So far, ChatGPT is living up to its hype. It’s writing entire articles, producing code, creating recipes and even passing exams, including professional and legal boards. Other popular generative AI (GenAI) interfaces are creating music and art, from digital painting to sculptures and clothing designs. GenAI apps can create, adjust and morph imagery, saving a lot of time in reshooting or editing photographs. While there’s a lot of excited speculation about the future of GenAI, it’s already
dy a powerful tool for industries such as retail. From a frontstage, customer-facing perspective, it has a range of applications: Improved personalisation Personalisation isn’t new – but it’s now on steroids, with incentives, offers, targeted products, cross-selling and upselling and dynamic pricing as mentioned below. With GenAI, marketers can create content that will resonate with a specific individual. Instead of personalising creative and messaging for a segment of consumers, it can be precisely targeted to a single customer based on logic and observed behaviours and needs. Visual search GenAI can revolutionise visual search. If someone sees a picture of a celebrity wearing an outfit they like, or even snaps a picture of someone in the street, they can use visual search to identify and find that specific item or to receive recommendations for similar items. This has powerful applications for retailers. The Louis Vuitton mobile app, for example, enables users to take photos of a Louis Vuitton bag and then shows them how and where to purchase it. Similarly, AI can analyse social media images and fashion magazines to identify emerging trends, helping retailers stay ahead of the curve and stock products that will interest customers. Product demo videos Every aspect of video production can be generated or assisted by AI, from scripting and storyboarding to actual video content generation and voiceover. It’s also easy to make different versions for A/B testing, and to include accessibility elements such as captions and subtitling. GenAI can also be used for virtual try-ons, including analysing user-created videos and giving feedback such as recommending trying an outfit in a different size or colour. Backstage applications Behind the scenes, GenAI is already being used for a host of retail applications. A particularly important one is decreasing time-to-resolution for customer services queries. By empowering call-centre agents with the knowledge of the hive mind and the best ways to answer the most difficult questions, and combining that with a view of who a particular customer is and what their needs are (segment based and individual behavioural), GenAI can more fluently support making scripts and conversation trees. Additionally, by assessing sentiment, mood and tone from a customer on the phone (Are they stressed out, upset, angry, indifferent?) GenAI can adapt to resonate and connect at the appropriate level, given the task or resolution at hand. This not only enhances customer service but also makes work easier and less stressful for front-line customer support staff, resulting in better retention and resolution rates. Another important area is price optimisation. GenAI can adapt pricing strategies and offers to customers by analysing market data, competitive pricing and consumer behaviour. By adjusting prices in real time based on this analysis, retailers can increase revenue, reduce markdowns, and remain competitive in a constantly changing market. Airlines have been doing this for years. The question is how customers will respond when they become aware that prices shown may change from day to day, depending on their location or the volume of other buyers. Untangling supply chains Supply chain and logistics became a major headache during the pandemic, and while congestion has eased, many long-standing issues remain. GenAI solutions, such as Inspectorio, can play an important role in untangling the complexity of transport and routing, by generating efficient transport plans with optimised routes, minimised expenses and timely deliveries. It can also provide dynamic routing with adaptations to disruptions and delays and general risk management. GenAI is also transforming inventory management. One of the biggest challenges in this area is accurately predicting demand. GenAI can generate demand forecasts by analysing complex patterns in historical sales data, market trends, promotions, and customer ratings and reviews. This helps retailers optimise inventory and reduce under- and over-stocking. The technology can also generate negotiation strategies or scripts based on past negotiation data and current market conditions, helping retailers get better terms from their suppliers. Challenges There are several concerns and limitations to GenAI. A major one in our industry is that we are not always able to verify the source of content, leading to potential plagiarism and legal risk and, therefore, a level of discomfort or lack of trust. Another issue is the inherent bias and echo chamber that can be created. This could lead to unfair or discriminatory outcomes, such as certain groups of customers being excluded or unfairly targeted. Data privacy is another challenge. Retailers have access to vast amounts of customer data that can be used to train GenAI models. But it must be handled responsibly to comply with security, data protection and customer privacy laws. Retailers must take a human-centric approach when designing new customer experiences. By starting now in a small way, retailers can safely learn how GenAI works before extending its application across higher-stakes business functions and features.