By Kristin Naragon, Chief Strategy Officer, Akeneo
Consumers, faced with an almost overwhelming number of choices at their fingertips, are increasingly turning to AI-powered systems to find the products that best meet their needs. However, for brands and retailers, this can make understanding exactly what their customers are looking for even harder. Because of this, many organisations are turning to AI to more accurately read and interpret customer signals (search terms, reviews and feedback) to create authentic product experiences that are aligned with individual preferences and help to build brand loyalty and trust.
Authentic product experiences
Customer feedback is an invaluable asset for any brand. Not only does it reveal how consumers are actually using and perceiving products, but it can also reveal the intent behind a consumer’s search, enabling businesses to better align their product experience offering and ensure they feel more personal and relevant.
This year at NRF in New York, I was joined onstage by multi-tool brand Leatherman’s Software Development Manager, Carl Ogden, and Piet Walvoord, Head of Product Development at 1-800-Flowers. Carl shared an insightful story about one of Leatherman’s products – the Raptor® Rescue. Initially, this tool was targeted at emergency responders, but customer feedback soon revealed that it was a hit with IT professionals for cutting zip ties and random cables. By incorporating customer reviews directly into their Product Information Management (PIM) system, Leatherman was able to adjust its marketing and product development strategies to better meet the needs of this newly identified customer group.
Piet Walvoord of 1-800-Flowers, which also relies heavily on customer feedback to inform its product discovery process, explained how analysing customer intent through search and site interactions helps the company refine product descriptions and ensure they align with what customers are looking for, whether it’s occasion-based, recipient-based, or another specific need. This continuous feedback loop allows brands to adapt their offerings to meet customer expectations dynamically.
Aligning content with customer behaviour
AI has changed the game when it comes to personalising product discovery. By leveraging AI to analyse customer behaviour, including search patterns, product interactions and reviews, businesses can gain actionable insights that help refine product content, not only improving the product discovery experience but also ensuring that brands always stay relevant.
Both Leatherman and 1-800-Flowers are already leveraging AI to align their product content with customer behaviour. For example, Leatherman uses customer search data to inform SEO keywords and metadata in their PIM system, ensuring that their products are discoverable based on how customers are searching for them. Whether customers are looking for tools related to specific activities like camping or everyday carry, Leatherman’s AI-powered systems are able to match the right product with the right customer.
Data-driven strategies to reduce returns
In an environment where returns can significantly impact profitability, reducing them is a top priority for brands. According to the NRF in a recent report, total returns for the retail industry are projected to reach $890billion in 2024. To address this growing problem, AI, combined with customer feedback, can help businesses make more informed decisions that lead to higher conversion and lower return rates.
As Carl shared during our discussion at NRF, Leatherman approaches this by aligning product descriptions and metadata with customer feedback, enabling better demand prediction and ensuring that customers are matched with the most relevant products. Identifying new market opportunities, such as the IT community, in addition to its traditional emergency response audiences, helps the brand target to not only expand its product-market-fit while also ensuring its marketing efforts are effective at the original target market, ultimately reducing returns thanks to setting high confidence expectations.
1-800-Flowers also benefits from a data-driven approach. By building an intent engine that analyses a customer’s entire journey through their site, 1-800 Flowers has a stronger ability to predict which products are most likely to resonate with certain customers, ensuring that the right products are surfacing at the right time for the right needs and increasing the likelihood of a successful, confident purchase. Additionally, by analysing product performance in the context of customer intent, such as the occasion or specific use case of the product, the brand can fine-tune its offerings to ensure a more personalised experience for the customer.
AI-Powered product discovery
As AI technology continues to evolve, the way customers discover products will also change. Tackling the problem by looking at customer data is risky and slow. The only way that a company can be ready for natural language and AI powered search engines is to have a rich, complete, contextualised single source of truth for product information. If a business doesn’t have that as a foundation, successfully innovating with AI will prove to be elusive.
Leatherman and 1-800-Flowers have those foundational product information elements which is empowering them to innovate with AI to serve these new discovery paths. For Leatherman, this includes working on an AI-powered chatbot to help customers navigate their product catalogue and provide gifting recommendations. By training the chatbot with customer reviews and other relevant product content, Leatherman is able to provide a more personalised experience for visitors that better accounts for the intent of their search. Meanwhile, 1-800-Flowers is exploring how to use AI to enhance product recommendations based on a customer’s interactions with the site, including dynamic adjustments to product recommendations based on the customer’s past behaviour and the context in which they are browsing.
The key to success in this new AI-powered product discovery environment is having a solid product data foundation. AI’s ability to generate meaningful insights and predictions is directly tied to the quality of the data it is processing; if product data is inaccurate or incomplete or does not include the voice of the customer in the description, AI systems will generate flawed recommendations, which can ultimately harm the customer experience.
The importance of listening
AI is not a magic wand; the future of personalised product experiences depends not on adopting AI technology, but in creating the foundation of a strong, accurate and flexible product data structure that can support and enable any powerful AI technology to come.
At the core of a successful strategy is the need to listen to customers and continuously adapt the core information they need in order to make a purchase – contextualised product information. Whether it’s through customer reviews, search terms or site interactions, businesses must ensure they are collecting and acting on customer signals to create more authentic, personalised experiences, which not only improve the customer experience but also lead to increased sales and fewer returns as consumers make more confident purchase decisions.
Looking to the future of product discovery, it’s clear that the brands that prioritise listening to their customers and adapting their product descriptions accordingly will be the ones that thrive. Your customers are talking, are you listening?
