Latest / Retail Media Breakfast Club / AI in real life: how retailers and brands are leveraging AI (real numbers)
Transcript
- 4 AI use cases === [00:00:00] The biggest barrier to AI adoption isn't the technology, it's imagination. Many retailers and brands struggle, not with what AI could do, but with envisioning what it should do for their specific challenges. That's why today I'm sharing four real world practical applications of AI from retailers and brands, and most importantly, the outcome. [00:00:25] How much should it save them? How much should it make them? And if you like this content format, please let me know and I'll consider turning it into a regular series on the podcast. But today in this inaugural edition, I have four great case studies for you, let's go. [00:00:45] Good morning and welcome to the Retail Media Breakfast Club. I'm your host, Kerry Masters, and every weekday at 6:00 AM Eastern, I bring you candid and real discussions in the world of retail, media and marketplaces in just 10 [00:01:00] minutes. So bring your own cup of coffee and we'll get smarter about retail media together. [00:01:05] So case study number one, how product review analysis improved conversion rate by 15 to 74%. Elizabeth Rivas, e-commerce marketplace director at the supplement brand, algi Cal told me just today how she's using AI for customer product review analysis She uses AI to process and analyze thousands of product reviews, including their own and competitors, specifically on Amazon. [00:01:37] This helps her to identify patterns like what customers love, frustrations, and market gaps. How they were able to get there was updating product imagery, for example, including adding instructions for certain products due to customer confusion. [00:01:56] She's also able to create targeted [00:02:00] conquest campaigns against competitor ASINs, highlighting their specific product weaknesses, and finally incorporate exact customer language into marketing copy for relevance and resonance. [00:02:14] So the result here of 15 to 74% conversion rate improvement across their products on Amazon, That is such a win. Well done to Elizabeth and the team at Alga Cal. [00:02:27] [00:02:29] Case study number two, Walmart shrinks designed to production cycle by 70%. So Walmart just announced this publicly, um, which was really cool. It's called Trend to Product, and it's a new AI enabled tech solution that cuts the time that designers would spend researching and conceptualizing new products for their fashion ranges. [00:02:55] So here's how. Trend to product works. [00:03:00] The AI pulls trend data from runway, social media, and also previous sales performance. The AI builds mood boards complete with collection names, colors, textures and ideas. And then the humans get involved. The designers and merchants refine the mood boards using sell through data and their own experience. [00:03:25] And then finally, back to the ai, which generates. Fashion tech packs, which instruct suppliers on exactly how to make each item and the result here, this is amazing, honestly, cutting production time from six months down to six to eight weeks, so that is at least a 70%. [00:03:49] Time improvement and of course untold hours from designers to actually get all of that going. [00:03:57] [00:03:57] [00:03:59] [00:04:00] Case study number three, drafting legal contracts, saving $92,000. All right, this is a bit of a cheat. It's not from a retailer Rob brand, but it was just too good to not share it with you. Um, there's a podcast I listened to called Never Enough. It's by the entrepreneur, Andrew Wilkinson. And it was sort of just as an aside that he shared this story of how he used. [00:04:25] Claude to draft a legal agreement to merge three companies together, which he said would ordinarily have cost 80 to a hundred thousand dollars in legal fees. But what he did was got Claude to draft it and then asked a lawyer to review it and said, another firm has prepared this agreement, and I just want you to point out any obvious errors and issues to be addressed. [00:04:49] And the lawyer billed him $2,000 for the review. He said, this draft is really good. And kept asking which firm prepared it. And Andrew [00:05:00] said he was really unhappy when we finally told him it was Claude. So, um, look, this is obviously not legal advice, but a huge cost savings here and, [00:05:12] obviously something that needs, absolutely needs to be vetted by a professional. But just imagine the time and cost savings for getting a draft initiated before a professional comes in. [00:05:24] [00:05:27] And final case study. Case study number four, j and j Trims AI initiatives from 900 down to the vital few. [00:05:37] So j and j Johnson and Johnson took a really expansive approach to AI experimentation, encouraging all their teams to explore many, many generative AI use cases, and about an a year of broad experimentation here. They discovered they had 900 generative AI experiments [00:06:00] on the, on the boil at any given time, but only 10 to 15%. [00:06:05] Of these use cases, uh, we're actually delivering 80% of the value across the board. Classic Pato principle at play here. So then j and j used that information to shift their strategy, moving away from broad experimentation towards more of a prioritized approach, allocating resources only to the highest value generative AI use cases. [00:06:32] And then cutting all those projects that were redundant not working, or where non-AI technology works better. Which, yes, it's true. Some things work better without AI today. So the result there, going from a lot of probably duplicative AI experiments or ones where there's not a whole a, a great deal of ROI [00:06:57] down quite a bit. , I presume they cut [00:07:00] those 85 to 90% of projects that weren't adding value and likely saved a bunch of time and resources and subscriptions. Along the way. So those are the four case studies. But before you go, I have one more bonus for you. [00:07:15] My friend and e-commerce industry consultant, Danny Silverman, created a AI job resilience self-assessment, and it helps you to evaluate how resilient your role is. To AI automation, it helps to identify where your work is most human, most strategic and most vulnerable to automation so you can plan accordingly. [00:07:39] , I just took it, it really made me think of some areas where I should be leveraging automation a bit more and leaning into those areas that are distinctly human that are really not going to away, so going away. So things like. Relationship management and in-person connections is a huge [00:08:00] one. [00:08:00] So it really did get me thinking. So I'll link up to that self-assessment in the show notes. [00:08:07] That's it for today. I'd love to hear what you think of this episode. If you have AI success stories of your own, I'd love to share some more real results on this podcast. Thanks for tuning in and I'll catch you tomorrow.