Both e-commerce and retail companies struggle to track changes in customers' preferences and continually update their offerings to deliver accurate recommendations.
The solution to such a problem is the use of AI-driven product recommendation engines.
Both retail and e-commerce businesses use ML-based personalized product recommendations to select items to display across the web, social media, and email.
How Do Product Recommendation Engines Work?
Product recommendation engines generate recommendations and predictive suggestions for special deals tailored to individual audiences.
They can review the data and use the results to create the right, customized audience profiles.
The recommendation engine will consider these audience profiles to produce products or content that cater to audience interests.
As a result, audiences might receive follow-up emails from their favorite brands based on their most recent site interactions or purchase history.
The engine can inspect the following kinds of consumer data:
- Browsing history
- Present buying behavior
- Interests
- Previous purchases
- Wish lists
- Recently searched products
- Shopping carts
- Opinions
It can smartly understand consumer intent and add suggested items to website searches, ads displayed on web pages, marketing brochures, and apps.
The engine is based on sophisticated algorithms. Such algorithms consider large amounts of consumer data, such as search behavior, interests, and purchase history.
These algorithms enable set procedures to automatically generate relevant recommendations after analyzing consumer data.
Later, the system delivered the best recommendations to every individual.
When fresh customer data is available, the system implements that criterion and provides upgraded suggestions.
A tailored product recommendation engine involves predefined rules set by the organization to filter and categorize products from an online store.
This process uses product data, including reviews, sales, and various perspectives, to identify popular products in multiple ways.
The presentation of these outcomes is simple and follows the same order as the products shown on the homepage or category pages of any website.
Later, that can be used to influence purchasers at each step of the visitor journey.
Based on visitor-specific information, such as their most-viewed products, categories, and previous purchases, both e-commerce and retail product recommendation engines can provide accurate recommendations.
These product recommendations use AI in conjunction with machine learning algorithms and natural language processing to fuel limitless features of the customer experience, such as:
- Email campaigns
- Category, product, and add-to-cart pages
- Personalized social or display ads
- Home pages
- Discounts within search results
Product recommendations serve as a platform to increase the business's revenue.
Different Types of Personalized Product Recommendations
You can find numerous types of personalized product recommendations, each with its own specialties. You have to decide which type is best suited to your business.
Let's illustrate this with Amazon Prime:
Trending recommendations
These recommendations are judged by social proof within a specific geographic region or by popularity.
For instance, decisions can be made based on data from Google Analytics and social media.
Personalized recommendations
The Amazon Prime recommender system predicts what viewers will like to watch at the moment.
Business-oriented recommendations
These are crucial for Amazon Prime for two reasons:
- They have invested more money in producing them and are available only on Amazon Prime
- When viewers watch content, Amazon Prime must pay the content owners. However, if Amazon Prime is the owner, they save a lot of money and boost their revenue.
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What are the Three Types of Recommendation Engines?
eCommerce product recommendation engines vary based on the specific data they gather and how they use it to recommend items to clients.
There are three primary approaches:
Collaborative filtering
It analyzes data from various clients to forecast which products will meet individuals' needs, then offers compelling product suggestions.
For instance, a consumer looking at a moisturizing cream on a personal care website may look at recommended products purchased by others who viewed the same item.
Similarly, they view products purchased alongside the moisturizing cream, such as a facial cleanser.
A collaborative filtering system is the best choice for businesses that have access to a wide range of consumer data.
This filtering system's main drawback is its tendency to produce false predictions. It is possible to mistakenly assume that a customer will like a product just because others do.
In collaborative and content-based filtering recommendation systems, propensity models can be deployed within a CDP to generate relevant recommendations when historical data are scarce.
Content-based filtering
It examines individual customers' preferences and buying behavior.
A content-based filtering system generates a personalized profile and offers recommendations based on the customer's personal interests.
A visitor who frequently visits a website will have an optional profile built on any of the following data:
- Age
- Geolocation
- Social presence
- Browsing device
- Purchase history
The AI engine analyzes all the aforementioned data to predict what the visitor might want to buy.
Hybrid recommendation system
It provides mixed filtering options, especially collaborative and content-based.
In short, a hybrid recommendation system leverages information from related user segments and an individual visitor's previous preferences.
Hybrid systems typically conduct these inspections separately and then merge them to offer personalized online recommendations.
Why Do Businesses Prefer Personalized Product Recommendation Engines?
Recommender systems in machine learning can act as a link between shoppers' preferences and data science.
According to 98% of marketing professionals, personalization strengthens client relationships, and they have doubled their ROI using advanced personalization.
Businesses prefer personalized product recommendation engines because they enable them to leverage clients' behavioral data to enhance customer service strategies and generate potential ROI from their marketing campaigns.
Relevant recommendation engine technology can increase awareness of the products or brand and boost customer satisfaction in multiple ways.
Furthermore, a research report by Barilliance states that personalized product recommendation systems can generate up to 31% of eCommerce sales revenue.
What are the Benefits of a Product Recommendation Engine?
Optimized inventory
Providing visitors with what they need helps a business avoid stocking up on unwanted products.
Here, personalized product recommendations come into play. It provides inclusive insight into which products are getting more impressions or clicks.
Based on this, e-commerce and retail businesses can decide to optimize their inventory.
Saves time
The most time-consuming and most challenging task for entrepreneurs is manually setting up their store for product suggestions, cross-sells, and upsells.
In this case, there is a high likelihood that customers might encounter irrelevant product recommendations. They don't waste time leaving the website.
Businesses can overcome these troubles using AI-based product recommendation engines.
Businesses need to set up their merchandising rules once, and AI engines automatically produce personalized recommendations for customers.
Thus, there is plenty of time left for businesses to concentrate more on improving and managing operations.
More conversions
If an organization expects more prospects to subscribe to its mailing list, it can achieve this by embedding a personalized product in an opt-in form.
After sending product catalog emails to subscribers, the likelihood of purchase will increase. Isn't it a good idea to expect higher sales?
Greater user engagement
Trust is the foundation of greater customer engagement.
Users like to feel that the business understands them, and suggesting relevant items will help nurture brand loyalty, encourage more website visits, and enhance user satisfaction.
Product recommendations in e-commerce play a key role in engaging users and reducing their search effort.
Product Recommendation Engines: Do All Personalized Recommendations Get Delivered?
No. Few recommendation engines provide non-personalized suggestions based on multiple sources of information, such as collaborative filtering, business rules, and social proof.
Let's see how it all works:
Collaborative recommendations
Customers who viewed those glass hand soap dispensers looked at these study trays.
Social proof
Products with more ratings, best seller ranks, and trending items are displayed in the "seen" category.
Business rules
Marketers display products from a category, including best-selling items, on-sale products, and complementary items.
What are the Challenges with Product Recommendation Engines?
Even though recommendation engines for personalization have become an essential element of daily business operations, building robust product recommendation systems creates crucial challenges.
Let's explore such challenges in detail and how to overcome them:
Data scarcity
Data scarcity is a common challenge in recommendation engines with many shoppers and products, but only a few of them are connected.
The best way to address data scarcity is to leverage matrix factorization techniques, such as SVD (Singular Value Decomposition) or NMF (Non-negative Matrix Factorization), to handle missing values and generate recommendations.
Another way to solve this problem is to employ collaborative filtering systems, such as user- or item-oriented methods, to suggest products based on similar products or users.
Cold start problem
When there is insufficient data for new users or products, the cold start problem arises.
For instance, a fresh shopper might have visited an eCommerce site and registered, but there would be no purchase or browsing history to draw upon when making relevant recommendations.
The most powerful way to address the cold-start problem is to use content-oriented filtering algorithms.
This method suggests products based on their features, such as price, description, and category.
For instance, if a new user is looking for a mobile, the system can suggest mobiles with features similar to those the user has previously seen or bought.
Diversity
Diversity is crucial in recommendation engines because shoppers want to discover fresh and compelling products, rather than just the popular ones.
The problem with recommendation engines is that they may suggest only well-liked products, leading to insufficient diversity.
The best way to assess diversity is to use metrics such as novelty and entropy to evaluate the diversity of recommendations.
Another approach is to use serendipity-oriented recommendations that suggest unexpected products that match shoppers' interests.
Privacy
Privacy has become a critical concern because product recommendation systems typically need access to shoppers' data, such as purchase and browsing histories, to offer customized recommendations. However, shoppers may not be interested in sharing their data.
The advanced approach to solving this issue is to use anonymization techniques such as hashing and encryption to protect shopper data.
Another approach is to use dissimilar privacy, which inserts random noise into recommendations to protect customer data.
Scalability
Scalability is another challenge in product recommendation systems when massive databases contain millions of shoppers and products.
A product recommendation system should easily handle a large volume of data and produce recommendations immediately.
The powerful solution to overcome this issue is to use distributed computing frameworks such as Apache Hadoop or Apache Spark to manage massive databases.
Another way is to cache predetermined suggestions and make them easily accessible.
For instance, a recommendation system for an e-commerce website may cache suggestions for frequently viewed items or popular categories to enhance the recommendation process.
Use Customer information management (CIM) for better Recommendations on your recommendation engine >>>> Schedule a call
How Do Product Recommendation Engines Help Your Retail Business?
In the retail and e-commerce sectors, personalized AI product recommendation engines increase conversions by supporting both upselling and cross-selling.
They can decide whether down-selling or up-selling is the best approach for the business based on the situation.
The factor that determines which approach is optimal depends solely on an analysis of each individual's intent.
With the introduction of propensity modeling and predictive analytics, retail businesses can leverage insights gathered from customer journeys and sentiment to uncover customer intent.
Based on this analysis, they can personalize all interactions to meet customers' needs better.
Examples of Product Recommendation
Examples of product recommendation technology are illustrated below:
Amazon:
You might have observed various recommendations on every device, page, and channel while browsing Amazon for online shopping.
These are impressive and unique strategies to impress shoppers.
Amazon offers recommendations by comparing related items and combos, and encourages shoppers to sign in if they log out.
Thus, it uses recommendations as a secret weapon to change users' mindsets and compel them to take relevant actions.
Netflix
It relies on data science, AI, and machine learning to suggest accurate recommendations for 100 million subscribers.
Netflix analyzes users' streaming history to predict what they want to watch next.
These innovative technologies help it customize every subscriber's experience and produce billions of dollars in revenue.
Spotify
Apart from OOT, eCommerce, and retail, the music sector also leverages ML and recommendation engines.
Spotify's recommender system offers real-time suggestions by analyzing songs users skip, songs they add to their playlists, their favorite artists, and the songs they frequently listen to.
Spotify uses machine learning in its recommender systems to send playlists to 100 million subscribers every week.
This tailored list comprises approximately 30 new songs to encourage their exposure to music.
YouTube:
It uses an effective recommendation system with complicated algorithms to refine content depending on the user's age, previous browsing history, search terms, etc.
YouTube relies mainly on advertising, so it uses an effective recommender system to match suitable ads to users based on past data.
How to Pick the Perfect Product Recommendation Engine?
Choosing the right product recommendation engine for retail is not easy, as there are multiple options available in the market.
So, you have to consider the following features when selecting ML-based recommendation engines for your business:
Relevant recommendations
The system should have strong customization capabilities and use ML algorithms to analyze user interactions with the online shop and recommend products based on their interests.
Effortless integration
It's crucial to choose a recommendation technology that integrates seamlessly with your e-commerce or retail business.
Ensure the technology has APIs or plug-ins to facilitate integration.
Once the integration is complete, the engine can access and analyze user data, including search queries, purchase behavior, and past browsing history, to make tailored suggestions.
Hence, an effortless integration process will save valuable time and resources, enabling you to focus on different areas of the business.
Scalability
Business requirements will change over time, so you need to use a system that can adapt to those needs.
A scalable system will enable you to monitor multiple products, data, and users without degrading your business's performance.
In most cases, retail and e-commerce companies should scale up to billions of SKUs.
That's why it's mandatory to assess whether the selected system can handle such large amounts of data.
A cross-sell rules system
Intelligent product recommendations work better when consistently implemented across channels. Recommendations that only suit the web or email.
Powerful collaborative filtering
There are numerous choices for non-customized suggestions.
Ensure your system includes options like "Frequently purchased with this," "Compare with related items," and "More from repeatedly purchased brands."
Indicate fresh products
Alert users about updated items by sending "There is a fresh version of this product" notifications.
How Do You Create a Product Recommendation Engine?
A robust product recommendation solution can be challenging to adopt. Below are some easy ways to create a product recommendation engine:
1. Understand customers
The first step is to understand your audience. This involves knowing your audience's journey, which is divided into four steps:
Awareness: An audience has a problem or requirement, but it hasn't been fully defined.
In this step, provide "top-selling items" as suggestions.
Consideration: Soon after defining a problem, an audience thinks of probable solutions
In this step, filter the suggestions to include choices or substitutes for the current solution, as well as upsells.
Decision: An audience gets rid of a few solutions to concentrate on a few or one that they will buy
In this step, offer additional reasons to praise the current solution, but stick to their initial decision to purchase.
Validation: After making a purchase, does he feel satisfied with the product?
Re-provide extras, refills, and opportunities for repurchasing.
If you don't have enough information about 1st-time consumers, target product suggestions to their stage in the purchasing process.
To get a better idea of the consumer journey in detail, you should:
- Notice user data
- Interviewing consumers
- Monitoring traffic paths on the website
- Engaging consumers on social platforms
- Speaking to people who have direct interactions with consumers
2. Determine products based on relevancy
Soon after inspecting user journeys, the next step is to implement the merchandising rules.
You consider the following strategies:
Consumer data will be the key to determining a product's relevance.
As explained, you can look at your customers' purchase history and past visits to showcase personalized recommendations.
Here, you will remind consumers that they have saved products in case they added them to the cart but didn't complete the purchase. Also, you will provide them with alternate products to encourage them.
Moreover, if you have not collected data from customers' past visits or have limited data, you need to use data from a new visitor's current session to select relevant products. This might involve:
- Location of the visitor
- Browsing history
- Key phrases that led them to your site
- Ads that directed them to your site
- Social media activity that led them to your site
Trends: Product trend analysis is the basic foundation for making better product suggestions when you lack customized visitor data.
Here are a few suggestions:
- Suggest the best product sellers & most purchased products
- Suggest periodic products, but display them at the beginning of the season. As the season progresses, their importance reduces
- Suggest discounted products & sales. If you offer bonus gifts or free shipping, users love these offers and make purchases even if they were planning to buy those products later.
Page context: It's challenging to identify what led potential buyers to your site when they use a private window or incognito mode in a browser.
In such cases, you have to tailor product suggestions based on the page they are browsing and your knowledge.
Homepage: It should have a mixture of trending products and bestsellers.
Category pages: Both trending category items and bestsellers yield better results.
Product pages: Consistently offer identical items that are the right alternatives.
Also, use past data to predict what others purchase after seeing the product, and record the current session to filter recommendations based on users' browsing behavior.
Shopping cart pages: Offer additional products rather than alternatives.
3. Selecting the perfect recommendation engine
It's not bad practice to start with manual product suggestions.
Once your brand grows and you start adding more items, as your visitors increase and diversify, it becomes challenging to manage manual product suggestions.
It's not easy to implement periodic variability to change shopper trends and behavior.
Having relevant product recommendation technology is essential to getting the expected upsell outcomes.
Product recommendation systems using machine learning help you save time and money by providing higher-quality suggestions.
4. A/B Testing
In the beginning, A/B testing is essential to boost confidence that you are using the right recommendation concept. However, you can't stop testing even after getting more conversions.
Always be ready with recommendation techniques to execute frequently. They might produce results initially and later fail to achieve the same results.
Test various recommendation strategies consistently, regardless of whether sales drop.
A few modifications to attempt to involve:
- Suggesting various items
- Suggesting successful items on multiple pages
- Providing items at various steps of the user journey
- Providing various deals or sales
- Altering the look or design of the recommendation display
- Altering the position of suggestions
- Inserting suggestions in fresh places like pop-ups, more pages, and so on
- Eliminating rules to grant more control to AI software
Over time, your e-commerce product recommendation engine should learn to improve its recommendations, enabling you to modify a few merchandising rules alongside automation.
5. Go beyond your brand
Adopt product recommendations into:
- Display advertising
- Social media marketing
- Email marketing
Product recommendations are a boon for remarketing, as you can utilize them to attract fresh visitors and potential customers.
For instance, geo-targeted display ads can produce better revenue than generic display ads.
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How Do I Measure the Success of Recommendation Engines?
Measuring the effectiveness of a product recommendation engine is challenging but not complex.
A product recommendation system should increase the following metrics:
Variation in site browsing time
A shopper should show more interest in a product feed on the website than a general one.
However, it will not enhance browsing time. Instead, in terms of conversions, browsing time can be reduced. Visitors are getting quick results.
Increased CTRs on email campaigns
Both triggered and promotional efforts (browse and cart abandonment) could be more engaging with the addition of suggestions.
Increased click rates on websites
When the item feed is customized, click rates should increase as shoppers see what you've recommended for them.
Increased open rates for emails
Category details can be used for subject lines to compel customers to open emails with accurate recommendations.
Higher AOV (average order value)
If your suggestions are designed to encourage shoppers to purchase items that match what they are looking for, the average order value should increase.
Increased sales
An increase in sales is a powerful metric, whether from email campaigns or complete retail or e-commerce sales.
How Does Customer Information Management (CIM) Enhance Recommendation Engines?
To deliver personalized experiences, high-quality data about the visitor is needed.
Information, including transaction history, product feedback, customer service records, and browsing records, is internally generated and captured to provide detailed insights into visitors from which new experiences can be derived.
Business owners use rule-based approaches to address the issue of data fragmentation.
The drawback of the rule-based approach is that it supports only a limited number of scenarios within a predefined set of rules, leaving business owners unclear about what's required and excluding them from other possibilities.
These drawbacks led to the introduction of ML algorithms into customer information management (CIM), which support use cases involving real-time audience data.
In CDP or CIM, machine learning algorithms are trained to generate suggestions by combining historical and real-time audience data.
Later, store them in a dashboard and use them as a response technique to activate campaigns across various platforms.
How Express Analytics Can Help?
Express Analytics' AI product recommendation engine helps you align your recommendation efforts with the audience's stage in the buyer journey or lifecycle.
The company's product recommendations merge real-time information with critical merchandising rules and advanced machine learning to deliver the most accurate recommendations for every audience.
With Express Analytics, both retail and e-commerce companies can develop a recommendation strategy that aligns with their business goals.
With our simple-to-use dashboard, it's easy to A/B test different types of recommendations and implement the ones that yield the best results.
Conclusion:
Building a powerful product recommendation program is a challenging, ongoing task that requires a multidimensional perspective and expertise in ethics, machine learning, and data science.
By incorporating innovative concepts and developing ethical frameworks, you can build the best product recommendation engine that meets shopper needs and fosters loyalty and engagement. In the end, productive recommendation systems have the power to change the way you find and engage with products, services, and content.
References:
Your Guide to Personalized Product Recommendations in E-commerce