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3 Ways AI is Shaping European Retail

Retailers must constantly adapt to customer tastes, employee feedback, pricing trends, fluctuating markets, and shifting economies. In 1997, Wal-Mart entered the German retail market by acquiring... - Machine learning predictive analytics with no-code
10 April, 2023
Est. Reading: 8 minutes

Struggling Supply Chains and Stock Issues Are Turning Retailers to Machine Learning Solutions

Retailers must constantly adapt to customer tastes, employee feedback, pricing trends, fluctuating markets, and shifting economies. In 1997, Wal-Mart entered the German retail market by acquiring the failing German retail chains Wertkauf and Interspar—and essentially ignored said precepts.

First, Wal-Mart got into trouble with its “predatory pricing”—and Germany’s highest court ruled that Wal-Mart’s pricing strategy undermined competition and violated the country’s antitrust laws. Wal-Mart also forced employees to smile and do bizarre calisthenics and chant “Wal-Mart! Wal-Mart!” before starting a shift.

Germans did not exactly like these practices—nor did they like the low-quality goods or Wal-Mart’s antipathy towards labor unions, forbidding romantic relationships amongst employees, and making them spy on one another. By 2006—after losing $1 billion—Wal-Mart sold its 85 stores and said “Schönen Tag” to Germany.

If Wal-Mart—with 10,500 across the globe and annual revenues of $573 billion—couldn’t succeed in Germany, then you know the retail game in Europe is a challenge. And things have only gotten tougher. COVID has sent more shoppers online, inflation is raging, supply chains are being choked, labor costs are rising, and customers are increasingly demanding. So what’s a retailer to do? It starts with an honest assessment of the situation.

Over the last decade, U.K. retail sales have actually risen by 35.7%. However, in that same time period—profits have dropped by 10.9%, according to a new report by Alvarez & Marsal (A&M).

This so-called profit crisis has caused many U.K. retailers to go bust. This situation is sadly exemplified by Cath Kidston, the British brand famous for its floral designs. Cath Kidston opened its first shop in London’s Holland Park in 1994. For its 20th birthday, the retailer celebrated the opening of its 7,000 sq. ft. store in London’s posh Piccadilly.

By the financial year ending March 2018, The Cath Kidston Group reported an operating loss of £19.6m and sales growth of only 1.2%. By March of 2023, Next bought the Cath Kidston brand for £8.5m after the British retailer again fell into administration—or to be given protection from creditors threatening to recover outstanding debts.

Whilst the retail crisis has caused many to close their doors, others have been looking for data-driven solutions and to new technologies—such as AI and machine learning—to keep pace with the current climate.

In this blogpost you’ll see:

  • Big problems facing European retailers today
  • Ways AI and machine learning can helps retailers today:
    • Supply chain management
    • Sales prediction
    • Price optimization
  • Some interesting AI for retail use cases

Why Are So Many Retailers Struggling Or Even Falling Into Administration?

Retailers across Europe are struggling—and the fate of Cath Kidston has become all too common. Let’s look at the top factors influencing retailers’ recent struggles:

Online Economy and Inflation: The COVID pandemic has only accelerated EU and UK consumers’ migration to online retailers. The proportion of EU e-shoppers has increased from 55 percent in 2012 to 75 percent in 2022. In 2022, the most common online purchases of goods in the EU were clothes (including sports clothing), shoes or accessories (ordered by 42% of internet users).

The UK, meanwhile, has the most advanced e-commerce market in Europe—with nearly 60 million e-commerce users in 2022. However, as Inflation in the United Kingdom unexpectedly rose to 10.4 percent by February, 2023, 82% of UK shoppers have been willing to switch their usual brands for cheaper alternatives. UK Cost-of-living increases have also caused BNPL (Buy now, pay later) payments to increase by over 10%.

Reverse Supply Chain Woes: With an increase in online shopping comes an increase in online returns. An increase in online returns leads to the tidal wave now known as the Reserve Supply Chain. Returns in the UK alone cost retailers £60 billion per annum.

In the EU, Germany is the so-called “European Champion” of e-commerce returns,  according to a recent study by the University of Bamberg. Almost one in four online purchases in Germany (24.2%) ends up being sent back to the retailer, with the following results and ramifications:

  • Over 93% of returned items can still be sold as new
  • The percentage of returns disposed of by e-tailers is 1.3%—or about 17 million returned items
  • An estimated 795,000 tons of CO2 were emitted by returns in Germany in 2021

In 2003, EU Reverse Supply Chain legislation broke ground when it required tire manufacturers operating in Europe to arrange for the recycling of one used tire for every new tire they sell. So, whether it’s the cost of returns, the time spent, potential environmental damage, or compliance—the Reverse Supply Chain is a potential peril for European retailers.

Traditional Supply Chain Woes: The supply chain for European retailers—if not the world—is a mess. German companies are now waiting on accumulated order reserves for an average of 4.5 months, according to an analysis by the Munich Economic Institute (IFO). This is the greatest gap in the history of this index—which was created in 1969.

Germany also just passed a new Supply Chain Due Diligence Act—requiring large companies to ensure that social and environmental standards are observed in their supply chain. Failure to comply could lead to:

  • Fines of up to €800,000, or up to 2% of their average annual global turnover
  • Exclusion from winning public contracts in Germany for up to three years

A recent survey from London-based K3 Business Technology Group revealed three key ways in which German fashion houses are unprepared for this new legislation:

  • A lack of transparency in supply chains
  • Inefficient, non-automated methods to store compliance information and certifications
  • A lack of new technologies

Despite the lack of preparedness, the fashion, textile, and retail industries, respectively, are planning to leverage their GSCA implementations for their upcoming marketing campaigns.

Out of Stocks & Excess Stock: In addition to excessive returns and supply chain woes—retailers in the UK are facing a tale of two stock stories:

  • Grocers can’t fill the shelves
  • Clothing retailers have excess stock—especially after Christmas and the New Year

The German-owned discounter Aldi has joined Asda and Morrisons to ration certain fresh produce lines as salad crop shortages hit the UK. Food tsar and Leon founder Henry Dimbleby has blamed the UK’s “weird supermarket culture” for the ongoing shortages of certain vegetables on grocers’ shelves.

In sharp contrast—more than half of UK fashion retailers have excess stock after a disappointing start to 2023. In 2022, 22 percent of excess stock was written off altogether by apparel sellers in the UK.

Nearly half of UK fashion retailers have stated that there would be “dangerous ramifications” for their cash flow if they failed to sell excess stock—whilst 77 percent are planning to offer even bigger discounts to help move unwanted products, a recent fashion survey notes.

Thankfully, it’s certainly not all bad news for European retailers—especially with the new opportunities offered by AI. From inventory management to price optimization, modern technologies are giving retailers advantages that couldn’t have even been dreamt of even 10-20 years ago. Let’s take a look at how retailers can satisfy customers with AI and machine learning.

Accelerating Supply Chains and Inventory Management With AI and Machine Learning

On May 3, 1966, the Sea-Land container ship Fairland arrived in Rotterdam for the first time—loading and unloading 35-foot containers with its own cranes. The effect of container transportation for Rotterdam and Europe—and all of global trade—cannot be understated. Some have even called the container “the invention of the century.”

While containerization revolutionized post-war supply chains in Europe—today it is AI and machine learning that is changing the game in the following ways:

  • Demand forecasting: AI and machine learning algorithms can analyze vast amounts of historical sales data—and external factors like weather, seasonality, promotions, and economic indicators—as well as customer behavior to forecast future demand accurately.
    • Inventory management: By analyzing inventory levels, sales data, and other factors in the supply chain, retailers can ensure that they have the right amount of inventory in the right place at the right time—reducing the risk of overstocking or stockouts.
  • Supplier management: Looking at supplier performance data to identify potential issues—such as delivery delays or quality problems—empowers retailers to make informed decisions about which suppliers to work with and how to optimize their supplier relationships.
  • Transportation optimization: Machine learning can optimize transportation routes, modes, and scheduling to reduce costs and improve efficiency. This optimization helps retailers deliver products to customers faster and at a lower cost.

These days, the Port of Rotterdam handles about 467.4 million tonnes and has installed AI Sensors throughout the huge dock facility to continuously gather real-time data about air temperature, wind speed, (relative) humidity, turbidity and salinity of the water plus water flow and levels, and tides and currents.

By leveraging the power of AI, the Port of Rotterdam can more accurately predict what the best time to moor and and the best time to depart.

Improving Sales Prediction With Machine Learning

Amazon and Zalando are Europe’s top e-commerce brands. Both also prefer to be seen as technology companies—not just retailers. Zalando embodies the essence of technological thinking towards European e-commerce.

Zalando can also boast that it uses recommender systems so you can easily find your favorite shoes or that great new dress. They want these items to fit you just right—so another set of algorithms will give you the best size recommendations. And even if it’s in the middle of Black Friday or Cyber Monday—their demand forecasts will ensure that everything is in stock.

So how do they do it? It starts with their web design. When you first visit the store—you see three big banners: women, men, kids. From the jump, the store was designed so the user will unwittingly tell the company products they are looking for.

The Zalando AI-based personalization engine will then provide customers with personalized product recommendations based on said browsing and purchase history—in addition to their location and the time of day.

This personalized approach to sales has helped Zalando to increase customer engagement and loyalty—and ultimately drive higher sales. Anticipating customer behavior—what they will want to buy and when and where and how they will want to buy it—is crucial to Zalando’s success.

This AI approach also helps them predict seasonal demand. Forecasting sales is essential for retail companies since it directly affects the identification of benchmarks and the determination of incremental impacts of new initiatives, as well as the planning of resources in response to expected demand and projecting future budgets.

Determining demand for certain products is an asset that guides retailers to know how much of each product should be stocked. This estimation is affected by the seasonality of sales, economic indicators, pandemics, weather conditions, and much more.

The European eCommerce market is expected to grow by 80% between 2022 and 2026. Hence, modern European e-retailers need an end-to-end machine learning platform that can forecast efficient and scalable sales predictions by training the data drawn from sales systems, company databases, local archive files, and external variables.

Improving Price Optimization Starts By Focusing on Tomorrow and The Day After Tomorrow

Decathlon, the world’s largest sporting goods retailer, has 47 stores across the UK and 33 in Germany. Decathlon UK uses an AI-powered customer messaging platform that enables better personalized customer experiences through one-on-one conversations on social and messaging apps.

Decathlon Germany, meanwhile, leverages machine learning to personalize the customer experience with tailored product recommendations and promotions based on individual customer data best-aligned with their needs, preferences, and behavior.

With regards to forecasting and price optimization—the process of finding the optimal price point for a product or service—Decathlon prefers machine learning—as it’s more focused on tomorrow and the day after tomorrow than traditional analytics.

Decathlon uses an AI-driven dynamic pricing system that analyzes real-time data on customer behavior, competitor pricing, inventory levels, and market demand to adjust their prices. Their system also uses machine learning algorithms to make predictions about product demand, empowering Decathlon to optimize their pricing strategies to meet customer needs and preferences.

Elite retailers like Decathlon know that traditional market research into sales and customer satisfaction done by companies to optimize their prices can be very slow and may not serve companies’ best interests profit-wise in the long term.

It’s vital to evaluate the market value of the product and customer behavior in possible price levels to ensure the best profit scheme—along with minimizing errors in price estimation.

In the same way, ML Studio offers solutions for pricing suggestions that leverage its access to broader databases than traditional manual sales records. ML Studio democratizes AI by making it accessible for your organization to solve real-world retail problems—such as sales prediction and price optimization—with a robust and ready-to-use End-to-End AI platform that doesn’t go off budget.

Get in touch for a personal demo.

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