How Artificial Intelligence Is Revolutionizing E-Commerce Product Recommendations

AI Product Recommendations in 2023 E-commerce Matter

Product recommendations are not a new concept. We know how they work, why they work, and what value they deliver. However, artificial intelligence redefined the limits of said value and brought it to an entirely different level. Modern applications of AI in the e-commerce industry are boundless and can improve many key aspects of your platform.

Personalization

The algorithm analyzes large amounts of data to identify patterns and make predictions about what a customer is likely to be interested in. This allows businesses to personalize the shopping experience and offer more relevant product recommendations. Given that 74% of consumers heavily favor tailored content, e-commerce personalization with AI is your go-to.

User Experience

In this era of convenience, СX can make or break the relationship between your clients and products. Under AI guidance, users can find what they need more easily and feel like their preferences are understood. Here, customer experience personalization is an ace up your sleeve.

Customer Engagement

With the attention span decreasing, it is the appeal and accuracy of the offer that decides the bounce rate. AI-based selection where each product speaks to the customer is bound to capture their interest. In fact, Accenture reports a 91% increase in the likelihood of continued shopping when provided with personalized recommendations.

Conversion Rate

AI can be the driving force that encourages consumers to come back. As stated by Barilliance, upon interacting with a recommended item, clients are 5.5 times more likely to convert. AI-driven insights will maximize the precision of offered items and draw in users who perceive the product page as their personal space.

Average Order Value & Revenue

For online shopping, AI can make accurate predictions regarding products of interest. By tailoring recommendations to each individual customer, AI increases the likelihood of users making a purchase. In turn, this enhances up- and cross-selling, propelling AOV and ROI. And Forrester’s study showed that 77% of clients would pay more when there is customized experience.

Customer Loyalty

A survey by Salesforce demonstrated that 70% of consumers who recognize the provided personalized approach feel that companies earned their loyalty. In e-commerce, AI recommendations can enhance the delivery of an atmosphere unique to each user. Hence, your platform can become a safe haven for your clients’ purchase needs.

Inventory Management

To no surprise, even in e-commerce inventory management, AI has its merits. Through AI recommendations, it is easier to forecast and track demand as well as optimize prices. Further, for the e-commerce supply chain, AI translates to improved visibility and flexibility.

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Types of AI-Based Product Recommendations

Different recommendation algorithms can be optimized for various objectives and user scenarios. AI can even be a part of your e-commerce marketing. So, you can either choose which option is the best match for your business needs or mix them for a more customized approach.

Collaborative Filtering

CF is a comparison-based recommendation process. With it, AI groups users with similar purchase and preference histories, draws parallels with other consumers, and offers items capitalizing on related client tastes. Say user 1 liked products A, B, and C. And user 2 enjoyed products A and B. The system identifies this commonality and can recommend product C to user 2.

Collaborative filtering works best when there is ample data, a high degree of overlap between user preferences, and a need for personalization. In cases of lack/absence of user behavior data or striking polarity of preferences, CF would not be too effective.

Content-Based Filtering

Content-based filtering considers attributes of products a client viewed or ordered prior. Here, AI makes suggestions centering on characteristics that echo previously purchased items. For instance, an AI algorithm notices that a person often selects healthy foods. Thus, it may recommend other groceries with related features, such as “low carb”, “high-fiber”, “vitamin-rich”, etc.

Contrary to CF, this technique works well for new or niche products that do not have a lot of user data available, swiftly avoiding the cold-start issue. Consequently, it can only recommend items with similar attributes and will not push products it considers of no interest to the user.

Hybrid Filtering

This option combines the abovementioned variants. First, AI applies collaborative filtering to generate a set of similar items. Then, content-based filtering is used to select precise products by taking into account their features and user preferences.

For example, consumer A watched a series of adventure movies, and consumer B reviewed half of them. AI recognizes this match and can offer similar films to consumer B. But before doing so, it considers the second user’s preferences for actors, directors, length, etc. In short, hybrid filtering moves from general to specific, canceling out the discrepancies between collaborative and content-based filtering.

Reinforcement Learning

RL is a machine learning method that relies on trial and error to condition desired behavioral patterns. Briefly, the algorithm learns to take actions in an environment to maximize a reward signal. In other words, it studies products to recommend (action) to a user based on their history and preferences (environment) to secure a purchase (reward).

RL is most beneficial in situations where the environment is dynamic or uncertain, i.e., the state of the environment may change over time or may not be fully observable. For that reason, AI organizes a decision-making strategy that adapts to changes in the environment and incorporates uncertainty without requiring explicit modeling of that environment.

Association Rule Learning

ARL focuses on what products tend to be purchased or viewed together and uses this information to suggest other products that might be of interest to consumers. For instance, if many users who buy running shoes also buy fitness trackers, association rule learning might recommend fitness trackers to users who have recently purchased running shoes.

Association rule learning is a more specialized approach to AI-based recommendations. Commonly, it is excellent in situations where the goal is to offer complementary or frequently purchased together products. ARL is particularly effective in cases where the user behavior data available includes information on the co-occurrence of items in transactions or interactions, such as purchases or views.

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Case Studies on the Utilization of AI-Based Product Recommendations

AI-powered e-commerce recommendation systems (RS) have been applied by countless companies. And thanks to their drive for innovation, we now have a portfolio to learn from. So, let’s take a look at the most favorable and not so much outcomes.

Successful AI Implementations

For Amazon, Sephora, and Walmart, giants in e-commerce, AI has become an asset.

Amazon Recommendations Make Up 1/3 of Its Profit

Amazon’s AI-based product recommendations have played a major role in its success. It uses a combination of best features of each popular RS to make recommendations to users. This blend allows Amazon to maximize the personalization of its page for each customer. The company strives to further advance its CF by mixing browsing data and purchase history.

As an illustration, when a client purchases tees from the store, upon their next visit, Amazon will not stop at just suggesting more t-shirts. Rather, their algorithm pays attention to browsing history, notices the client enjoys, say, horrors on Prime, and recommends Scream-themed apparel. Now, this RS covers 35% of Amazon’s revenue.

Sephora Deploys Apps That Motivate Engagement with AI

Sephora has leveraged AI-based recommendations to enhance the user experience on its website and mobile app. The company’s algorithm motivates consumers to interact with its AI systems. For example, Visual Artist (an AR extension) offers infinite choices of makeup to try on online, and Color IQ looks for the best hue match. Such innovations have become a form of entertainment that encourages users to stay and explore.

The prolonged usage of these apps presents AI with more data and, thus, it can make ample yet specific recommendations. The results such technologies produce made Sephora invest in related systems. And their collaboration with a firm specializing in ML generated a ROI that was 6 times the amount it had committed to the partnership.

Walmart Uses AI-Based Recommendations to Enhance User Retention

Walmart implemented a fresh recommendation engine where AI helps customers locate the best possible alternative for out-of-stock items. Imagine a shopper looking for strawberry milk at Walmart’s online store. In case the product is unavailable, the algorithm relies on aggregate user data, brand information, price range, inventory, etc., to select a substitute that best fits this particular client (e.g., strawberry-infused coconut milk).

The system also uses a feedback procedure, asking consumers to approve or refuse the offered choice, which allows AI to learn further and improve its recommendation mechanisms. Normally, in situations when a user cannot find the desired item, they will promptly switch to another platform. This AI-powered tool increased Walmart’s retention, and customer acceptance of substitutes grew to over 95%.

Failed AI Implementations

A point to note is that AI is not a magic wand that makes all your problems disappear. It requires care and a little bit of love for optimized functioning. Otherwise, you may encounter some unpleasant surprises.

Amazon’s “Frequently Bought Together” Offers Want Users to Make Bombs

A 2017 investigation by Channel 4 News team in the UK revealed interesting issues with Amazon’s RS. When customers searched for a common chemical that was used in certain food products, the “Frequently Bought Together” recommendation category would suggest other items the user could buy. Collectively, these suggested products could be used to make black powder, a chemical explosive.

The system also seemed to push ball bearings, which, in homemade explosives, can effectively substitute shrapnel. What is more, this situation led to a few successful prosecutions since some individuals purchased materials that could be used to create bomb-like devices.

Zalando Suggests Lingerie for Children

In 2019, a user encountered a post with poorly translated text and an inappropriately offered item. The phrase “comfortable fashion for your kids”, followed by a picture of red strapped suspenders, surely caused some ruckus. Fortunately, the issue was promptly resolved and motivated the company to advance its AI filtering as well as focus on preventing improper offers.

Challenges & Limitations of AI-Based Product Recommendations

From the above, it is evident that there are aspects you need to be prepared for when working with AI. But do not let these stop you from committing to AI-powered systems. If you set them up right, you will have no regrets. Still, you should be well familiar with issues you may encounter.

• Data quality, quantity, and bias. Since AI-based recommendations heavily rely on data, it is critical for the learning base to be ample and of high quality. Poor data may lead to inaccurate or biased recommendations that favor/discriminate against certain products or demographics.
• Privacy and ethical concerns. RSs necessitate access to customer data, which nowadays is a trigger for many. Customers are increasingly hesitant to share their data if they feel that it is being used inappropriately or without their consent.
• Cost and complexity. Implementing AI-based product recommendations is an intricate process, and it is a considerable investment. Additionally, the technology requires ongoing maintenance and updates to keep the system evolving and dynamic.
• Lack of the human touch. Since AI operates with data analysis, it may not be able to provide creative or innovative recommendations that a human salesperson could. This can be particularly problematic for complex or high-value products that require a more personalized approach.
• Limited ability to adapt. AI-based recommendations may not be able to adapt to changing trends or customer preferences as quickly. As a result, some recommendations can be outdated or no longer relevant.
• Over-reliance on past behavior. AI often banks on past customer behavior to predict future offers, which can lead to a lack of diversity in recommendations. The issue of over-relying on previous data can hinder the promotion of new or niche products that have not yet gained popularity.

How to Deal with AI Issues

Among the most practical methods of creating a healthy algorithm is software testing services. Professional QA resources allow to minimize implementation risks and increase expected benefits. Here is what they can do.

• Identify and address data quality issues. To ensure superior data quality, QA engineers verify the accuracy, completeness, and relevance of the information AI is to learn from. Naturally, this is a long-term process, as the study database should be regularly updated.
• Evaluate the performance of the algorithms. QA specialists create test cases that cover different scenarios and data sets to ensure that the recommendations work as intended. I.e., they review whether AI offers truly suitable suggestions.
• Test for bias. To address this, QA experts test for bias in the algorithms by contrasting fair outputs (desired results) with provided outcomes. Later, models are adjusted as needed to remove unhealthy inclinations.
• Optimize system performance. Securing fast recommendation generation means stable load and API testing. QA professionals measure the time it takes for the system to produce offers and compare it with performance requirements.

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Critical Testing Types for AI in E-commerce

In no way is this a complete list, but these are the essentials you will need to establish a reliable AI-based product recommendation system and strengthen your sales.

• Functional and UI testing. In the context of AI, such tests are used to verify that the algorithm does what it is supposed to (offer accurate product recommendations) and that clients can easily identify and interact with them via the user interface.
• Integration testing. This testing ensures that AI models, data processing, and other components work correctly when combined to produce the final product recommendation.
• System testing. System testing checks whether AI-based product recommendations are working correctly within the overall system and how they interact with other systems.
• Performance testing. Performance testing secures the system’s ability to handle a high volume of requests and generate recommendations within the expected response times.
• Security testing. With product recommendations, security testing could include testing for vulnerabilities in the AI models or ensuring that sensitive user data is appropriately protected.
• Data quality testing. Data quality testing verifies that the system has access to the right data sources and that the data is clean, consistent, and relevant.
• A/B testing. For e-commerce AI, A/B testing could be used to compare different recommendation algorithms or models to see which one generates more accurate and relevant recommendations.
• Exploratory testing. For AI systems, exploratory testing could be used to uncover issues that were not caught via other testing types, such as unexpected user behavior or edge cases.

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To Sum Up

Just like any other technology, artificial intelligence is constantly evolving and changing. While its future in product recommendations is exceptionally promising, AI should be examined, tested, updated, and maintained. Only by giving the algorithm due care can you maximize its potential.