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2013-10-03

Uniqlo Finds Deep Data
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Japanese fashion giant Uniqlo quietly gathering data.

In Bangladesh’s villages and on its street corners, Japanese fashion giant Uniqlo is quietly gathering market data to help it in its ambition of becoming the world’s top clothing brand in seven years.

Like other global brands, Uniqlo set up shop in Bangladesh’s cities to tap into their cheap labor and make casual fashion wear affordable and profitable. But it has also gone into the country’s impoverished villages and neighborhoods looking for a way to give something back to a country that many said was being exploited by the garment industry.

In 2010, Uniqlo formed a joint venture with Grameen Healthcare Trust, a not-for-profit organization created by Nobel laureate Muhammad Yunus, founder of the microfinance organization Grameen Bank.

The purpose of Grameen Uniqlo is to sell clothes made by locals, for locals, at affordable prices for people living near the poverty line, and to nurture local retailing expertise. But as the local retail staff diligently tallied their door-to-door sales of T-shirts, priced at $1 or $2, from village to village, the company that owns Uniqlo, Fast Retailing Co., realized it had stumbled on a wealth of untapped data on a rapidly growing economy.

“We learned which communities craved bright reds and greens, which preferred muted tones,” Fast Retailing Senior Vice President Yukihiro Nitta said in an interview. “We learned to stock more men’s wear, because women who wear saris buy mostly leggings and home wear.”

When the venture opened its first stores in Dhaka in July, Mr. Nitta discovered that in a shop full of $2 and $3 items, shoppers will spend $10 for a pair of jeans or $9 for a shirt with a collar that can be used on more formal occasions. The stores now stock 70% to 75% of a store’s floor space with items for men.

Fast Retailing — whose global empire is expanding at a breakneck clip of almost 20 stores a week — is looking for any edge it can use to overtake bigger rivals Gap Inc., Hennes & Mauritz AB and Inditex SA. Fast Retailing’s billionaire chief executive Tadashi Yanai aims to make the company the world’s biggest clothing retailer by 2020, with his focus on China, Southeast Asia, and emerging markets.

“Bangladesh is extremely attractive as a market, not just as a production hub,” Mr. Nitta said. “The know-how we are accumulating will be invaluable as the economy grows. It’s also a great resource as we expand throughout Asia.”

Each Grameen Uniqlo outlet in Dhaka is a fraction of the size of the mammoth Uniqlo outlets in Shanghai or New York, but the network is growing. The venture will open two more stores on the first weekend of October, doubling its stores there in less than three months. Fast Retailing envisions a nationwide chain in three or four years. The stores plow all profits back into running and expanding the venture.

The venture’s growth in Dhaka follows the tragic collapse in April of the Rana Plaza garment complex, in which more than 1,100 workers died. Demonstrations erupted around the country, resulting in the closure of hundreds of clothing factories outside Dhaka last week.

Fast Retailing, which says it wasn’t affected by the closures, signed a legally binding labor-safety accord in Bangladesh in August, pledging more transparent inspections and oversight of fire and safety standards.

Our Thoughts

To be honest, most of us hear about the importance of data almost every day when it comes to retail and banking with regards to market entry and network expansion planning. People often ask us questions about what kind of data we have, how ‘good’ our data is, or where we get our data from.

Don’t get us wrong, these are all valid questions, but the reason we chose to highlight this story about Uniqlo is because they hit it right on the head. Uniqlo was willing enough to explore different sources of data. Not only did this lead them to find interesting data, but they actually made ‘sense’ of it.

Thus, our point is that data is almost close to useless if you don’t know what to do with it. More importantly, combining different types of data like location data, sales data, and local knowledge is imperative to gain a clearer understanding of the market. From our experience, a lot of companies (large and small) still don’t understand the value of visualizing all of this data on a map to uncover important correlations that you can’t see in a spreadsheet.