerce product recommendation that is demographic-based will be generic and targeted at a broad segment. Such recommendations are helpful for marketplaces but not for online stores that want to differentiate.
Utility-based product recommendations
This type of product recommender system calculates the level of usefulness or utility of a product to a particular user. Depending on the computation result, the tool decides what product to recommend. Utility-based product recommendation is industry-specific. Each industry will have its own criteria for defining utility, which makes it harder for ecommerce businesses to adopt this type of product recommender tool across channels. The strong side of such recommendations is that the tool can consider industry-specific data when computing the utility. This, for example, can be business reliability or stock data essential for building an effective supply chain strategy.
Knowledge-based product recommender
Ecommerce product recommendation is done with the help of knowledge AI has about a user and their preferences. The decision on whether to recommend a product is made after the machine considers particular user needs. The correlation between a user need and a product that can solve it defines what suggestions a consumer will see on their product page. To enable this type of product recommender, you’ll have to unlock access to specific historical data on user and their behavior to help the machine make the right conclusions about your audience.
Hybrid recommendation engines
Hybrid recommendation engines are made by combining collaborative and content-based methods by either merging the two approaches to create a separate model or by adding the capabilities of one model to the system based on the other one. Hybrid recommendation engines have been quite effective in recent years, Netflix being a great example. Their users receive recommendations based on what similar users express interest in, as well as what they themselves have rated highly.
Boost your business with a recommendation engine
The advantages that recommendation engines bring to a business are undeniable. If you are looking to implement a robust recommendation system into your eCommerce store or need help updating your Magento store, contact us! We have extensive experience in both eCommerce and Magento development, and a record of helping businesses achieve their goals.