Change or Fail: Apparel Retail Needs Big Data and Analytics to Survive

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Click to learn more about author Nikolay Savin.

In the last nine years, twelve thousand retail stores have closed in the US alone. And yet, 80% of apparel purchases still happen offline. It’s all about the right stores in the right locations, states Art Peck, President & CEO at Gap Inc. These new stores reflect the new reality where customers call the shots, while retailers analyze tons of data to optimize pricing, assortment, and payment to enhance customer experience.

Customer-centric Approaches for the New Era of Retail

What exactly is changing? The first US shopping mall opened its doors back in 1956. Back then the following formula worked just fine: a huge store in the mall, a lot of productive traffic, and high rent. Today customer behavior is getting different: shoppers do not want to drive to the shopping mall, pay for the parking, and deal with the hassle. They want to spend less time on buying and get exactly what they need. Otherwise, they just leave empty-handed. In fact, from 70 percent to 96 percent of global shoppers do precisely that. The formula which works best today, according to Art Peck, is a properly sized store outside the mall, low productive traffic, and low rent. And all of this should be enhanced by a digital experience.

“We are doing something super cool and game-changing. And that is bringing big data and analytics to bear on a customer, the customer experience and the service proposition in the stores,” stated Art Peck at Shoptalk in early March. The company seeks to open smaller boutiques with localized assortments based on the data about size curves, esthetic, the entire population, demographics, psychographics, etc. “We are scratching the surface. But if you are not doing it this or next year, five years from now you won’t be around,” he added.

Customers are ready to share their data with retailers. But in exchange, they expect highly personalized pricing and assortment offers, as well as better payment and delivery options. At the same time, apparel retailers have to deal with particular challenges of their industry: short collection cycles, markdown optimization, and expert-based pricing for new entries. Therefore, learning how to work with Big Data not only in terms of collecting it, but analyzing it to get actionable insights is a must for retail winners.

How to Collect Big Data Properly

Shoppers and retailers generate tons of data every second. That’s why the first thing retailers need is to collect all of that data properly. Some retail companies build in-house pricing engines, which are also responsible for data collection. However, such a system requires heavy funding, takes a long time to kick off and calls for the involvement of the IT department. What is more, it calls for constant upgrading, needs to be sophisticated enough to factor in all the challenges of the industry such as the ability to process similar or alike matches, collect data from various websites with different layouts and languages, and verify data. In the long run, such an option does not seem feasible as it adds to the expenses and can merely be not efficient enough. Another option is to hire a third-party data provider that delivers turnkey data about the market and saves IT and pricing departments hours of their working time.

Either way, retailers have to ensure that the data they get is reliable, timely and accurate. To be able to do that, they can roll out an internal data verification system, which again requires significant dollars in investment and the expertise of the IT department. Luckily, the market already offers solutions which guarantee an up to 98 percent data accuracy and provide the means to verify Data Quality right in the dashboard.

How to Use Price Analytics to Stop Selling Below Price Floors

Apparel retail has several challenges peculiar to this particular industry. Due to short collection cycles, retailers tap into high-low pricing, which is illustrated by deep promos and results in lost profit margins. Also, it is difficult to define direct competition since in most cases the assortment and audience differ significantly depending on the retailer. For that reason, when it comes to pricing a new entry, apparel pricing managers would rather rely on their intuition and past experiences than on data.

The next step when dealing with a new product is to quickly define whether it is a champion which will be sold in no time or a slow mover which needs to be pushed more aggressively through a series of promo campaigns. Often it takes months to figure out which is which. As a result, forced to clear off shelves to vacate them for the next collection, retailers can offer as much as 80 percent as a discount for a slow mover and eventually cut their margin. It is especially true for fast fashion businesses.

AI-powered price analytics solutions consider all the pricing and non-pricing parameters such as seasonality, weather, customer behavior, and business goals. Moreover, such solutions establish latent interconnections between the variables, help to group products into clusters depending on their sales potential and suggest pricing moves by weeks earlier than in a traditional scenario. The system notifies retailers about slow-moving products and offers a series of gradual discounts, which eventually helps retailers sell all the necessary items within a defined timeframe and to earn more. With AI-based technology, over 90 percent of products can be sold when necessary, while revenue and sales can grow by 10 percent and 15 percent respectively.

Apparel retail is reshaping around the growing expectations of customers rapidly. Sitting on the sidelines and watching sales drop is not an option. Especially when retail leaders are using advanced solutions to win the hearts of shoppers and eat up the market. It’s time to act and leverage the power of technology to stay relevant. Otherwise, you just won’t last long.

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