Zara is undoubtedly one of the most successful fashion retailers on the high street at the moment and it is widely acknowledged that one of the main drivers of this success has been their unwavering focus on the customer. They are obsessive about their customers and this has defined the company’s culture from the very beginning back in 1975.
The term customer co-creation sums up the Zara approach - with the company co-creating its products by leveraging its customers input on stock and prioritising continuous customer feedback.
This is personified by the following, very real, story;
Back in 2015 a lady named Miko walked into a Zara store in Tokyo and asked the store assistant for a pink scarf, but the store did not have any pink scarves. The same happened almost simultaneously for Michelle in Toronto, Elaine in San Francisco, and Giselle in Frankfurt, who all walked into Zara stores and asked for pink scarves. They all left the stores empty handed – an experience many other Zara fans encountered globally in different Zara stores over the next few days.
7 days later, more than 2,000 Zara stores globally started selling pink scarves, 500,000 pink scarves were dispatched – to be exact. They sold out in 3 days.
How did such lightning-fast stocking of pink scarves happen?
In the fashion world, a trend starts small but develops fast.
Zara employees are trained to listen, watch and be attentive to even the smallest signals from their customers, which can be the first sign that a new trend is taking shape. The company knows that the quicker it can respond, the more likely it is to succeed in supplying the right product at the right time across its global retail chain. Zara has set up sophisticated technology-driven systems, which enables information to travel quickly from its stores back to its headquarters where the design teams work closely with the store managers to deliver the products that customers want.
The fact that Zara’s customers and designers are inextricably linked with the creative team getting their inspiration from customer insight is a key factor in the company’s success but it’s not the only part.
Equally as important is how those teams harness that data to quickly make decisions and then create a product that can be brought to market quickly and at scale.
Zara has one of the most sophisticated and complex supply chains in the world, enabling it to take customer feedback like this quickly and churn out the volume of product it needs to fill its thousands of stores.
This supply chain has been a continual work in progress since 1975 and is out of reach for the vast majority of retailers to achieve at scale, the modern day fast fashion brand might bring something to market in 2-3 weeks but they’re often only able to back it with a couple of hundred units or in a best case scenario, a couple of thousand.
Zara has built the perfect mix of customer feedback, data, and a world class supply chain to secure its market-leading global position in high street fashion, so how can others keep up?
In short, many can’t, they can adopt systems that enable them to have a model similar to Zaras, but copying that all-important speed is nigh on impossible.
Getting as close to this model as possible is why we built Product Future, to help brands and retailers get feedback on products from real customers before they commit to stock decisions and to be able to quickly understand emerging trends before taking action.
We do this by showcasing pre-release products to shoppers on the Mallzee app and gathering authentic consumer feedback on potential items spilt by customer demographic and benchmarking the data against historical and competitor product ranges, all quickly and without any technical integration. Our benchmarking data pool now features over 3m products over 500m customer opinions.
This approach is now helping brands and retailers of all sizes improve their sell-through rates by putting the customer at the heart of the product development strategy and helping buying and merchandising teams to have more confidence in their stock selection decisions.