Artificial intelligence (AI) is a hot tool these days. Not only is it helping brands and retailers streamline business operations to become more efficient but it’s also playing a crucial role in enabling brands and retailers to distinguish themselves and captivate consumers.
See also: Retailers Pin Hopes on AI to Increase Sales, Decrease Returns
Among the various AI-powered solutions available, such as virtual try-on, customer service chatbots, and predictive analytics for inventory management, one tool that often flies under the radar but serves as the foundation for all others is product attribution. It forms the backbone of these technologies, enabling their effectiveness and seamless integration within retail operations.
Product attribution refers to the process of categorizing and labeling products with relevant attributes such as color, size, brand, material and style. Traditionally, this task has been labor-intensive, requiring manual effort from employees to inspect and tag each product individually. With AI-powered product attribution, however, this process is automated, saving significant time and resources.
One of the key advantages of AI-powered product attribution is its ability to handle large volumes of data quickly and accurately. Retailers often deal with extensive product catalogs, ranging from hundreds to millions of items. By leveraging AI algorithms, these catalogs can be analyzed and attributed in a fraction of the time it would take for manual processing. The efficiency allows retailers to scale their operations, handle larger inventories, and focus on other critical aspects of their business.
The impact of AI-powered product attribution on retail goes beyond operational efficiency. It plays a pivotal role in enhancing customer experiences and driving sales.
Accurate and comprehensive product attributions help customers find what they are looking for more easily. Shoppers can filter and search for products based on specific attributes, making their purchasing journey smoother and more personalized. For instance, a customer searching for a red, sleeveless dress can quickly find the desired product among thousands of options, thanks to accurate attributions.
Moreover, AI-powered product attribution enables retailers to provide personalized recommendations to customers based on their preferences and previous interactions. By analyzing patterns and correlations in customer data, AI algorithms can suggest relevant products that align with individual tastes and needs. This level of personalization enhances customer engagement, increases conversion rates, and fosters long-term loyalty.
In addition to customer-centric benefits, AI-powered product attribution empowers retailers to optimize their inventory management and pricing strategies. Accurate attributions allow businesses to analyze product performance, identify trends, and make data-driven decisions. Retailers can identify underperforming products, understand customer demand patterns, and adjust their assortments accordingly. By optimizing inventory and pricing, retailers can reduce costs and maximize profitability.
It is important to note that AI-powered product attribution is not without its challenges. Training AI algorithms requires high-quality labeled data, which can be time-consuming and costly to collect. Additionally, ensuring fairness and mitigating biases in attributions is crucial to avoid discriminatory outcomes. Retailers must be diligent in their data collection and algorithmic development processes to address these concerns and ensure ethical and inclusive attributions.
To enhance the process and increase conversion potential of its Product Studio offering, Google has introduced a simplified method for setting up product feeds. The new approach automatically extracts information from a merchant’s website and populates their product feed. Merchants will have the option to modify the populated data or disable the automatic feature, giving them full control over the displayed information.
“Our goal in building tools for merchants is to bring Google’s unique expertise to merchants at scale and help them grow their business in a time when shopping is happening anytime, anywhere,” Google VP/GM, Merchant Shopping Matt Madrigal said at a Google Marketing Live event. “Eye catching unique content helps grab shoppers’ attention, but creating that content requires a lot of time and resources our teams have been building tools to help merchants meet shoppers where they are and put their best foot forward for years.”
See: Google Turns Product Images Into Merchant Sales Using Generative AI
While Google’s foray into generative AI to help merchants create more compelling product imagery and relevant product attribution, it only extracts information from the merchants’ website.
Retail technology platform Lily AI looks to include the customer’s take by harnessing AI to help retailers think like their customers and incorporate customer-centric search terms into their product descriptions. The company can also then leverage the customer language to help retailers optimize their site search, product recommendations, filters and facets.
“It starts with understanding the language of the customer, and realizing just how poorly served they are in the online shopping experience today. Products are often put on shelves with legacy, ‘out-of-the-box’ attributes that come directly from manufacturers and distributors, and that don’t capture the nuance and detail that shoppers actually use when they’re looking for relevant products that match their needs,” Purva Gupta, co-founder and CEO of Lily AI, said in 2022.
According to Gupta, each consumer has their own unique search approach, emphasizing the significance for retail eCommerce brands to establish a well-structured product taxonomy that caters to both common and long-tail searches.
James Kim, VP of eCommerce at Bloomingdale’s, emphasized the importance of delivering relevant results to customers through on-site search. Kim stated that the manner in which results are presented to customers significantly impacts their experience and satisfaction with the brand.
Companies such as thredUP have also observed noteworthy results which include a 15% rise in sell-through rates, accompanied by a 2% increase in conversion rates for customers who have made at least two purchases thanks to enhanced product data.
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