E-commerce platforms don’t have the benefit of a sales assistant who would accompany customers in their shopping journey in the Brick and Mortar stores. And this is indeed one of the biggest challenges that e-commerce platforms face today. The solution to this problem is Product Recommendation that would give your shoppers an experience similar or rather better than shopping in a physical store.
Here’s all you need to know about Product Recommendation to get you started.
What is Product Recommendation?
Product Recommendations are basically the two words that make up the name – recommending products to your customers. These recommendations can be some of your new launches, best-selling products, most popular ones, and more.
These recommendations are delivered across platforms and channels. They can be a part of your on-site shopping experience or communicated through emails, push notifications, and more.
The recommendations can be manual – which a lot of smaller stores choose – or an AI-based recommendation engine doing the work for you when the volumes are unmanageable by humans.
What is AI – based Product Recommendation?
Recommendation engines filter and recommend the most relevant products to a specific user on the basis of the algorithm and data. On the basis of every micro-engagement, the AI engine decides the next product to be shown to the user. The engine acts like an automated shop assistant and this increases the chances of conversion, manifold.
Benefits of a Product Recommendation Engine
Knowing what a product recommendation engine is, the next question you are most likely to ask is, ‘why should I use a product recommendation engine?’
Think of an experience in Myntra or Netflix where you purchased or watched something because it was recommended to you.
E-commerce companies have millions of products. And let’s say that I want to buy a product on Amazon. Now this means that I have to skim through thousands of product pages to select the product of my choice. So this is where product recommendation comes in handy where I easily get what I was looking for and the buying experience is a delight to me.
Here’s why a recommendation engine tool goes a long way in boosting sales.
A study of 300 ecommerce websites indicated that Product recommendations account for up to 31% of E-Commerce site revenues.While shopping online when your customers receive appropriate suggestions based on their taste and preferences you are more likely to retain them. It proves to be a critical factor in your personalization strategies as personalization increases the likelihood of a prospect purchasing from you by 75 percent and all this ultimately contributes to the success of your e-commerce business.
Checkout: Increase Online Purchases through Omnichannel Customer Experiences | Netcore Smartech
How do Product Recommendation Engines Work?
A recommendation engine captures the essence of customer behaviour and gives you the direction you need to devise your marketing strategies to attract and retain customers.
But before you put your recommendation engine to action, you need to understand what type of customer you are looking at and the kind of information you have with you about that customer. Based on these factors you choose the type of recommendation engine you employ.
- ‘Content-based filtering method’
Take an instance where you have customers who make multiple visits to your site. Now in these visits you can record the likes and dislikes of your users and based on their browsing history you can offer similar recommendations using the ‘Content-based filtering method’ approach. The underlying concept of this particular method is that if your users have liked a particular item in the past, they are most likely to enjoy a similar item that you recommend.
- ‘Collaborative filtering method’
A second approach and a more complicated recommendation – ‘Collaborative filtering method’ is based on the simple concept that if there are two users who have similar taste and preferences, then we assume that a product purchased by one user will be liked by the other user. This method gathers data about customers who have purchased similar products and then you stitch it together to recommend items liked by customers whose choice is similar to yours.
- ‘Hybrid filtering approach’
Now, what happens when you combine the above two approaches? Well, you can make more accurate recommendations. A brilliant example for a ‘Hybrid filtering approach’ is Netflix that suggests films/movies by comparing user habits and also makes recommendations on the basis of what a user has liked in the past.
And this is how recommendation engines work. They gather data, analyse it and based on that they provide relevant recommendations.
Related: E-commerce: Delivering Delightful Customer Experiences Through Personalizatio
Kinds of product recommendation:
Now that you have a great variety of items to display on your website and more product recommendations to choose from, you can deliver product recommendations on the basis of how your customers behave instead of simply broadcasting recommendations to your visitors. Here are a few types of product recommendations that are widely used:
1. Trending products – This is more of a real-time recommendation that shows products that are popular or trending at that particular instant when the customer is shopping online.
2. Previously viewed – Only 17% of your customers browse with the intent of purchase in their first visit. They will be scrolling through competitor’s sites before making a purchase decision. And switching between multiple sites might confuse your visitors and they find it tricky to recall what they last viewed on your site. But with this type of recommendation, visitors can pick up where they left in the last visit.
3. Similar products – As a customer, when I am looking for a particular product, I can also see a lot of other products which I am not interested in. But with this recommendation type, you can display similar products next to each other to make your customer’s search hassle-free.
4. Recommended for you – This is commonly used by the top dogs like Amazon. This particular recommendation contributes a good amount to revenue generation. Reason being, it is purely based on a particular customer’s previous purchases and personal preferences.
5. Frequently bought together – You would always want your customer to have a larger cart value. The best way to do that is by employing this particular recommendation type that would give complementary product suggestions to your customer. For instance, if your customer has purchased a phone, you can suggest a case/cover. Or if they’ve bought clothes, you can invite them to pair it up with accessories.
6. What people like you bought– If you give your customers options of what similar people purchased, it’s more likely that they will follow suit. As a customer, it’s nice to know what people like me have bought because I tend to believe in their choice.
7. People also viewed – This type of recommendation shows what products people viewed. And usually your customers are very much interested in knowing what others did. This often leads your customers to products they might not have even considered before. It helps them navigate around the site thus enhancing their experience.
These recommendations are not hard to implement. All you need is an omni-channel platform like Smartech to integrate these recommendations. You can pick any recommendation depending upon the stage of customer journey and make your e-commerce experience a delightful one.
Where to Promote Product Recommendations?
Should you have your product recommendations in the cart and on the product page? How about your homepage? Are there more places to promote? The answer to all these is YES. The recommendations work best when they are promoted across multiple touch-points. Here are few locations to consider:
1. Product detail page – Relevant recommendations on the product page offer a ‘next step’ in your customers search to keep them browsing on your site for a longer time. This increases the chances of them making a purchase.
2. Category page – This page essentially narrows down products to a specific sub-category and helps the users find what they are looking for. You can recommend products on the basis of deals in that particular category or recently viewed items.
3. Homepage – Customers landing at the homepage might not necessarily be there with the intent to purchase something. So showing top deals, offers, discounts and trending products could lead to sales.
4. 404 Pages – Instead of displaying an ‘oops, we’re sorry’ on a 404 page you can optimise it to offer relevant product recommendations to display perfectly fitting proposals to your customers and that you can convert the traffic on this page.
5. Cart – Your customers arrive at this page when they have decided to make a purchase. And if at this point you recommend products similar to those in the cart then it’s likely to boost the cart value. A study revealed that product recommendations in the cart were among the 10 best performing recommendation types.
6. Correio electrónico – Emails have always proved to be one of the best channels for increasing conversion rates. In addition to the standard transaction emails, sending personalized recommendations can influence the customer to back to your site.
Tapping on these touch-points and promoting the most relevant and engaging product recommendations that offer more value to your customers, is what will fuel transactions along the way.
Future of Product Recommendations:
Product recommendation has come a long way from hand-picked or personally curated to automated recommendations based on commonly browsed products to giving best recommendations at a more individual level based on customer’s preferences, browsing history and many more aspects.
Recommendation engines of the future will be playing on more and more in-depth customer data taking into account each individual’s detailed actions, engagements with the site.
E-commerce platforms need to deliver real-time recommendations to their users. You have to show what is relevant to your customer today and not what was relevant last week. Recommendation engines of the future would put together multiple algorithms to produce a single strategy that would offer recommendations based on more factors.
In fact, the future of recommendation goes much beyond simple recommendations on your homepage. The user journey and experience across all channels will be such that every layout, navigation, category/brand selection, and views will be highly personalized and unique to every customer.
You can also read up on: 8 Post-COVID E-Commerce Personalization Strategies for Re-Growth
To know more, contact us to Schedule a Demo.