Currently, the e-commerce sector is like a flower garden. We all adore roses for their richness. But the truth is, a multitude of petals is sometimes a disadvantage. Ornamental plants produce less nectar due to spending more energy on appearances. And more petals actually mean more ‘distractions’ for pollinators to go through to get to their target.
With the intensifying progression of the industry, businesses see constantly expanding growth opportunities. SaaS, AR/VR, ERPs, headless CMSs, virtual shopping, etc., will make your head spin and question whether these options are worth their while. So, for e-commerce platforms, the goal is to find stable footing and build upon it, making sure that each decision is justified and valuable.
Now, at the core of innovation is artificial intelligence. It has found its place in every new tech and trend. This means that establishing a sturdy AI technology is a certain, solid way to upgrade your business. Ergo, it is essential to know what it can do for your venture and ensure that your AI algorithm is a triumphant investment.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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 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.
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.
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.
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.
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.
For Amazon, Sephora, and Walmart, giants in e-commerce, AI has become an asset.
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 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 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%.
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.
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.
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.
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.
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.
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.
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.
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