How AI is Transforming the Retail Industry

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Until recently, demand forecasting and inventory optimization were among some of the key AI applications leveraging AI. However, the recent developments in AI have led to an array of innovative AI offerings that have revolutionized the retail business. 

Think Outside the Box!!!

 
One unique use case involves optimizing the quality of product images to improve sales conversion rates. In this scenario, retailers leverage AI, specifically computer vision techniques, to enhance the visual appeal of product images, increasing the likelihood of the buyer clicking on the product. 

As part of the sales funnel structure, the increase in clicks, in general, flows down to the higher conversion rates, i.e. sales. Extending it further, the retailers can conduct hypothesis tests and experiment with different image qualities, determining which image attributes, such as high-definition pixels and appropriate lighting, contribute to driving conversions. 

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Computer vision techniques enable retailers to automate the process of implementing fixes to improve image quality, ensuring they resonate with customers. 

Product Descriptions

 
Most e-commerce websites host products from different sellers, resulting in heterogeneous ways of listing product descriptions, which are often inconsistent and incomplete. 

The lack of awareness and insights on what constitutes a compelling shopping experience leads to inconsistency in style, length, tone, and completeness, potentially impacting users’ purchasing decisions. 

However, with Generative AI – the AI technique to generate content, such as text, images, videos, etc. based on large datasets, the companies can generate effective product descriptions which can lead to increased clickability, and in turn, a high conversion rate.

Such AI-generated content ensures that not only information is comprehensive but also engaging and persuasive, tapping into the attention of users. Extending this a step further, the algorithms can even learn user preferences and provide product descriptions that resonate most with them. It is worthwhile to note that the retailers can continue enhancing and building on such AI systems, based on the responsiveness of model outcomes with the users. 

This iterative process enables companies to refine their product descriptions over time, optimizing them for maximum effectiveness in driving conversions.

Seller Risk Management

 
Talking about sellers, the e-commerce platforms can also build an AI-powered seller risk-management system to monitor risks related to product quality, customer service, and adherence to ethical standards. 

AI can learn from factors like past seller behavior, customer feedback, and transaction records, to detect irregularities or deviations from expected norms. Such deviations flag the sellers that might exhibit non-compliance with the platform’s policies and the code of conduct.

Analyze factors such as timely shipping, accurate product descriptions, fair pricing, and responsiveness to customer inquiries to highlight seller behavior. The ones who consistently receive negative reviews or complaints, engage in fraudulent activities, or violate terms of service agreements can lead to bad customer experience as well as tarnish the platform’s reputation. 

Hence, e-commerce platforms can proactively identify such sellers and suspend their engagement by leveraging AI. In addition to ensuring the quality of reliable sellers, such AI systems also foster trust and build confidence among customers. 

While sellers’ behavior may change over time, the AI-powered risk management system can continuously learn and adapt based on the evolving patterns of non-compliance. 

Fraud Detection

 
Often, fraud detection is assumed to be the bank’s responsibility and that’s true to a certain extent. But think of a customer who became a victim of a fraudulent transaction on the retail platform and tries to connect with the retailer to reverse the sale.

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Typically, the retailer is unable to help and is assumed of no fault. However, for the customer, the retailer is the first layer of trust aka defense. Imagine, if the retailer would have an AI-powered algorithm that could identify the potential fraud based on the buyer’s purchase history and could introduce an additional identification step to proceed to the sale, then the fraud is stopped at the very layer of defense already. 

We are living in a highly competitive world where it is crucial to have a differentiator. Managing and mitigating risks of fraud highlights the retailer’s commitment to customer-centricity, leading to increased trust and brand loyalty from customers.

Quality Control

 
Imagine AI being your quality control assistant, checking every product before it reaches the shelves, especially in the case of perishable products, where it is crucial to maintain freshness and ensure consumer safety. 

Similarly, computer vision can analyze the quality of clothes by detecting imperfections in stitching, fabric consistency, and print alignment. By automating quality control procedures, retailers can maintain consistent product standards, delivering superior products to their customers.

Cognitive Overload

 
Different brands have different size guides and it is often a pain among customers to remember the size specific to a brand. AI is known for taking away the cognitive load from the customer and can help make relevant recommendations, enhancing their shopping experience. For example, if the algorithm suggests the size based on purchase history, user characteristics, and possibly the feedback on sizing preferences. There you go – full points on customer delight. 

Summary

 
From optimizing product images and generating compelling product descriptions to managing seller risks and detecting fraud, AI has the potential to revolutionize every aspect of the retail industry. With the open sourcing of more AI products and pre-trained models, the era of free AI for every industry is here to stay. 
 
 

Vidhi Chugh is an AI strategist and a digital transformation leader working at the intersection of product, sciences, and engineering to build scalable machine learning systems. She is an award-winning innovation leader, an author, and an international speaker. She is on a mission to democratize machine learning and break the jargon for everyone to be a part of this transformation.