Customers Who Bought This Also Bought
This section, found below the âfrequently bought togetherâ section, focuses more on product discovery. Customers can scroll through a longer list of related products to find what interests them. The title of this section leverages on social proof, suggesting to shoppers that products here are trusted by people just like themselves. This screenshot was taken in a product listing for a laptop – like the previous section, the recommended products here are not other laptops, but accessories, peripherals, and software. This strategy encourages customers to add more items into their cart, instead of replacing the items in their cart.
Learning point 2: The name of your recommendation widget is important. It functions as a clue to customers about why those products are being shown, and it should inspire trust in your recommendations. The paradox of choice means that sometimes providing too many choices of products in the same category can overwhelm customers, so you can explore recommending complementary products instead.
Youre Our First Priorityevery Time
We believe everyone should be able to make financial decisions with confidence. And while our site doesnt feature every company or financial product available on the market, were proud that the guidance we offer, the information we provide and the tools we create are objective, independent, straightforward and free.
So how do we make money? Our partners compensate us. This may influence which products we review and write about , but it in no way affects our recommendations or advice, which are grounded in thousands of hours of research. Our partners cannot pay us to guarantee favorable reviews of their products or services.Here is a list of our partners.
Tip : Remind Customers Of Products That Theyve Looked At
Finally, the Browsing history shows me items that Ive clicked on. This works in two different ways. Firstly, it reminds me of products that Ive recently looked at, and was probably interested in buying. Secondly, it makes it easy for me as a potential customer to quickly revisit these items. I dont have to look through the history on my browser, or have to search for the product again.
Customers can easily find these items, making it more likely that they will go through with the purchase. Some people have a Dory-like memory and need to be reminded of things. So at the very least, it reminds customers of the products they were interested in.
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How To Better Manage And Improve Amazon Recommendations
Matt Klein has nearly two decades of technical writing experience. He’s covered Windows, Android, macOS, Microsoft Office, and everything in between. He’s even written a book, The How-To Geek Guide to Windows 8. Read more…
As the worlds largest retailer, Amazon has a lot of options to wade through and this expands to managing your account. One effective thing you can do with your account is improve your shopping experience with better recommendations.
Its obvious that theres a lot going on with your Amazon account. If you order tons of stuff, then youll likely have a substantial order history, which as weve shown, can be better managed. You also stand to benefit if youre a Kindle owner, which is far easier when you know where and how to do so on the website. You an manage your device, content, and other important functions, such as being able to deauthorize devices on your account.
Another aspect of the Amazon buying experience are recommendations, which are based on stuff youve already purchased. Today we want to talk a bit about buying recommendations, explain how they work, and, of course, how to improve them.
Examples Of Amazons Innovative Recommendation Strategies
If todays retailers want to maximize eCommerce conversion rates and revenue, theyll have to go beyond serving average recommendation experiences. Below are a few of the unique ways Amazon is implementing recommendations, which brands can repurpose and leverage to capture sales and increase average order value .
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Get Your Pricing Right
Finding the right pricing strategy for your products is tough enough as is, but add Amazons complexities and an open marketplace for other sellers to compete with you and you have quite a handful. However, if you keep some key considerations in mind, you can hopefully prevent other larger issues from arising.
Pricing parity
Your selling agreement with Amazon includes a pricing parity clause. Your item price and total price cant be lower at any other online sales channel according to the general pricing rule. This includes your own Shopify site. Avoid a potential account suspension for not following this mandate and ensure that you price Amazon as low as your other channels.
Automate pricing
Since Amazon is a marketplace, you may very well be competing against other third parties to win the buy box. There are several repricing tools available on the market, and Amazon recently released the Automate Pricing tool on Seller Central to help you automate pricing decisions. For example, you can set a rule to beat the buy box by 2% until you reach a certain floor.
Ensuring you are winning the buy box regularly, and being alerted when you are losing the buy box, is essential to growing your Amazon business. Note that pricing for vendors has a different process from marketplace sellers.
This benefits you not only by allowing you to sell more units in a short amount of time, but it also temporarily lifts your baseline business post promotion .
How Helpful Are Product Recommendations Really
Youre about to buy something online lets say a shirt but as youre adding it to your cart, you see three similar shirts the website recommends. So you click on one for a closer look, then another, and eventually maybe you buy one of them.
Every major e-commerce site uses product recommendations like these, and some say they generate a huge portion of their sales. A McKinsey & Company reportattributed 35 percent of Amazons sales to recommendations .
Anuj Kumar, a professor at the University of Floridas Warrington College of Business, wasnt buying it.
These estimates are hard to believe, he said.
Amid the overwhelming options in an online inventory, recommendation algorithms make items more visible to customers, which increases our likelihood of buying them. But they also lure our attention away from the original item, so its hard to tell if the retailer is actually coming out ahead.
Kumar points to two different factors that inflate the purported impact of recommendations. The first is something youve likely used when shopping online: browsing and search tools that help you narrow the field by category, color, brand, or other parameters. Those tools make it fairly easy to find what you want without the aid of recommended products, Kumar says.
Overall, product recommendations boosted product sales by 11 percent a more believable number than the inflated claims, Kumar says.
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How Recommendations Helps Increase Average Order Value Click
When a subscriber or customer receives an email from you, the goal is to get them back to your site to make a purchase.
Now with Rejoiners recommendations engine, you can intelligently serve people, top selling items or products that are frequently purchased together, inside your emails to increase engagement and click-through rate back to your online store.
Serving other products that are frequently purchased together in your emails, giving people to the chance to add more products to a cart they previously abandoned, which helps increase average order value.
Sending an 3-step abandoned cart email series with the last product someone left in their cart is a great way to get them back to your site, but if theyre not interested in that specific product anymore theyre more than likely just going to delete the email.
So if a product they left in their cart isnt going to peak their interest, the recommendations engine can act as a another way to encourage every person reading your emails to come back to your store.
Blue Nile do this perfectly with their cart abandonment email sandwich.
The first email is a standard abandoned cart email you left this Diamond Stud Earing in your cart.
The second email recommends different earrings from the same diamond stud category.
Emails 2 and 3 do a great job of giving the person multiple options to come back to the store and to either buy or start the buying process again.
Choose Your Fulfillment Options
As a seller, you have two fulfillment options: the do-it-yourself option or using FBA, where Amazon is responsible for receiving, packaging, and shipping orders.
Fulfilled by Merchant
You fulfill directly to customers and manage shipping, returns, and customer service. This is a good option for made-to-order products or for products that require a longer lead time for processing.
Fulfillment by Amazon
You send inventory to an Amazon Fulfillment Center and it ships products and manages returns from customers. You control how much inventory to send to FCs and you pay storage fees for the product in addition to a fulfillment fee for every unit sold to customers. Keep in mind you still own the inventory until a customer receives it.
In this model, Amazon handles payment from the customer and pays you every two weeks. You get access to Amazons customer service team that handles questions, returns, and refunds. Youll also get access to Amazon Prime and Free Super Saver Shipping to scale your business.
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How Amazons Recommendation Engine Works
There are in-depth discussions about .
Existing recommendation algorithms couldnt scale to Amazons tens of millions of customers and products, so they decided to develop their own.
Amazon currently uses item-to-item collaborative filtering, which scales to massive data sets and produces high-quality recommendations in real time.
This type of filtering matches each of the users purchased and rated items to similar items, then combines those similar items into a recommendation list for the user.
Their recommendation algorithm is an effective way of creating a personalized shopping experience for each customer which helps Amazon increase average order value and the amount of revenue generated from each customer.
Tips For Effective Personalized Product Recommendations
1. Put Product Recommendations Above the Fold
Position of product recommendations influence how effective they are. We found widgets placed above the fold were almost twice as effective as widgets below the fold.
2. “What Customers Ultimately Buy” Widgets are the highest performing
Out of the 20+ product recommendations types that were reviewed in this study, the most engaging recommendation type was what customers ultimately buy.
3. Use “Best Selling” Recommendations for new visitors
When a new visitor comes to your store, you don’t know what products to recommend.
The best practice is to supply the best sellers of your store toward the top. You can also consider having multiple widgets, one for each of your top categories.
As customers engage with your site, your product recommendation engine will begin to understand what types of products this customer is interested in, and supply more personalized suggestions.
4. Personalize Product Recommendations Based on Web Behavior
Position of product recommendations influence how effective they are. We found widgets placed above the fold were almost twice as effective as widgets below the fold.
This falls in line with our findings ondynamic content that increases conversion rate.
5. Inject Personal Recommendations into Emails
Another great way to personalize emails is via product injections. Software like Barilliance can inject product recommendations directly into the email.
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Enhance Sales & Average Order Value
One of the excellent methods to increase your revenue and average order value is to encourage your website visitors to add recommended products and offerings at the checkout page.
Recommendation systems allow you to drive much higher conversions and enhance average order value. You can bring multiple data sets into a recommendation algorithm using a recommendations engine. These data sets can then deliver relevant recommendations in real-time and allow customers to engage with your brand in real-time.
This kind and level of relevancy give a definite boost to your sales and average order value by exposing your customers to a higher volume of products that are likely to pique their interest.
Further, by leveraging various data algorithms and inferences about what the customer will like based on their past preferences or what has been purchased by similar customers, the recommender systems can systematically encourage additional spending while offering a much more engaging user experience.
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Recommendations On The Product Page
A product page or product information page is where visitors find detailed descriptions of a product and its features, and can choose to add it to their cart or order it right away.
The main aim of recommendations on these pages is to display the most relevant items and therefore provide a next step in users search and keep them browsing your site.
In most cases, the more time they spend in your shop, the higher the chances theyll actually make a purchase. In fact, in their ecommerce KPI report, Wolfgang Digital found that both time spent on site and pages viewed have good, positive correlations with revenue volume.
4. Similar products
Similar product boxes can be based on very different logics. The least complex one is simple category-based filtering, which can be implemented even without a recommendation engine .
If you combine this simple filtering method with meta-data based similarity , you can greatly enhance the performance, such as by recommending items of the same brand or same color from the current category). For this, you will need to have advanced recommender functionality available on your site.
One of the best-performing similarity based logics is a method called item-to-item collaborative filtering, a method pioneered by Amazon. Ill elaborate on this below.
5. Customer who bought/viewed this collaborative filtering
6. Personalized recommendations
To get a general idea about how these work, look at a simplified example below:
Spend Much Time Researching Product
Great content comes after a lot of research.
Research is where you get into the nuts and bolts of getting the raw material for your content. It may take some grunt work, but the amount of research you put into your content will show in the accuracy and thoroughness of how well you know your product.
If you dont put a lot of research into it, your content will come off as unbelievable and lack credibility. You will have very little hope of selling anything without doing a decent research on what youre selling.
When youre doing your research, dont limit yourself to a single website.
Read the customer reviews on Amazon, reviews left on the manufacturer website, industry authority sites and perhaps look at other sources on how it works and what others think about it.
You should also check YouTube for video reviews.
Case in point, the more research you do, the better prepared youll be to write a convincing product review that converts.
Needless to say, if you dont put adequate research into your product review before you write it, your product review will suffer in two ways.
One, no one will read it or take it seriously. That will result in horrible sales figure.
Two, if people dont read it or stay on your page, your bounce rate will be high, and search engines dont like website with high bounce rate.
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And Thats Why Rejoiner Has Created Its Own Recommendation Engine
Weve been so fascinated by Amazon over the years .
After we saw them rolling out their recommendations engine, we knew we wanted something similar incorporated into our software so that we could help online retailers increase increase sales by intelligently offering up and predicting what customers are likely to buy next and then dynamically serving those products into our customers lifecycle email campaigns.
What Is A Recommendation Engine Exactly
Recommendation engines are, at their core, information filtering tools that use algorithms and data to recommend the most relevant items to a particular user in a given context.
Here, an item can mean a piece of content, a product, or even a person . Recommendations can be powered by aggregate data, which determines the relevance of a certain item in relation to a given context, or user specific data, that fuels personalized recommendations.
Naturally, the types of products a certain ecommerce site sells and the kind of audience it caters to have an enormous impact on how recommendations should be used and presented in the store. Same goes for the kind of logics that work best for them.
Logic is kind of a niche word, although I will use it often in this article. For clarity, by logic, I mean the set of predefined rules and algorithms based on which recommendations are provided .
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The New Rules Of Customer Engagement: Handbook For Growth Marketers
Winning CX will come from Brands who can balance relevance, consistency and convenience to drive engagement. The kind of engagement that drives optimal customer lifetime value and real business impact.
Because of how well recommendation engines boost subscriber numbers through engagement and stickiness, facilitating such serendipitous discovery has turned into a high stakes multi-billion-dollar race for the worlds biggest digital companies. Personalized suggestions are implemented via software programs that crunch massive amounts of data to learn user preferences and come up with a list of recommended items for the user.
The customer personalization journeys of Amazon and Netflix demonstrate just how powerful recommendation engines can be. See how these online giants built cutting edge recommendation engines that keep subscribers coming back for more.
Amazon
A lot of Amazons fantastic revenue growth has been built on successfully integrating recommendations across the buying experience — from product discovery to checkout. Enabling personalized suggestions in e-commerce, is perhaps the number one reason for recommendation engines, because of what is known as the long tail problem rare, obscure items that are not very popular and dont drive the bulk of revenue. Recommending long tail items to shoppers is critical because if successful it has the potential of giving ROI on slow-moving inventory.
Netflix