“It is a fatal error to theorize before having data”. Sherlock Holmes (Sir Arthur Conan Doyle)

…particularly because the emergence of so-called “Big Data” makes the problem of data scarcity a thing of the past. Capturing data and transforming it into business insights as a core element of strategy has long helped Spanish retailer Zara increase productivity, improve decision-making and gain competitive advantage. As a result, it overtook Gap as the world’s largest clothing retailer in 2008.

Zara has long been a symbol of supply chain excellence due to its ability to spot trends as they emerge and deliver new items to stores quickly to meet the needs of its fashion-conscious customers. In an industry where the standard lead time (design, production and delivery of new garments) is approximately nine months, Zara leads the way with just two to three weeks. However, the driver behind this effective supply chain is the use of data and analytics for accurate decision-making and forecasting. It is enabled through processes and systems built to bring together data, analytics, frontline tools, and people to create business value. The key differentiating uses of Zara analytics are:

– institutionalizes the collection and use of real-time statistical market data. Zara’s cross-functional design teams pore over daily sales and inventory reports to see what’s selling and what’s not, continually updating their view of the market. Twice-weekly orders from store managers provide more real-time information about what might sell;

– supplement statistical market data with detailed raw market data. Trained retail managers regularly send word of mouth about customer wants and preferences, from “the length of this skirt is too long” to “our customers don’t like the fabric of this dress.” Managers can also suggest modifications to an existing style or propose entirely new items or designs. The benefit of store information is summed up in the example of a line of tight clothing that was not selling. Feedback from stores was that women loved the look of the skintight clothing, but did not fit into their usual sizes when trying on the garments. Zara pulled the items and replaced the labels with the following sizes and sales exploded;

– create an adaptive and informal planning process. It is rooted in the company’s flexible supply chain, as it maintains strong ties with its 1,400 third-party suppliers, who work closely with its designers and marketers. Based on market data, Zara experiments with a wide variety of offers in small batches. If they turn out to be a success, production increases in response to local conditions while keeping inventories tight and markdowns low;

– disseminate information widely throughout the organization. Designers, pattern makers, marketing managers and merchandisers, as well as everyone else involved in production, are housed on a single floor of open-plan offices. This allows for frequent discussions, chance encounters, and visual inspection. The entire team can diagnose the broader market, see how their work fits into the bigger picture, and spot opportunities that might otherwise fall through the cracks of organizational silos;

– build a simple and effective information technology system available to all. Zara’s internal IT reflects the shape of the organization. It is unsiloed and accessible to vendors and suppliers who report that it is easy to use and quick to provide answers; Y

– build a culture of using data to learn new things and discover the right answers. Data analytics is at the base of the Zara model and its use is encouraged for decision-making since bad decisions are not severely punished. Failure rates for new Zara products are reported to be just 1% versus an industry average of 10%.

A few years ago, Zara entered the virtual arena of e-commerce in the United States, Europe, and Japan. With this move, it entered the next generation of usage analytics for real-time marketing and decision-making: tracking individual customer behavior from click streams across the Internet, updating their preferences, and modeling their likely behavior over time. real in addition to social monitoring. -network conversations and location-specific smartphone interactions.