Apr 19, 2023

How Personalized Product Recommendations Can Drive E-commerce Growth

E-commerce is rapidly growing to become the primary retail channel for the majority of consumers worldwide. As customers get to shop more conveniently, their expectations for the digital shopping experience are increasing exponentially. 

Simultaneously, AI and machine learning have made their way into business systems with powerful recommendation engines. Aside from offering a cohesive shopping experience, retailers must display personalized product recommendations to meet surging customer demands.

That being said, personalized product recommendations do bring fruitful results - as 75.5% of businesses get a positive ROI from this practice.

That’s not all. 90% of customers are willing to share their personal information if they get a more convenient shopping experience. So, now is the time to implement personalization in your retail business.

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Why Personalized Product Recommendations Matter

Adding a personal touch to customers’ online interactions significantly improves their experience and increases conversion rates. By improving the presentation of products and adding relevance, personalized product recommendations help increase average order values. 

90% of customers prefer working with companies that personalize customer service.

Recommending products based on a customer’s past purchases or views makes them feel at home with your brand. This means that every time they reach out to your online store, they can start right where they left off the last time. 

A personalized product recommendation in e-commerce helps reduce shopping cart abandonment and encourages users to spend more time on your website. Instead of forcing your customers to search for a product relevant to their past purchases, you can offer them automated recommendations of similar products.

Personalized Product Recommendations Statistics

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How Personalized Product Recommendations Can Impact Your Business

Personalized recommendations are like an experienced and friendly shop assistant replacement for online stores. These have the potential to improve interactions and engagement with customers to keep them hooked for longer.

With giants like Amazon already mastering the art of personalization, most digital businesses still struggle to get it right. According to research, most eCommerce platforms consider only a few use cases to design their product suggestions. This results in a 50% efficient system that can sometimes show completely irrelevant recommendations to customers.

In order to improve the digital shopping experience for your customers, you need to rely heavily on AI-based solutions. A personalized product recommendation engine not only helps you develop a better marketing strategy but also takes into account multiple parameters to provide relevant recommendations. 

Recommend the Right Products to Your Customers

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Collecting Customer Data for Personalized Recommendations

While brands focus on delivering highly personalized shopping experiences, there is a risk of breaching customers’ privacy. Although customers crave personalized suggestions, they still worry about their privacy. So there is a fine line between just the right amount of personalization and violating a customer’s trust.

When collecting customer data for personalization, it’s important to build mutual trust and focus on your customer’s likes and dislikes. While you need to monitor their online journeys to understand their decision-making process, it’s also important to ensure that you don’t overdo it. 

Types of data to collect for effective personalization

Common Parameters Monitored by Recommendation Engines

The types of data you need to collect for effective personalization can be sub-divided into 4 categories:

  1. First-party data:

Data about customer activity across your website or applications, social media, and emails. It also includes information collected through surveys and feedback forms.

  1. Second-party data

Just like first-party data, this information is based on a customer’s activity on your company platform but is collected through a trusted partner in compliance with privacy regulations.

  1. Third-party data

This data is collected from multiple sources that refine it to assist your marketing ventures. It includes information like age, education, income, and websites visited by a user.

  1. Zero-party data

This relatively new term refers to the data that a customer proactively shares with your brand, including product preferences, communication preferences, and account configurations.

Techniques for collecting customer data

  1. Website Tracking: You can incorporate tracking tools to gather customers’ browsing data on your website. 
  2. Newsletter signups: By convincing customers to sign up for your newsletter, you can collect data like their preferences and purchase history.
  3. Social Media: Gather data about customers’ interests and demographics from their social profiles and activities.
  4. Surveys and Feedback: Ask your customers the right questions to understand their likes and dislikes.
  5. Customer Service Interactions: Identify and work on the concerns your customers raise during customer service interactions.
  6. Location Data: Determine nearby store locations and local interests in the customer’s area to offer relevant recommendations.

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Implementing Personalized Product Recommendations

The first thing your potential customers will see is your landing page. Considering that a new user coming from a direct traffic channel to your eCommerce site will not be looking for a product, you need to show recommendations that educate and inform. In the case of product, category, and cart pages, there are different types of recommendations.

Types of personalized product recommendations  

Popular product recommendations are the most commonly used method by marketers, but also one of the most effective. While this type of recommendation is generated based on the number of times a customer has seen or bought the given product, there are different implementations for different pages.

Landing page recommendations

Normally, when a recommendation engine has enough data about a particular customer it starts putting together personalized recommendations for them. In the case of landing pages, however, you need to cover new website visitors as well. When the system does not have any previous data about a new visitor, it goes into ‘fall back’ mode and starts producing more generalized recommendations.

Product page recommendations

For product pages, you need recommendations that let visitors know about your offering and features. These types of recommendations come with the option to add items directly to the shopping cart. They act as a call to action or the next step that a user can take while browsing your eCommerce website.

Product page recommendations are displayed with tags like ‘similar products’, ‘customers who viewed/bought this also viewed..’, and ‘people like you also buy’.

Cart page recommendations

As you may have guessed, cart page recommendations include items related to or ideally paired with the products a customer has added to their cart. At a point where the customer has made up their mind about checking out, personalized recommendations play an important role in increasing the average order value.

Category page recommendations

Considering that your customer has reached the category page of your eCommerce website, there is a high probability that they have the basic information. The recommendations to be displayed here should provide assistance with finding the relevant item and making their shopping journey easier. 

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How personalized product recommendations work        

Using customer data taken from different sources, personalized product recommendations show products that customers would like to buy. Ideally, the more personalized your product recommendations are, the better it is for customer engagement. 

The secret behind the working of personalized recommendations is recommendation engines. These engines analyze both customer data and product sales numbers to generate these recommendations. They are implemented by video streaming websites, retail platforms, and other customer-centric services.

Recommendation engines are also categorized into 3 main types:

  1. Collaborative filtering systems

This type of recommendation engine analyzes the online shopping data of over 1000 - 1500 customers to create a group of product recommendations that the customer should like. Collaborative filtering uses existing customer data as the basis for recommending products to new customers.

  1. Content-based filtering systems

These recommendations are based on conditions like “since you viewed this, you might also like so and so product”. The individual shopping habits and preferences of a customer are analyzed to offer content-based product recommendations.

  1. Hybrid recommendation systems

A combination of the two recommendation engine types mentioned above, the hybrid system uses all kinds of information to create recommendations. It uses the preferences of similar customers and the data from their previous orders to offer personalized recommendations.

Tools and technologies for implementing personalized product recommendations   

With technological advancements, the eCommerce world has been introduced to various recommendation engines. Depending on your application, XStak’s Recommendation Engine assists you as a self-service tool that creates personalized recommendations based on customers’ past orders.

It uses the perfect blend of collaborative filtering and content-based filtering to recommend products with similar tags and attributes to those that customers view. Another great feature of XStak’s Recommendation Engine, and also one of the reasons why you should choose it over the rest, is that it monitors real-time customer activity and offers recommendations based on what the customer is currently looking at. 

The recommendation engine helps you improve conversions and average order value by offering better recommendations. A high level of personalization means that you have better chances of convincing your new customers to come back for future purchases.

Improve Customer Engagement for Higher Revenue

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Best Practices for Personalized Product Recommendations

Since you are going to offer personalized recommendations with the help of automated data analysis, it’s important to look at some important factors. Here are some of the best practices to follow in order to extract the maximum benefits.

Importance of relevancy and timeliness

When it comes to offering personalized product recommendations in an effective way, timing is everything. You need to figure out when and how you should show these recommendations to your customers. 

If you have created the right recommendations but are not able to show them in the right places, the whole purpose of personalization is not fulfilled.

For this to work, you need to focus on customer segmentation and target the right ones at the right time. During the early stages, you can use broader segments and narrow them down when you have a larger number of customers. 

Importance of transparency and consent

As with any other product description in eCommerce, what you show your customers as part of personalized product recommendations must be true. There should be no compromise on transparency as online shopping is notorious for returned orders and bad reviews.

When you give your customers the guarantee of “what you see is what you get”, they get the confidence to add more items to cart. In case your product recommendations come with inconsistent descriptions, there are high chances that your personalization strategy will not work at all.

Importance of testing and optimization

Testing and optimization are a crucial part of all eCommerce processes. Unless you are testing your personalization strategies before deploying them, you may be unaware of some scary loopholes.

Even with the best recommendation engine, you need to be working in the right direction to be able to make things work. One of the techniques to test your new marketing strategy is A/B testing. 

With A/B testing, you will be able to figure out which recommendations are working on which pages. For instance, recommendations about trending products only work on the home page but not on other pages. Similarly, you can add a “package deal” recommendation on the cart page to compel the customer into buying a top with the pair of jeans they originally intend to purchase.

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Example of Effective Personalized Product Recommendations

Alkaram Studio

One of Pakistan’s renowned fashion brands, Alkaram Studio leverages XStak’s recommendation engine to drive profitability with increased average order value per customer. 

Alkaram Studio

Even with all the right marketing and advertisement campaigns, the brand struggled with conversion rates. With the product recommendation engine, Alkaram Studio is able to improve customer engagement and convince its customers to spend more with every order.

Customers are able to see the right recommendations for top-selling products on the homepage, as well as “frequently bought together” recommendations on the shopping cart page.

Improve Conversions with Personalized Recommendations

Explore XStak’s Recommendation Engine Now!

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With personalized product recommendations, you can offer your customers a better shopping experience. Making it easier for them to find what they can buy helps create positive customer reviews and repeat purchases.

Using the techniques covered in this blog, you can use your sales data and produce highly relevant product recommendations. Before you know it, your customers will begin to appreciate your brand more than ever.

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