Fashion and lifestyle retailers are facing enormous challenges. These omnichannel retailers have to deal with customers who expect products to be available and also want the lowest prices. More pricing transparency and comparability combined with long lead times and short product life cycles are putting retailers in the highly-competitive fashion market into the pressure cooker.
A retailer needs to be quick and flexible to be successful in today’s changing market dynamics. That applies to adapting to what customers are willing to pay and being able to precisely calculate that amount. Of course, that amount can change with the stock levels, seasons, weather and other trends, all of which retailers have to keep a trained eye on. Brick & Mortar retailers have to set prices that can compete with online retailers. To do all of this successfully while raising profitability and creating a seamless customer experience, fashion retailers have to offer optimized prices across all channels.
Pricing decisions depend on business strategies and should consider long-term goals such as revenues, profits and stock levels. This is challenged by increasingly shorter seasonal cycles as well as demanding discounting processes and decisions. These are best solved with modern technologies that master automated shipping processes and offer dynamic pricing.
Fashion Retailers Rely on Innovative Machine Learning Solutions for Price Optimization
To successfully navigate the high-level competitive pressures in e-commerce, retailers like OTTO are turning to innovative machine learning solutions. The multichannel retailer’s competitive field is marked by low margins, competition and market conditions that change at an increasingly faster frequency. Decision processes are made with immense amounts of data, a variety of influential factors and under time constraints. Meanwhile, OTTO still has to put a central focus on ensuring a positive customer experience and customer satisfaction. To wow and retain its customers, OTTO has to offer everything on a single platform: Comprehensive selection at competitive prices and good service. Attractive offers, availability and fast delivery times are among the conditions that have to be met for success.
Setting the Optimal Price with Price Optimization
Fashion retail is a seasonal business. On the one hand, product availability has to be ensured for an entire time period. On the other, stocks should be cleared by the end of the season with a minimum amount of leftovers. To achieve this, it is necessary to balance pricing decisions with stock levels. As a “data-driven company”, OTTO has long harnessed the advantage that vast quantities of data provide to make its decisions and has been working with Blue Yonder for more than a decade to calculate precise demand forecasts. When it comes to price optimization, OTTO also relies on artificial intelligence solutions from Blue Yonder.
Pricing decisions at OTTO used to be a mostly manual process. The business acted reactively as employees monitored stock levels, customer behavior and the competition. But automated price management is urgently needed today compared to the days of mail-order retail. Customers expect a fair price and items from brand-name retailers offer 100% price transparency.
The optimal price depends on a variety of influencing factors that can change on a daily basis: Availability, manufacturing, competition, season, weekday, season, time of day, weather, distribution channel, competitor prices, as well as many others. By using Blue Yonder Price Optimization, OTTO can determine the right price at the right time. The machine learning solution incorporates article master data, sales, stock levels as well as competitors’ prices into the decisions it produces. The result allows for better sales with more revenues, higher profit and better customer satisfaction.
By using machine learning solutions, OTTO today offers its customers game-winning prices and the cost-benefit ratio is constantly optimized. This, in turn, improves customer satisfaction, which is seen in fewer leftover end-of-season stock and significantly lower returns.
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