July 3, 2020
Discover how Amazon’s recommendation algorithm drives 35% of its sales and how your business can benefit from recommendations.
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Recommendation algorithms are based on machine learning.
They use input about a customer’s interests to generate a list of recommended items. Many applications use the items that customers purchase and explicitly rate to represent their interests, but they can also use other attributes, including items viewed, demographic data, subject interests, and favorite artists.
Based on your sales data you should be aware of your bestsellers in every category of products.
Showing this type of recommendation is a good starting point if you don’t have large datasets on the visitors or they visit your online store for the first time.
A 2018 study found that if internet users were looking at two similar products—with a price difference of $2—73% would be more inclined to buy the pricier product with a higher rating.
Aside from driving increased sales revenue, reviews are a great way for shoppers to get to know a brand they haven’t heard of and build trust.
Customers expect brands to know them: their interests, their preferences, their past purchases etc. They expect a personalized experience while browsing online stores because it saves them time and shows them these brands care about them.
Online shoppers are reported to share their personal data in exchange for personalized recommendations: 80% of self-classified frequent shoppers will only shop with brands who personalize their experience.
In fact, shoppers trust Amazon the most when it comes to using their data responsibly.
The main reason customers share their behavioral data with online stores? To receive exclusive discounts on products they like.
Similar products recommendation is a filtering method which can go from basic filtering such as showing your shoppers products of the same category to more advanced filtering which results in recommending similar products based on tags, prices, colour, patterns, functionality, titles etc.
Robert Cialdini defined the theory of influence as based on seven key principles: reciprocity, commitment and consistency, social proof, authority, liking, scarcity, unity.
This type of recommendation, where shoppers are provided with products other customers have bought leverages the power of social proof.
What is social proof?
Social proof is when people look to the actions of others to determine their own. If other buyers like me shop this particular product, it means it’s worth buying it myself.
Customers are also buying is a collaborative filtering which works by collecting preferences or taste information from many users (collaborating). Collaborative filtering is used by Amazon.
When shoppers get to the shopping cart page, it’s a good moment and place to recommend other products based on the content of their shopping cart. Why? Because they are ready to spend money and if the recommendation algorithm shows them products that add value to their initial order (ie: accessories), they are more inclined to follow them up.
This type of recommendation provides shoppers with further information which could improve their knowledge in the form of fashion styling ideas or recipe ideas.
It’s a data-heavy recommendation algorithm but if used correctly, it can be one of the most effective.
Recently-viewed products recommendation is time-sensitive. It can uncover fashion trends, seasonal items etc.
This type of recommendation algorithm is based on products that somebody has been engaged with while browsing your site.
Amazon.com has been working on developing its own machine learning-powered recommendation algorithm (also named recommendation engine) since the late 1990s.
In 2003, Amazon.com published their paper Amazon.com Recommendations – Item-to-item Collaborative Filtering.
The paper states that the giant online retailer uses recommendation algorithms to personalize the online store for each customer.
“At Amazon.com, we use recommendation algorithms to personalize the online store for each customer. The store radically changes based on customer interests, showing programming titles to a software engineer and baby toys to a new mother”.
That’s the power of personalization and that’s why Amazon’s recommendation algorithm drives 35% of its sales according to a McKinsey report.
The item-to-item collaborative filtering is Amazon’s creation.
In their report paper, they compare this innovative filtering with the existing filters that other online stores were using at that time: traditional collaborative filtering, cluster models, and search-based methods.
Unlike other filtering methods, which focus on finding similar customers, Amazon’s item-to-item filtering focuses on finding similar items. “For each of the user’s purchased and rated items, the algorithm attempts to find similar items. It then aggregates the similar items and recommends them”, explain the paper’s authors.
Here are 3 main advantages of Amazon’s proprietary recommendation algorithm:
Amazon.com’s recommendation algorithm provides an effective form of targeted marketing by creating a personalized shopping experience for each customer.
Learn more: The history of Amazon’s recommendation algorithm
Website traffic boost. With a recommendation engine, you can drive more traffic to your website through custom emails or ads.
Increased user satisfaction. Online stores that provide shoppers with a poor user experience lose customers quickly. Every shopper wants to find what they are looking for fast and in a couple of clicks. Online stores that meet their shoppers’ needs and expectations earn valuable points in the user satisfaction area and increase the likelihood of turning them into returning customers.
Increased sales revenue. Online stores that implement a mix of recommendation techniques are more effective in generating increased sales revenue.
Increased loyalty and share of mind. Satisfied customers become loyal customers. And loyal customers keep your brand top of mind which means your online store is their first choice when looking to go shopping.
Increased shopper re-engagement. If a shopper abandoned their shopping cart and left the online store without buying anything, you may re-engage them by sending a personalized email related to their cart items.
Are you looking to improve shopper engagement?
Contact us now and we will draft a digital solution in 48 hours!
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