Great, great post found about how retailer ZARA made a constructs that enables them to act fast, efficient and effectively. A living proof that a construct does not always imply heavily investments in technology. Keen and eager people are the keys to success!
by: John Caddell
Andrew McAfee’s blog is a great place to learn about how businesses can gain competitive advantage by their use of IT. But yesterday he took a left turn and discussed business situations where data crunching is not helpful to decisionmaking, and I loved it.
In “When Information is NOT the Answer,” McAfee takes issue with Don Sull’s assessment of fashion retailer Zara’s “fast fashion” approach, at least when it comes to data-driven decisionmaking. Writes McAfee:
Sull stresses that “Zara’s business model demands good information,” which is certainly true. But my work with the company (see this Sloan Management Review article and this case study) revealed something I found fascinating: Zara succeeds in large part because the company makes comparatively light use of market data and sales information, at least as these terms are commonly understood in the retailing industry.
McAfee further explains the difference he sees between Zara and other retailers’ use of information:
The decisions about which clothes should to go which stores at what time(s) are probably the most important decisions made by any large apparel retailer. Most chains make them by collecting large amounts of daily sales data from stores, combining it with other hopefully relevant information, then applying a variety of statistical techniques to generate a forecast – a quantitative prediction about what will sell. This forecast is used to push the ‘right’ items – the ones predicted to sell — over time to each store.
Each retailer forecasts differently, of course, but I find their techniques broadly similar: they all gather lots of data, analyze it centrally, then use the resulting predictions to determine shipments to stores. In this model, the stores themselves have fairly limited roles: they are expected to record data accurately and send it promptly, then do their best to sell whatever headquarters decides to send them.
This seems sensible enough, and it also seems logical that as the business world gets more and more turbulent more and more supporting data will be required. This data will need to be acquired, analyzed, shared, and interpreted with ever-greater velocity, requiring ever-bigger computers, ever-faster networks, and ever-more-quantitative decision makers.
But Zara, operating in an intensely turbulent environment, does something totally different. The company doesn’t really generate a store-level sales forecast at all. Instead, it relies on its store managers to tell headquarters what they think they could sell immediately at their locations. Headquarters then gets as many of these clothes as possible to the stores as quickly as possible.
What’s more, the store managers are given very few quantitative or analytical tools to help them make their short-term predictions. They rely largely on intuition and experience, on walking the floor and talking to customers and employees.
I think this distinction between high-level numbers (what McAfee calls “general knowledge”) and the ground-level view of the customer needs (”specific knowledge”) is very important. In order to gather and understand specific knowledge, it’s necessary to be very close to the customer, “walking the floor and talking to customers and employees.”
Small retailers have always worked this way. When I worked at the local hardware store during high school, each department had a buyer who looked at stock levels, assessed what was selling, took the season into account, and placed orders weekly.
For large retailers, the fashion has been, as McAfee writes, to gather scads of information “Numerati“-style and make central purchasing and stocking decisions. Overreliance on this had a negative effect on one large store: Home Depot.
So it’s good to learn that at least one mega-retailer is using high-level number crunching judiciously, and relying on the folks closest to the customer to set ordering levels at each store. However, I think even they can do better.
As McAfee describes it, Zara “relies on its store managers to tell headquarters what they think they could sell immediately at their locations. Headquarters then gets as many of these clothes as possible to the stores as quickly as possible.” In other words, headquarters’ visibility into the “specific knowledge” in the store is limited to the managers’ forecasts.
I wouldn’t propose changing the ordering process, but I do think Enterprise 2.0 has a role here in sharing specific knowledge more widely. Information in the form of customer or employee narratives (generated by a simple prompt–”what was the most interesting thing that happened today?” or “tell me about your experience today” for example) could be captured at the store level and uploaded to a story-bank accessible to all Zara employees–especially those at a remove from the direct customer experience.
Through tools like commenting, scoring, nudging, sharing, etc., those narratives can inform a much broader base of employees what customers are doing and how they’re reacting to the products on the shelves. [I have been trying out a very cool open-source story-banking tool currently in alpha test that would fit this need perfectly.] This would provide a great service to the company by “bringing the outside in” (John Kotter’s phrase) and enabling all employees to make decisions with much deeper customer insight than they now possess.
This narrative data can supplement the high-level numbers, essentially combining McAfee’s general and specific knowledge to provide better insight into customers–for product, marketing and customer service purposes.
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