There are more than 1.9 million small and medium-sized companies on Amazon’s e-commerce website to sell their products. These sellers make up to a whopping 60% of the retail sales [source: Amazon]. And it doesn’t stop there; a consumer survey showed that 89% of the participants agree they are more likely to buy a product from Amazon than another e-commerce store.
So, you have seen from above that Amazon plays a significant role for both seller and buyer. It bridges the gap between the two seamlessly. And that is not it; Amazon keeps improving its search results to give users the most relevant data as efficiently as possible. One of the ways that Amazon uses AI to boost product sales is by providing reviews and comment options to the user.
Recently, this review system has been refined using AI-driven review. However, like with every AI system, this is also unfortunately not perfect and has specific flaws that must be taken into account.
Here, AI processes all the data provided by the customer in the form of feedback and reviews analyzes them and shows you a review, which can be called a summarized version of all reviews. That’s great, right? You don’t have to go through tens or more reviews anymore to know more about people’s options for the product.
Since all the analysis is being done in a rational way without any bias (hopefully), the data presented as the final result is pretty efficient. They are kind of a summary of all the reviews that have been given by the customers to date.
Since AI works on pre-designed algorithms, they will analyze all the comments and reviews with the same level of precision & regularity, and fairness. Hence, all AI-generated reviews are going to be impartial & unbiased.
This feature is extremely critical as any irregularity in review will alarm customers about the uncertainty around the product.
Simply put, AI-driven customer review is an outline and overview of the product & service’s performance. This is especially beneficial for less popular products with minimal reviews as they get more chances of exposure with quick summaries & effective overall ratings.
AI-driven reviews are short and bite-size that customers can quickly go through to know about any product’s practical performance. This may include customer concerns, what they like and dislike about a product, issues raised about packaging, etc.
With a short and informative review, a customer will be able to make a quick decision about whether they need the product or should move on to the next recommendation.
An AI-driven review will not include any descriptive information about any product bought. They will probably do not include in-depth experience about a product.
One problem with AI tools is that they often sound cold, have short-length sentences, and appear as if someone has chopped off some words. On top of it, there is language limitation that comes with AI-driven review.
So, even though a lot of work is being done in this field, a lot more is needed to be done. So, stay tuned; there may be some breakthroughs in the software part coming in the near future.
Of course, emotions are a huge part of human psychology. This is one of the areas in which AI’s hands are tight. They can’t reflect upon human emotions. An AI-driven review may not be able to capture the underlying emotion about personal attachment towards a brand or a product. It is often referred to as brand loyalty or brand attachment.
A logically designed algorithm-based AI may have trouble understanding human sentiments. This may not be reflected in the review. On the positive side, you will get the most rational review, but on the flip side, it will miss the personalization part. So, what kind of review do you prefer – rational or emotional? The choice here, too, will vary from person to person.
AI may not understand the user’s personal context of writing a review. Hence, it may interpret it in a different way from the user’s point of view. For example, a user might give a poor star rating to a product because they received a damaged product or lousy packaging. But that does not reflect anything about the quality of the product itself.
Another way AI can misinterpret a user’s message is by comparing. Again, if we take an example, a user may have compared a $10 product with a $110 product to convey that they do not like it. Now, these examples may result in poor ratings, but they do not really tell anything about the product itself.
AI can’t tell (for now) whether the product review is given by a genuine customer who has bought and used the product or by a competitor who just wants to out-sell their own product. However, this malicious practice is not illegal but is highly unethical.
They are people who are paid to write positive reviews about a brand to upsell their product or to write a fake negative review that damages the rating and the brand’s image or its product’s quality. The good news is that there is a constant strive towards detecting these frauds by the Amazon team and all other e-commerce platforms.
So, Should You Buy A Product Based On AI-Driven Customer Review Summary?
For now, even though the current AI-driven review systems may have some ups and some downs, in upcoming years & further incorporation of AI and ML (machine learning), we may see more genuine and precise reviews.
Even Amazon acknowledges that there are some fake reviews that may affect AI-driven reviews in a negative way. But they are working and constantly improving on it. So, let’s hope that the same AI will help spot manipulative reviews and intentional negative comments in the near future. From a consumer point of view, it is crucial to read not just AI-driven reviews but also some other reviews to get a general idea of the product and its functionality.