Fast Five Shorts | Buy Or Sell: Levi’s Claim That Data Helped It Capitalize On The Baggy Jeans Trend

Summary

Omni Talk Retail Episode Summary

Title: Fast Five Shorts | Buy Or Sell: Levi’s Claim That Data Helped It Capitalize On The Baggy Jeans Trend
Release Date: January 30, 2025
Hosts: Chris Walton and Anne Mezzenga

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1. Introduction to Levi's Data-Driven Strategy

The episode delves into Levi's strategic use of data analytics to leverage the baggy jeans trend. Chris Walton (Speaker A) introduces the topic by referencing a Wall Street Journal report:

[00:00] Chris: "Levi's reportedly crunched the data to jump on the baggy jeans trend... running daily machine learning algorithms designed to identify and predict purchase trends."

Levi's partnership with Google Cloud in 2020 is highlighted, where the company aggregated data from various sources—including purchases, web browsing, retail partner sales, and loyalty programs—into a centralized database. This data-driven approach was intended to inform and predict emerging fashion trends, allowing Levi's to respond proactively.

2. Skepticism Towards Levi's Claims

Anne Mezzenga (Speaker B) expresses initial skepticism about the complexity and efficacy of Levi's data initiatives:

[00:26] Anne: "A lot of buzzwords were in that last sentence."

Chris continues by detailing Levi’s global data integration from over 110 countries and 50,000 distribution points, though only 1,100 are Levi's own stores. This extensive data system purportedly revealed that baggy and loose silhouettes appealed to a wide demographic, not just younger consumers active on platforms like TikTok.

However, Anne challenges the effectiveness of such data-driven decisions:

[01:17] Anne: "My God, there's so many merchandising questions in this. I feel like I'm just getting slaughtered and they're going right to me."

She acknowledges the importance of data investment but questions whether Levi's execution truly capitalizes on these insights. Anne draws parallels to other retail giants like Walmart, emphasizing the necessity of combining data with the "art of Merchandising."

3. The Role of AI and Data in Modern Merchandising

Anne underscores the critical role of AI and data aggregation tools in transforming raw data into actionable insights for buying and merchandising teams:

[02:00] Anne: "You also have to be investing in AI tools to aggregate this data to really bring that to something that the buying and merchandising teams... can utilize and help inform some of the decisions they're making."

She compares Levi's strategy to fast fashion competitors such as Shein and Timu, who utilize aggregated platform data to predict trends and adjust their product lines accordingly. Anne suggests that while Levi's approach is promising, it remains in the early stages and may not yet be a robust example of successful data application in retail.

4. Critical Examination of Levi's Success Attribution

Chris takes a more critical stance, disputing Levi's attribution of their success to data analytics:

[03:15] Chris: "I'm selling this hard... this story to me is an example of claiming text impact for something that lines up after the fact."

He argues that the rise in baggy jeans was an obvious trend that any savvy merchant could anticipate without heavy reliance on data analytics. Chris suggests that Levi's may be overstating the role of their data partnership, implying that the trend alignment was more serendipitous than strategically data-driven.

Anne clarifies and expands on this viewpoint, emphasizing that while data is foundational, the human element in merchandising remains indispensable:

[05:00] Anne: "I think we're on the same page... Data. 100% data is the foundation of good retailing going forward."

5. Conclusions and Takeaways

The discussion concludes with both hosts agreeing on the paramount importance of data in modern retailing, while maintaining a critical perspective on how effectively companies like Levi's leverage this data:

• Data as a Foundation: Both Chris and Anne acknowledge that data analytics and AI tools are essential for informed decision-making in retail.

• Human Expertise Remains Crucial: Despite the advancements in data technology, the instinct and expertise of seasoned merchandisers play a vital role in trend forecasting and product selection.

• Skepticism Towards Overclaiming: The hosts caution against companies overclaiming the impact of data on their success, urging a balanced view that recognizes both technology and human insight.

[05:47] Anne: "So to clarify, then, I think so you're saying you're selling the, the claim that Levi's is making. You're not selling the idea that companies should be investing in this type of technology...?"

[05:55] Chris: "Data. 100% data is the foundation of good retailing going forward."

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This episode provides a nuanced exploration of how traditional retailers like Levi's are integrating advanced data analytics into their strategies. While acknowledging the transformative potential of data and AI, the hosts advocate for a balanced approach that blends technological insights with the intuitive expertise of merchandising professionals.

Transcript

[A]

Levi's reportedly crunched the data to jump on the baggy jeans trend. According to the Wall street journal, in 2020, Levi signed a deal with Google Cloud and began gathering data points from purchases, web browsing, retail partner sales and its loyalty program into a Google database and running daily machine learning algorithms designed to identify and predict purchase trends. Jesus Christ. And how many. How many buzzwords were in that last sentence?

[B]

A lot.

[A]

For the last time, Levi's chief Digital. For the first time. Not the last time, man. The. For the first time, Levi's Chief Digital Officer Jason Gowins told the Journal, Levi's was able to continuously pull together data from 110 countries, not 100 and 110 countries, and 50,000 points of distribution, only 1100 of which were Levi's own stores. Hmm. As a result, the new data system helped the company understand that Baggy and Loose silhouettes weren't just for the TikTok generation. They were for everyone. As a result, Levi's dove into marketing campaigns like Live Loose, got to like that campaign, and began evangelizing on the trendiness of roomier fits to its retail partners. And yes, are you buying or selling the impact of data that Levi's claims it has had on its business?

[B]

My God, there's so many merchandising questions in this. I feel like I'm just getting slaughtered and they're going right to me. So I'm interested to hear where you land on this, Chris. But. But look, I'm buying because the price is low.

[A]

You're buying.

[B]

I'm buying because the price is low. And things can only go up from here. Not because I think this baggy jeans example is like the case study I would choose to represent investing in this kind of data. But I think you and I have heard repeatedly over the course of the last several weeks at all these conferences that the number one thing that the retail CEOs and executives that we've been interviewing have been saying that they're investing in is data to support decision making for them, including in merchandising scenarios. I think you also have to be investing in AI tools to kind of aggregate this data to really bring that to something that the buying and merchandising teams at Levi's in this case, can really utilize and help inform some of the decisions they're making. But much like the Walmart story that we were talking about earlier within home testing, I still think that that's going to be the thing that you have to invest in, but you still need this. The art of the Merchandising here, like, you still need. A good merchant would know that the baggage jeans trend is coming. A good merchant understands that. But I think what's cool about this is that I think you start to position Levi's in a more competitive space against some of the fast fashion players out there, like Shein and Timu, who are taking aggregated data on their platforms. How much time, like, what people are searching, how much time they're spending engaging with, you know, games or, or shopping experiences for certain products in the app and then using that data to determine how much of products they make, what trends are kind of coming down the pipeline. I think there's, there's a use case for this. I think this is just an early stage and maybe not the best example for it, but I have a feeling that you're going to, you're going to take this in a completely different direction. Chris. Well, you're not buying. You're not buying.

[A]

I got to tell you, you know me really, really well at this point. You could probably tell from how I did the. I'm, I'm. No, I'm not buying this. I'm selling this hard. In fact, like, I think back, this is like, why, why I personally got into the business that we're in is, is to call, call PS on headlines like this. I'm. I'm selling this so hard. This, this story to me is an example of claiming text impact for something that lines up after the fact, after it's happened.

[B]

Okay, the begging. You're just doing this on Levi, like, for this Levi's.

[A]

Yeah, on the claim, on the claim that Levi's is attributing their success on the baggy jeans trend to their partnership with Google Cloud. I just think it's total baloney. I mean, and seriously, the baggy jeans trend, you didn't see that coming? I saw that coming. You know, like, I mean, and you're telling me, like, you even mentioned all the fashion merchants at Levi's went to data from Google Cloud to tell them that baggy jeans trend was calming. Come on, I wasn't born yesterday. And, and, and, but, you know, my last, I'd say is, good job by you, Jason Gowans, for trying to make Levi's sound much cooler from a tech standpoint than it probably is. And, and for my friends there, like, I've heard very, very different varying degrees of how, how tech forward Levi's is and how, and how much they struggle on the tech side of things. So, so maybe so. I, I just think this is taking a victory lap for something that is just nicely correlated with your sales performance. That's what I'd say. So.

[B]

So to clarify, then, I think so you're saying you're selling the, the claim that Levi's is making. You're not selling the idea that companies should be investing in this type of technology to aggregate data to supply their merchants with, and that there could be a positive outcome from that?

[A]

Yes. Right? Yes. A hundred percent. Yes. Okay, here's.

[B]

I think we're on the same page. I think we're on the same page.

[A]

Yes. Here's how I'm guessing this conversation actually went down. If I had, if I had, you know, a bird's eye view in a Macy's, if I was a fly in the wall or not Macy's in Levi's, if I was a fly on the wall in Levi's, like the fast. The, the, the merchants are like doing their line review. They're like, biggie Baggy Jeans is going to be the trend this year. We're going to buy into it big. And then some computer walk in the sides like, yep, our data says that, that, let's do that. And then they're like, okay, fine, yeah, let's take credit for it. That's how it works. You know that. Well, that's. That's a funny thing about retail. I mean, there's there's only still so much art and sci, you know, so much science that goes into the art of just having to make bet. I mean, Levi's has to make some pretty big fricking bets pretty darn early too. So, like, I don't know, I just, I don't. I'm not buying it. Maybe a little bit, but not buying it.

[B]

All right, all right, fair. Well, we kind of agree not. But not on the Baggy Jeans case study. That's where we'll leave this.

[A]

Oh, yeah. Data. 100% data is the foundation of good retailing going forward.

[B]

Yes, yes.