Reinventing Retail: Customer Intimacy in the Data Era

Retail has continually reinvented itself over the past 100-plus years. Every 20 to 30 years, the form of retail has changed to meet the changing tastes of the public. McKinsey & Company, the global strategy consultancy, has explored the history of retail in depth, citing five distinct timeframes:

*1900s: The local corner store was prominent in many towns. These small variety stores offered a range of items, including food, clothes, tools, and other necessities. The primary goal was to offer anything a person would need for day-to-day life.

*1920–1940: The corner store was still prominent but had grown to a much larger scale. In this era, department stores first began to emerge, and some specialization of stores began to occur.

*1940–1970: In order to effectively deal with some of the specialization seen in the previous era, this timeframe was marked by the emergence of malls and shopping centers. This allowed for concentration of merchants, many of whom served a unique purpose.

*1970–1990: Perhaps best described as the Walmart era — a time when large players emerged, putting pressure on local store owners. These massive stores offered one-stop shopping and previously unseen value in terms of pricing and promotions. The size of these stores gave them economies of scale, which enabled aggressive pricing, with the savings passed on to the consumer.

*1990–2008: This era was marked by increased focus on discounting and large selection, coupled with the emergence of e-commerce.

Each era represented a significant innovation in the business model, but more important was the impact it had on each part of the retail value chain: merchandise and pricing, store experience, and the approach to marketing. Each new era has longed for balancing the new innovations and expansion with a key hallmark of the past: customer intimacy.

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Retail, by definition, is mass market. It has been through every era. While subtle changes in approach have occurred, very few have captured the intimacy of the original corner store. The corner store’s owner knew the customers personally; he understood what was happening in their lives, and the store became an extension of the community. In the Data era, mass marketing can reclaim the corner-store experience.

Stitch Fix

Stitch Fix is a data era retailer, focused on personalizing a shopping experi- ence for women. While many women love clothes shopping, Stitch Fix realized that it is an inefficient experience today. It requires visiting many stores, selecting items to try on, and repeating. In fact, a successful shopping trip requires a relatively perfect set of variables to align:

*Location: A store must be near the shopper.
*Store: The store itself must interest the shopper and draw them in. Clothing: The clothing in the store must be of interest to the shopper.
*Circumstance: The clothing must match the circumstance for which the shopper needs clothes (dinner party, wedding, outing, etc.).
*Size: Even if all the preceding elements are present, the store must have the right size clothing in stock.
*Price: Even if all the preceding elements exist, the shopper must be able to afford the clothing.

To some extent, it’s amazing that all of these variables ever align. And perhaps they do not, which leads to compromise. But if all the variables could align and occurred repeatedly, would the shopper be more inclined to buy? Yes, and hence the premise of Stitch Fix.

Stitch Fix is disrupting fashion and retail, targeting professional women shoppers who want all the variables to align. These women do not have the time nor perhaps inclination to search for the alignment and hence, Katrina Lake, the CEO and cofounder states, “We’ve created a way to provide scalable curation. We combine data analytics and retail in the same system.”

When a person signs up for the service, she provides a profile of her prefer- ences: style, size, profession, budget, etc. The data from that profile become attributes in Stitch Fix’s systems, which promptly schedule the dates to receive the clothes, assign a stylist based on best fit, and enable the stylist to see the person’s profile (meaning her likes and dislikes). The customer also specifies when and how often she wants to receive a fix, which is a customized selection of five clothing items. Then the data-and-algorithms team will present sugges- tions to the stylist. This recommendation system helps the stylist make great decisions. Once the customer receives the fix, she can keep what she wants and send back the rest. Stitch Fix obviously maintains the data on preferences so that, over time, it becomes a giant analytics platform, where recommendations can be catered to a unique shopper. Not since the corner store has such intimacy been available, and it’s all because of the data. Clients are happier, the job of the stylist is easier, and this data then feed into the backend processes.

Retail is a difficult business. Fashion retail is even harder. It’s not as simple as managing the supply chain (although that’s not simple) because changing styles, seasons, and tastes are overlaid against the more traditional issues of sizes and stock. Any one poor decision can destroy the profit of a fashion retailer for a particular period, and therefore making the right decisions is at a premium. Stitch Fix attacks this challenge with human capital. Said a different way, this is not your typical management team for a fashion retailer. The leader of Operations at Stitch Fix comes from Walmart.com, while the analytics leader was previously an executive at Netflix. In a sense, Stitch Fix is building a supply chain and data analytics company that happens to focus on fashion. Not the other way around.

The company is making the bet that better customer insight will resolve many of the common fashion retailer issues: returns (ensuring fewer returns), inventory (predicting what people will want), and higher inventory turns (stocking things that customers will buy in the near-term). While Stitch Fix may not succeed as a retailer (although we think it will), it is laying the groundwork for the architecture of a retailer in the Data era.

Ms. Lake makes it clear that the company is first and foremost a retailer, but a retailer with a unique business model incorporating data and technology. Lake says, “We are a retailer. We just use data to be better at the core functions of retail. It’s hard to buy inventory accurately without knowing your customer, so we use data in the sourcing process as well.” She cites the example of looking at not just basic sizes (S, M, L or 2, 4, 6) as most buyers would, but looking at the detail of inseam size too. They can use this level of granularity in the buying process because of data. This attention to detail leads to a better fit for their clients and a higher likelihood those clients
will buy.

Most data leveraged by Stitch Fix is generated by the company. Their advantage comes from the large amount of what Lake calls explicit data, which is direct feedback from clients on every fix. That’s specific, unique, and real-time feedback that can be incorporated into future fixes and purchases. The buyers at Stitch Fix, responsible for stocking inventory according to new trends and feedback, love this data, as it tells them what to buy and focus on. As Lake says, “What customers buy and why, and what they don’t buy and why not, is very powerful.”

Stitch Fix has analyzed over 500 million individual data points. While the company has shipped over 100,000 fixes, no two have ever been the same. That’s personalization. The company sells 90 percent of the inventory that it buys each month at full price, again because of personalization. Data and personalization have the impact of delighting clients while revolutionizing the metrics of retail.

Zara

Zara’s business model is based on scarcity. In a store, if a shopper sees a pair of pants he likes, in his size, he knows it’s the only one that will ever be available, which drives him to purchase impulsively and with conviction. Scarcity is a powerful motivator. In 2012, Inditex (the parent company of Zara) reported total sales of $20.7 billion, with Zara representing 66 percent of total sales (or $13.6 billion), with 120 stores worldwide. Scarcity can also be a revolutionary business model and profit producer.

Amancio Ortega was born in Spain in 1936. In 1972, he founded Confecciones Goa to sell quilted bathrobes. He quickly learned the complexity of fashion, extending to retail, as he operated this supply chain of his own creating. Using sewing cooperatives, Ortega relied on thousands of local women to produce the bathrobes. This was the most cost-effective way for him to produce robes, but it came with the complexity of managing literally thousands of suppliers. This experience taught Ortega the importance of vertical integration or, said another way, the value of owning every step of the value chain. He founded Zara in 1975, with this understanding.

Zara uses data to expedite the entire process of the value chain. While it takes a typical retailer 9 to 12 months to go from concept to store shelf, Zara can do the same in approximately two weeks. This reduced timetable is accomplished through the use of data: The stores directly feed the design team with real-time behavioral data. Zara’s designers create approximately 40,000 new designs annually, from which 10,000 are selected for production. Given the range of sizes and colors, this variety of choice leads to approxi- mately 300,000 new stock keeping units (SKUs) every year.Chapter 4: Personalizing Retail and Fashion 67

Zara’s approach to the business has become known as fast fashion, as they will quickly adapt their designs to what is happening on the store floor, usher new products quickly to market, and just as swiftly move onto the next thing. This fast pace drives incredible efficiency in the implementation of the business model, yet at the same time, it creates enormous customer loyalty and intimacy, given the role of scarcity. Since the business can react so quickly, there is always sufficient capacity to produce the right design at the right time.

Zara’s system depends on the frequent sharing and exchange of data through- out the supply chain. Customers, store managers, designers, production staff, buyers, and warehouse managers are all connected by data and react accord- ingly. Data drives the business model, but it’s the reaction to the data that produces competitive advantage. Many businesses have a lot of data, but very few utilize it to rapidly effect decision making.

Unsold items account for less than 10 percent of Zara’s stock, compared with the industry average of 17 to 20 percent. This is the data in action. According to Forbes, “Zara’s success proves the theory that if a retailer can forecast demand accurately, far enough in advance, it can enable mass production under push control and lead to well managed inventories, lower markdowns, higher profitability (gross margins), and value creation for shareholders in the short- and long-term.”

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Stitch Fix and Zara each provide a glimpse into the future of retail. It’s not simply about ecommerce and automation. Instead, with the power of data, a retailer can redefine core business processes and in many cases, invent new ways of interacting with customers. This new level of intimacy changes the role that a retailer plays in a consumers life; from a sales outlet to a trusted advisor. However, knowing what needs to be done is easier than actually doing it — therein lies the challenge for all fashion designers and retailers.

This post is adapted from the book, Big Data Revolution: What farmers, doctors, and insurance agents teach us about discovering big data patterns, Wiley, 2015. Find more on the web at http://www.bigdatarevolutionbook.com