Geek&Chic: Analytics redefining fashion instincts

In recent years, advanced analytics has become a core component across a wide range of consumer-facing industries to address increasing complexity.

Digital companies—with Amazon leading the pack—are at the forefront, constantly finding new and innovative ways to apply analytics throughout the value chain, from merchandising to marketing to operations to human resources.

The apparel industry, comparatively, lags behind in embracing analytics, often favoring merchant-and designer-driven “gut feel” over insight-driven decision making. Apparel players point to several core challenges as constraints for investing in their analytics capabilities, including poor data quality, a rapidly changing assortment and competitive landscape, high SKU and logistics complexity, and limited analytics expertise within the current employee base. 

To effectively start with fashion retail analytics, players in the fashion retail sector need to first decide where analytics will help them achieve the greatest business impact. Businesses need to develop a cross-functional road map to make tough decisions on where analytics matter most. Retailers face critical challenges when it comes to discovering the limits of the data available. Available data can offer useful insights if used appropriately. This is where an appropriate data ecosystem backed by fashion retail analytics can help companies to tackle challenges such as an ever-changing assortment, rapidly changing trends, and non-standardized SKU.

A Few Insights:

• Department stores are increasingly using data to help predict the rise and decline of future trends.

• Fashion houses like Marni and Miu Miu use data analytics to identify opportunities and weaknesses in their collections.

• While data-driven tools have improved trend forecasting, human interpretation remains essential.

 “You never design by data, but the data, but the data provides a compass as you’re navigating a hunch,” says Shinola CEO Tom Lewand.

The retail eCommerce companies are now working towards imbibing analytics in their product to stand out in the market, this makes way from seller, customer to their warehouse.

Power BI, tableau, excel, SQL and creative analytical minds are in great need and it seems to be rising tremendously by 2022. The data metrics such as seller price, warehouse funnel and customer retention alongside campaign strategy plays a vital role in identifying opportunities for the company.

The right product at the right price and the right time: That’s the retail recipe. Besides trends in assortment and pricing, retailers have to stay on top of the technological forces that are shaping the customer shopping experience. One of the big trends in retail now is visual search, which is more naturally suited to how people approach their shopping experience.

Our competition is already tracking and measuring retail metrics and key performance indicators (KPIs). 

a) Retail metrics, like conversion rate, are KPIs you must start tracking + acting upon if you want to promote retail growth. 

b) Sales per square foot is another important KPI retail. This industry metric is most commonly used for retail inventory management. Sales per Square Foot = Total Net Sales ÷ Square Feet of Selling Space(usable space)

c) Year-over-year growth is a comparison of a retail metric for one period to the same period the previous year. The year-over-year growth rate calculates the percentage change during the past twelve months. Year-over-year is an important retail metric for two reasons. First, it removes the effects of seasons. Second, using year over year as a focused retail metric highlights long-term trends.

d) Customer retention is an important KPI retail metric that measures how often customers return to your business to make a purchase. 

Customer Retention Rate = ((Customers at end of the period) – (customers gained during the period)) / (number of customers you started with) *100)

e) One of the best ways to understand how customers react to your retail store is the KPI retail dwell time. This is a retail metric that measures how much time customers stay in a specific areas of your store. Among other retail store metrics, it’s grouped with customer purchasing data like hold/wait time, service time, and transaction time. This retail KPI is often represented with heat maps, highlights the areas of the store with the most traffic and how long your average customer lingers in that area of your store.

Having the right data will help us serve our customers better.

The chic market has already opened doors for the geeks to make their hands dirty with numbers and trends. The race has just started.

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