Zara – The use of ‘Big Data’ to create commercial value

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

…particularly because the advent of so-called “Big Data” makes the problem of scarcity of data a thing of the past. Capturing data and transforming it into business information as a core element of the 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 at 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, front-line tools, and people to create business value. The key differentiating uses of Zara analytics are:

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

– supplement statistical market data with detailed raw market data. Trained retail managers regularly send word-of-mouth feedback 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 come up with completely new items or designs. The benefit of store information is summarized in the example of a tight clothing line that was not selling. Feedback from the stores was that women loved the look of the form-fitting clothing, but did not fit into their usual sizes when trying on the garments. Zara removed the items and replaced the tags with the following sizes and sales exploded;

– create an adaptable and informal planning process. It is embedded in the company’s flexible supply chain, as it maintains strong ties to 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 small-lot offerings. If they turn out to be a success, production increases in response to local conditions while keeping inventories low and markdowns low;

– disseminate information widely throughout the organization. Designers, pattern makers, marketing managers and tradesmen, 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 between 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 has no silos and is accessible to vendors and suppliers who report that it is easy to use and quick to provide answers; and

– 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 for decision-making is encouraged 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 field of electronic 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 internet clickstreams, updating their preferences, and modeling their likely behavior over time. in addition to social monitoring. -Network chats and location-specific smartphone interactions.