Wonders Of A Product Recommendation Engine for Ecommerce Businesses[lwptoc min=”2″ depth=”6″ title=”Contents”]
Imagine yourself as someone who is interested in gaming.
If you are shown a series of gaming-related products, won’t you be interested in it?
This is the wonder of a Product Recommendation Engine.
Simply put, a Product Recommendation Engine is able to churn out a series of product recommendations that users may be interested in based on their taste and preferences. The data collected from the user footprint such as browsing history, search keywords, or purchasing habits can be used to predict what the users are interested in, and so determine the type of products to be recommended to them.
For ecommerce businesses, this means understanding your users at a deeper level and being able to target them with the most relevant products.
Why You Need A Recommendation Engine Today
What can you do to make your business different from others?
There are many competitors out there. But to convert your customers into loyal ones, you need to be able to differentiate yourself and create an experience for your customers to make them feel special and understood. Personalization can make a big difference in the game.
“59% of shoppers who have experienced personalization believe it has a noticeable influence on purchasing.”
Artificial intelligence (AI) is able to personalize recommendations easily and quickly. These recommendations are different from the usual word-of-mouth that is communicated between friends and families. Word-of-mouth recommendations may be subjective as what suits your friends or families may not be applicable for you due to the different preferences.
However, the Product Recommendation Engine is different. By digesting data such as the browsing history and personal preferences, the engine then delivers the most relevant recommendations to a specific individual.
With data and a carefully constructed algorithm, it is possible to create a strong personalized recommendation. As our AI ‘Brain’ starts to learn about your users’ behavior, the recommendations become even more laser-like accurate over time. This personalized experience targets customers with products that are relevant to their interests and prevents them from getting overwhelmed by a sea of products, especially on an ecommerce site. Not only will your customers be happy, they will be more likely to continue purchasing from you in future.
Making your Customers Happy
73% of consumers point to customer experience as an important factor in their purchasing decisions. Customer satisfaction is an important point for ecommerce businesses as it determines their future purchasing dollar and habits. Good customer service translates to an enjoyable shopping experience and a positive experience can bring many benefits to your business.
Unhappy customers can bring bad news to businesses. They may spread negative reviews about your business and you may risk losing 22% of customers when they find just one negative review. This results in a loss of sales and revenue, which is painful for businesses.
However, with personalized recommendations, you can keep your customers happy. More often than not, your customer wants to revisit a product which they have come across, yet they don’t remember it. With the “Recently Viewed” feature, it can conveniently bring your customer to the product they are looking for. Not only does this increase convenience and make your customers more satisfied, but it also helps to increase your sales too.
“74% of online consumers get frustrated with websites when irrelevant offers, ads, promotions appear.”
Source: Business 2 Community
Remember, frustrating your customers is the last thing you want to do.
Discover the Undiscovered
This, my friend, is one of the best wonders of having a product recommendation engine in your eccomerce website. It is the fact that they can discover items that have never crossed their minds or never knew they needed.
Customers can often be overwhelmed by the number of products available on ecommerce platforms and at times it may not be easy to find a product that they are interested in. With hundreds and thousands of products available, it is also easy to get distracted while browsing every now and then. With the wealth of data in your pocket, instead of making your customers fumble around to find something that they like, why not deliver something that they themselves are not even aware that they like or need?
There are so many products available and many are waiting to be discovered by people. However, browsing through every single product page can be too much of a hassle. The Product Recommendation Engine can help you find the right products for your customers with minimal effort, targeting them with products that match their interests and spurring them to make an eventual purchase.
Strengthen Customer Loyalty with Increased Customer Engagement
“79% of consumers say they are only likely to engage with an offer if it has been personalized to reflect previous interactions the consumer has had with the brand.”
Recommending products to your customers is good. But, recommending the most suitable products is vital. When a product recommendation is relevant to a customer or something that they are interested in, they are more likely to engage with it.
There may be some problems in allowing your customers to take their own initiative to search for products. Firstly, they may not even have an idea of what to search for. Secondly, they may get distracted midway or give up. Don’t let this be a waste of time for both you and your customers.
A Product Recommendation Engine allows you to recommend the best-suited products for your customers, and this will increase the likelihood of them engaging with these products compared to random products displayed. With engagement comes a higher possibility of them taking the next step to purchase the product.
With increased engagement with your business, customer retention is likely to increase and this paves the way for a loyal customer base, one where they will continue to shop with you in the future.
Ultimately, a business needs to be able to earn sustainable profits in the long run. Hence, revenue and profits are important and are important goals for any business. Offering quality products and services is one of the ways to ensure sustainability for your business. Another way is to understand what your customers want and give it to them, and this goes a long way for any business.
“35% of what consumers purchase on Amazon come from product recommendations.”
Imagine achieving that for your business. For customers who may not be looking to buy anything specific, they might end up purchasing something because they are recommended something highly relevant.
Even at the check out process, your customers may “unintentionally” stumble upon another product recommendation and purchase it. This not only increases your basket size, but the average order value as well. Together with the ability to create a personalized experience, it can generate more revenue in the long run.
How Recommendation Engine Works
There are 3 basic approaches to recommendation engines.
Content-Based Filtering relies on a user profile and the type of content the user interacts with. It assumes that if users express an interest in a particular type of content, they will also be interested in content with similar attributes.
The algorithm used focuses on keywords and the actions of users, which are the characteristics of the content. A user profile is then constructed to determine the type of products that the user is interested in. From there, product recommendations relevant to the user’s interests are generated.
To put it simply, this approach compares the attributes of various content with the user profile before making a recommendation accordingly.
Collaborative Filtering relies on the collection and interpretation of large amounts of user behavior data. It assumes that the liking for a particular content in the past will transcend to the future. By comparing various users with similar actions, it is possible to predict what a user may be interested in.
To put it simply, users’ behavior and actions are analyzed to identify trends and make accurate predictions based on similarity to other users. This is different from Machine Learning as it relies purely on deriving a prediction from the data collected.
Should you use Content-Based Filtering? Or is the Collaborative Filtering better?
While both are good, there are drawbacks to each approach as well.
User personalities are not taken into account in the Content-Based Filtering approach, while an inconsistent and insufficient database may skew the results for the Collaborative Filtering approach.
Why not utilize both in your product recommendation engine to get the best of both worlds?
It is possible to combine the above 2 approaches into what we call the Hybrid Recommendation Engine. This is a more effective approach as it takes into account both the type of content and the similarities between users.
Take an ecommerce website as an example. When a user visits a product page, Content-Based Filtering is used to identify a series of recommendations that are relevant to the product viewed, and Collaborative Filtering is used to narrow down the recommendations based on similarities between different users.
The result of using a hybrid approach is a more accurate prediction of product recommendations.
Types of product recommendation
There are many ways to deliver product recommendations, and here we explore the different approaches that can be done to provide customers with their personalized recommendations. Amazon is an amazing example of utilizing the different types of product recommendation in their ecommerce site.
1. Frequently Bought Together
After a customer adds a product into his/her cart, suggested products that are relevant to the product added are displayed. For example, if a customer purchases a washing machine, some suggested products that will be displayed may be clothes hangers or a clothes rack. The display of related products can help to further increase the basket size, resulting in more products purchased and so more sales.
2. Top Selling Products
Best-seller products may be displayed in an attempt to attract a customer’s attention. This approach is suitable for first-time customers where much of their personal information or user footprint is not yet available. By leveraging on such social proof, this strategic sales method convinces customers that these top-selling products are needed and purchased by many people.
3. Latest Products
New products are given a shout-out to inform customers of the latest products. This is also a great opportunity for retailers to showcase their latest launches. Similarly, this approach is suitable for first-time customers as personalization will be difficult without prior data.
4. Similar Products
When a customer is highly focused on a particular product, he/she may not consider a similar product that may be even more suitable for them. This is why we need to expand the options delivered to them to broaden their choices.
A certain amount of customer data is needed, such as their browsing history and taste and preferences. Thereafter, similar products may be recommended to them.
By understanding their needs and interests, it will result in better customer satisfaction and retention.
5. Recently Viewed Products
Have you come across a product and want to revisit the page again only to realize that you don’t remember the name of it?
It is common for many to want to find the same products that they have looked at before. By displaying the Recently Viewed Products by the customer, he/she can revisit the product with ease. With just a simple click, customers can view their search history, making their revisits to pages easy and convenient.
6. Popular Products
For customers who have a broad idea of what they are looking for but do not know the exact product they want to purchase, displaying Popular Products is a great form of advice for customers. This is an indication that these popular products are well received or of good quality. This also serves as a great form of social proof.
For example, if a customer searches for a steam iron, the most popular steam iron bought by other people will be displayed to the customer, allowing him/her to make a more well-informed decision that is easy and quick.
7. Who Bought This Item Also Bought
This series of products are offered to the customer based on the shopping behaviors and histories of customers with similar profiles. These recommendations may be deemed credible and reliable since they are based on actual purchases.
8. Personalized Recommendations
As we all know, personalization is key. Recommendations are provided based on the customers’ browsing histories, purchasing habits and behaviors. With a substantial amount of data, the recommendations made are highly relevant since it is based on the customers’ own tastes and preferences.
Some Final Thoughts
The Product Recommendation Engine is a remarkable solution. It allows us to make accurate predictions of recommendations just by analyzing actual behaviors and habits, something that we humans may not be able to do so with such accuracy.
The business industry is changing and becoming more competitive. To be able to retain the competitive edge, continuous improvements need to be made to serve our customers better. One way is to use the Product Recommendation Engine to better target our customers and identify their needs before they know it themselves.
At groundup.ai, we will support and guide you through the process of implementing the Product Recommendation Engine into your business. Using AI and Machine Learning, we can efficiently pull data and behaviors from your customers to create evolutionary changes to your business and use innovative solutions to drive business value.
Request a call from us today!