The fashion industry creates 40 million tonnes of textile waste globally each year.
Scarier still, that figure doesn’t take into consideration packaging and other non-textile supply chain waste.
As a retailer or brand, waste from excess inventory is a huge cost to your business. It could also damage your brand equity.
Remember in 2018, Burberry was forced to backtrack after destroying $36.8 million worth of its own products? The excuse? To preserve brand exclusivity.
Instead of exclusivity, Burberry saw backlash from shoppers and ended up outlawing the practice.
In that same year, H&M admitted to a hoard of $4.3 billion worth of unsold clothing.
The pandemic has only exacerbated the issue of fashion waste. COVID-19 saw overproduction and over-ordering come to a head. Shops were forced to close their doors and consumer behaviour changed – almost overnight – as demand for loungewear rocketed by 49% in the UK alone.
Sustainability is more important than ever.
Retailers and brands have a responsibility to minimise their environmental impact – of both their products and operations. This goes beyond ‘greenwashing’.
A recent survey by Deloitte highlights growing consumer expectations. Almost a third of consumers surveyed have stopped shopping with specific brands due to ethical and sustainability concerns.
Sustainability is on the global political agenda too. COP26 underlined the need for change in the fashion industry, from greater transparency to improved trade policies. Meanwhile the Fashion Industry Charter for Climate Action’s mission is to push the fashion industry towards net zero emissions by 2050.
Decision intelligence is the solution.
Retailers often look to shift leftover stock with sales. Over the last couple of months we’ve seen sales for Black Friday, Cyber Monday, Boxing Day and New Year.
Traditionally, retailers manually worked through their inventory and used ‘gut feel’ to discount products in their online and physical stores. But is there a better way?
We believe decision intelligence is the answer.
Decision intelligence places data science and artificial intelligence (AI) at the heart of commercial decision making. Machine learning improves the effectiveness and speed of product, pricing, operational and supply chain decisions, driving revenue and improving customer retention.
At its core, decision intelligence helps people make better, faster and more unified decisions.
Applying data science and AI to fashion decisions.
Fashion retailers need to think smarter to get on top of issues such as excess inventory and sustainable business operations.
Decision intelligence pulls together customer, product, transactional and web browsing data to improve the quality and speed of decision making in key commercial areas:
1. Forecasting stock levels.
Overproduction. Over-ordering. Excess inventory. A retailer’s worst nightmare?
The good news – data and AI can be used to forecast stock levels based on customer demand and product analytics. This reduces waste, allows for more informed buying decisions and improves circularity.
2. Optimising online product merchandising.
Right product, right place, right time. Improving online merchandising helps increase conversions before the deadline for shifting stock (and before the need for markdowns).
Decision intelligence helps retailers create customer-led product attributes – helping customers find the products they’re looking for quickly and easily.
3. Setting the timing and level of markdown.
It can be tempting to set markdowns based on previous years or on what feels ‘right’. Decision intelligence identifies the ideal level of markdown based on price sensitivity and elasticity. A combination of data science and AI enables retailers to time offers and discounts based on stock levels and when customers are in the market to buy.
4. Reducing online returns.
Seasonal sales encourage more orders. More orders mean more returns. As the demand for ecommerce increases, so does the mounting pressure of processing returns.
Many fashion retailers are sending returns straight to landfill rather than figuring out the logistics. But what if data science could reduce the number of returns in the first place?
Machine learning can help retailers understand more about their customers as individuals, so they can recommend the right products based on style and fit.
5. Increasing customer satisfaction.
Customers are savvier than ever. A 2020 survey found shoppers considered only 52% of sale purchases to be ‘good buys’ – so what about the remaining 48%?
Decision intelligence can help fashion retailers drive satisfaction and loyalty through personalisation at an individual level.
Major retailers such as Amazon are already using data science and AI to improve decision making. Proactive adopters of decision intelligence will navigate key issues such as waste and sustainability faster than their competitors – ultimately increasing profits and succeeding.
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