Fashion’s New Approach to Pricing

Soaring inflation has put fashion companies in a difficult position. They can usually either raise prices and risk losing buyers, or absorb the higher costs and let their margins suffer.

Retailers who manage to navigate the narrow path between these scenarios are handsomely rewarded. Levi’s, for its part, stated in its solid last quarter that it was able to increase average selling prices by 10% over the period without seeing a decline in demand, which enabled it to mitigate rising raw material and logistics costs.

Levi’s relied on more than instinct and market research to find the right place to sell their denim.

“These decisions are guided by powerful proprietary technologies and analytics, including artificial intelligence and methodical price elasticity analysis,” chief executive Chip Bergh told analysts and investors on a call on Monday. April 6 to discuss the results.

Levi’s first applied AI to pricing early in the pandemic to determine promotions, a practice it has now rolled out in 26 countries. He realized that it was not necessary to make as many discounts as his competitors, if at all in some cases. The company credits AI for helping to grow its margins over the past year and a half. In its most recent quarter, gross margins reached 59.3% of net revenue, compared to 55.7% before the pandemic, as Levi’s saw more direct-to-consumer sales as well as “weaker promotions, a higher share high full-price sales and price increases,” It said.

A growing number of companies in all kinds of industries are developing pricing models that proponents say are more accurate and adaptable. At the extreme, some food and beverage companies, and especially Amazon, are using AI to enable dynamic pricing, where the cost of an item changes frequently in response to market conditions.

Fashion brands are not there yet. But more and more brands, like Levi’s, are exploring beyond standard mark-up formulas and off-the-shelf pricing software. While these efforts go back years at some retailers, they are attracting new attention as major economies from the United States to Germany to China grapple with high inflation.

However, getting results with AI involves collecting and cleaning large volumes of data, which can be difficult and labor-intensive. Predictions also become more fragile the further they stretch into the future – part of the reason why AI models tend to be used primarily for short-term decisions in fashion, such as the determination of end-of-season discounts.

However, businesses may find the returns worthwhile. McKinsey noted in late 2018 that some fashion companies using advanced analytics in pricing saw their margin and sales increase by three to six percentage points.

The price is right

Probably the biggest difference between old and new pricing methods is the volume and variety of data they use.

The measure of the change in consumer demand in relation to changes in price is called price elasticity. According to Michael Orr, director of product marketing at Blue Yonder, who developed his own artificial intelligence software used by fashion retailers such as Orsay, Bon Prix and BestSecret, traditional pricing software evaluates it using with a simple, rules-based approach. If the cost of a raw material increases by a certain percentage and you want to maintain your margin, you increase the price by a certain amount. Or you can price competitively and say that a competitor has lowered their price by a certain percentage, so if you want to match or beat them to keep the sale, you also lower your price by that much.

But AI can also take into account other types of data, like detailed weather forecasts, to produce more comprehensive price elasticity models. Orr said Blue Yonder’s AI incorporates about 20 separate weather factors, such as dew points, predicted high and low temperatures, and sunrise and sunset times.

Levi’s, which developed its own AI, found that the slight temperature differential between Rome and Milan is enough to affect shopping behavior in those cities, said Katia Walsh, senior vice president and chief strategy and society’s artificial intelligence.

The company’s traditional approach to pricing was based on data from competitive intelligence and market research, and “was always grounded in intuition and consumer surveys,” it said. she explains. Merchants and planners are still involved, but Levi’s can now integrate thousands of data points into AI models that allow it to “predict the optimal price at which a consumer would buy each of our thousands of products in our portfolio across the world,” according to Walch.

“They’re even fit and finish specific,” she said. “So our iconic classic 501s, for example, we know what the optimal price point is not only for 501s, but also for a specific dark finish, a specific fit of 501 in various parts of the world.” (Levi’s now uses AI for pricing and promotions in 26 countries.)

Data can include standard items, such as what Levi’s has charged for an item in the past and its sales history, but also weather, economic outlook, consumer sentiment and social media trends. Some of these data sources are more predictive than others, Walsh said, but she added that the ability to combine disparate sources into a model is what makes it effective.

Dynamic pricing

A problem recognized by Walsh and Orr is that these large volumes of data must be cleaned of problems such as errors and inconsistencies. One model will come up with very different suggestions for shrinking a tank top if you mix Celsius and Fahrenheit in the weather forecast.

The pricing decisions fashion retailers make with AI still tend to be more short-term, like whether or how much to mark items for sale, not so much about setting upfront prices. Indeed, AI models are most accurate when predicting short-term scenarios, according to Orr. Over a longer period, variables can change repeatedly, although he pointed out that traditional pricing software suffers from the same flaw.

During the first half of 2022, however, Levi’s also began using AI to set initial prices.

However, one of the potential benefits of AI’s rapid short-term predictions is that it can allow businesses to react more quickly to changing market dynamics. It’s conceivable that fashion companies could even one day use AI for dynamic pricing.

“I think companies are looking at this,” said Simeon Siegel, managing director of equity research at BMO Capital Markets. “We are in the very early stages of our efforts to determine how to benefit from dynamic pricing without causing a backlash.”

In Orr’s view, there’s resistance from retailers who don’t think consumers would accept it, even though they’re already used to the prices for Uber rides, hotel rooms and items. on Amazon that change throughout the day. In fashion, retailers typically don’t even make store-specific prices, he pointed out, noting the added wrinkle that clothes sold in physical stores tend to have price tags attached that are also expected to change.

On the other hand, Siegel said price does not exist in a vacuum: buyers and retailers already recognize that the same item is priced differently when sold in an outlet store.

The idea of ​​more fluid pricing, allowing retailers to adapt to market conditions and even potentially different buyers, is appealing to companies that imagine the perfectly optimized marriage of margins and sales.

“Are we already there in a significant capacity? No,” Siegel said. “Are we going in this direction? I think so.”

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