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<\/p>\n\u201cHello Mr Yakomoto, welcome back to The Gap. How did those assorted tank tops work out for you?\u201d<\/p>\n
Ever since Tom Cruise walked into The Gap as pre-crime chief John Anderton and was greeted by a 3D hologram asking him about his purchase from an eye scan, the “Minority Report” became an instant “future of retail” classic. It\u2019s personalized shopping Philip K. Dick style, set in 2054. While the movie is more than 20 years old now, the supercharged rise of generative artificial intelligence means we\u2019re on the cusp of what it portends right now.<\/p>\n
Clearly there\u2019s a way to go before in-store retina scanners are the norm, but the way generative AI can gather, store and learn from consumer information across all platforms means retailers can already amass a lot of the data they need to create highly bespoke customer experiences \u2014 at scale.<\/p>\n
AI\u2019s ubiquity today means the opportunity to streamline the consideration phase and drive increased revenue is very real. So how do retailers and brands harness that power? Where is it best used? There are three distinct ways that generative AI can help retailers get closer to the “Minority Report\u2019s” super-personalized experience.<\/p>\n
AI as an Additional Employee<\/strong><\/h2>\nTo set the scene, unlocking its potential lies partly in seeing generative AI as the additional employee on your team. It goes without saying that few employees should be unsupervised in some way, and generative AI is no different.<\/p>\n
Its job description should be set out too: the AI employee\u2019s role, in this context, is to close the gap between having the data and understanding it. It’s to turbocharge the data most businesses already hold to enable them to get to know their customer better. It’s to help leverage data to drive actionable insight.<\/p>\n
Supercharging CRM<\/strong><\/h2>\nSeen like this, generative AI can help with the first, and perhaps most powerful use: supercharging CRM and marketing systems to gather consumer preference and behavior information. Personalization continues to be one of the holy grails of retail and generative AI can create fine-tuned experiences based on real-time data, providing proactive, customized content, rewards and recommendations to drive revenue.<\/p>\n
Every purchase we make represents us in some form \u2014 you just have to figure out what that key insight is. That\u2019s where AI can change the paradigm dramatically. It can potentially go beyond a person\u2019s hardware (e.g., their age, gender, ethnicity) and understand their software (e.g., interests, experiences, purchase drivers, what makes them tick). If someone signs up for a loyalty program then AI can scan that consumer\u2019s social media feeds and get to know their lives \u2014 what they eat, where they go on vacation, the clothes they like.<\/p>\n
There’s a generational gap here that\u2019s worth noting. Generation X and millennials are more cautious when it comes to data sharing \u2014 happy to give up some but not all. Gen Z thinks differently. In general, they don\u2019t have the same hang-ups. After all, their lives are online already. There\u2019s not a lot we don\u2019t know. They are savvy though. If generative AI creates something that doesn’t quite ring true, they cotton on quickly.<\/p>\n
Over time, those kinks will be ironed out and AI will make customer journeys smoother. Loyalty ecosystems are a great place to start an AI-enabled personalization push in a way that embeds it into a retailer ecosystem. Built correctly, there should also be opportunity to grow.<\/p>\n
Refining What\u2019s Currently ‘Search on Steroids’<\/strong><\/h2>\nNext comes the refinement of what currently feels like search on steroids.<\/p>\n
Want to buy a blue suit? Then ask AI and it will come up with everything<\/em> it\u2019s gleaned from the web. The myriad sites might give the consumer more autonomy but from a retail perspective it\u2019s not ideal. You want them to come to you. And you want their loyalty.<\/p>\nSo the next stage of generative AI is one of refinement; used within data walled gardens, generative AI should be able to provide granular details based on customers\u2019 search criteria, enabling faster, better customer journeys.<\/p>\n
We\u2019ve all seen loyalty programs that take a customer\u2019s purchase history and provide them with special offers that may \u2014 or equally likely may not \u2014 be relevant because it’s built on past behavior. The generative AI employee can take that data and refine it, make it more about the individual and talk to them directly. It\u2019s about getting to a point where the customer feels like they\u2019re having a conversation with a friend who knows them better than anyone.<\/p>\n
In this context, a half-price offer on a product based on a customer\u2019s purchase history can become something even more powerful. An email or text suggesting that it might be time to grab a coffee or pick up that burger you usually eat around a certain time. It knows the consumer\u2019s habits and knows when they make their purchase. The relationship becomes that bit deeper as generative AI sifts through the data to help learn about customers\u2019 search queries and patterns more specifically. The payoff is saving your customer valuable time and easing their purchase journey online.<\/p>\n
Operational Advantage Through Innovation<\/strong><\/h2>\nFinally, there\u2019s the time-saving aspect. Generative AI\u2019s prowess at efficient operational execution has been well-documented across every sector. In fact, that\u2019s where most organizations currently use it.<\/p>\n
Retailers are starting to innovate even in an operational setting. Generative AI is being used to create enticing and accurate product descriptions in seconds, shaving significant amounts of time that it would a human to process information on material, composition, etc., as well as draft enticing enough copy that would lead to purchase. While you\u2019ll still need the human eye for accuracy, this technology effectively streamlines a process that’s super labor intensive.<\/p>\n
It isn\u2019t just big retailers that can benefit from this application; generative AI can be a boon for smaller businesses. If you only have 25 SKUs and want to get them onto Amazon.com, for example, AI\u2019s capabilities can get them to market quicker. Half the battle is getting to the marketplace and, now, here\u2019s an effective shortcut.<\/p>\n
So while the technology exists \u2014 and did exist when Philip K. Dick wrote “Minority Report” way back then \u2014 we as customers aren’t yet ready for in-store retinal scanners and AI shop assistants; what we as retail specialists are ready for is to use generative AI to make more of the data we already have to become more deeply connected with customers \u2026 something our generative AI employee can help us to do.<\/p>\n
Kevin O\u2019Connor is the director of growth at Designit, a global experience innovation company.<\/em><\/p>\n<\/div>\n
Seen like this, generative AI can help with the first, and perhaps most powerful use: supercharging CRM and marketing systems to gather consumer preference and behavior information. Personalization continues to be one of the holy grails of retail and generative AI can create fine-tuned experiences based on real-time data, providing proactive, customized content, rewards and recommendations to drive revenue.<\/p>\n
Every purchase we make represents us in some form \u2014 you just have to figure out what that key insight is. That\u2019s where AI can change the paradigm dramatically. It can potentially go beyond a person\u2019s hardware (e.g., their age, gender, ethnicity) and understand their software (e.g., interests, experiences, purchase drivers, what makes them tick). If someone signs up for a loyalty program then AI can scan that consumer\u2019s social media feeds and get to know their lives \u2014 what they eat, where they go on vacation, the clothes they like.<\/p>\n
There’s a generational gap here that\u2019s worth noting. Generation X and millennials are more cautious when it comes to data sharing \u2014 happy to give up some but not all. Gen Z thinks differently. In general, they don\u2019t have the same hang-ups. After all, their lives are online already. There\u2019s not a lot we don\u2019t know. They are savvy though. If generative AI creates something that doesn’t quite ring true, they cotton on quickly.<\/p>\n
Over time, those kinks will be ironed out and AI will make customer journeys smoother. Loyalty ecosystems are a great place to start an AI-enabled personalization push in a way that embeds it into a retailer ecosystem. Built correctly, there should also be opportunity to grow.<\/p>\n
Refining What\u2019s Currently ‘Search on Steroids’<\/strong><\/h2>\nNext comes the refinement of what currently feels like search on steroids.<\/p>\n
Want to buy a blue suit? Then ask AI and it will come up with everything<\/em> it\u2019s gleaned from the web. The myriad sites might give the consumer more autonomy but from a retail perspective it\u2019s not ideal. You want them to come to you. And you want their loyalty.<\/p>\nSo the next stage of generative AI is one of refinement; used within data walled gardens, generative AI should be able to provide granular details based on customers\u2019 search criteria, enabling faster, better customer journeys.<\/p>\n
We\u2019ve all seen loyalty programs that take a customer\u2019s purchase history and provide them with special offers that may \u2014 or equally likely may not \u2014 be relevant because it’s built on past behavior. The generative AI employee can take that data and refine it, make it more about the individual and talk to them directly. It\u2019s about getting to a point where the customer feels like they\u2019re having a conversation with a friend who knows them better than anyone.<\/p>\n
In this context, a half-price offer on a product based on a customer\u2019s purchase history can become something even more powerful. An email or text suggesting that it might be time to grab a coffee or pick up that burger you usually eat around a certain time. It knows the consumer\u2019s habits and knows when they make their purchase. The relationship becomes that bit deeper as generative AI sifts through the data to help learn about customers\u2019 search queries and patterns more specifically. The payoff is saving your customer valuable time and easing their purchase journey online.<\/p>\n
Operational Advantage Through Innovation<\/strong><\/h2>\nFinally, there\u2019s the time-saving aspect. Generative AI\u2019s prowess at efficient operational execution has been well-documented across every sector. In fact, that\u2019s where most organizations currently use it.<\/p>\n
Retailers are starting to innovate even in an operational setting. Generative AI is being used to create enticing and accurate product descriptions in seconds, shaving significant amounts of time that it would a human to process information on material, composition, etc., as well as draft enticing enough copy that would lead to purchase. While you\u2019ll still need the human eye for accuracy, this technology effectively streamlines a process that’s super labor intensive.<\/p>\n
It isn\u2019t just big retailers that can benefit from this application; generative AI can be a boon for smaller businesses. If you only have 25 SKUs and want to get them onto Amazon.com, for example, AI\u2019s capabilities can get them to market quicker. Half the battle is getting to the marketplace and, now, here\u2019s an effective shortcut.<\/p>\n
So while the technology exists \u2014 and did exist when Philip K. Dick wrote “Minority Report” way back then \u2014 we as customers aren’t yet ready for in-store retinal scanners and AI shop assistants; what we as retail specialists are ready for is to use generative AI to make more of the data we already have to become more deeply connected with customers \u2026 something our generative AI employee can help us to do.<\/p>\n
Kevin O\u2019Connor is the director of growth at Designit, a global experience innovation company.<\/em><\/p>\n<\/div>\n
So the next stage of generative AI is one of refinement; used within data walled gardens, generative AI should be able to provide granular details based on customers\u2019 search criteria, enabling faster, better customer journeys.<\/p>\n
We\u2019ve all seen loyalty programs that take a customer\u2019s purchase history and provide them with special offers that may \u2014 or equally likely may not \u2014 be relevant because it’s built on past behavior. The generative AI employee can take that data and refine it, make it more about the individual and talk to them directly. It\u2019s about getting to a point where the customer feels like they\u2019re having a conversation with a friend who knows them better than anyone.<\/p>\n
In this context, a half-price offer on a product based on a customer\u2019s purchase history can become something even more powerful. An email or text suggesting that it might be time to grab a coffee or pick up that burger you usually eat around a certain time. It knows the consumer\u2019s habits and knows when they make their purchase. The relationship becomes that bit deeper as generative AI sifts through the data to help learn about customers\u2019 search queries and patterns more specifically. The payoff is saving your customer valuable time and easing their purchase journey online.<\/p>\n