As foot traffic at shopping malls and retail outlets begins to trend upward during the holiday season, making sure inventory levels are accurate for the most popular clothing and accessory items is one of the best ways retailers can maximize profits.
Sparse shelves mean unhappy customers. Nobody likes it when they find that perfect piece of clothing, but it’s not available in their size — especially millennials, who are known for their specific spending habits.
Digital data in combination with internal information like point-of-sale (POS) data, CRM databases and customer support logs can provide powerful insight that can predict not just what sizes should be stocked, but also what designs, colors and materials are more likely to sell in a specific retail outlet. For retailers, however, these decisions are no small undertaking. There's a huge variation from region to region and even neighborhood to neighborhood.
Importance of Rightsizing Inventory
Demand is volatile, leading to forecasting accuracies that are often around 60 percent, according to commonly held industry beliefs. Due to this volatility, predicting inventory needs is challenging.
Retailers at the forefront of data analysis are employing size-pack optimization using POS and inventory data, viewing what was shipped and sold in a corresponding time frame to determine current shipping needs. Using sophisticated algorithms to process information such as search data, social media conversations, geographic location and population size, retailers have an untapped opportunity to build richer data sets and gain better insights to help predict and assign inventory based on these complex, interrelated factors.
Inventory comprises a company’s most important investment, and it’s imperative that retailers employ the technology tools and resources necessary to help take inventory planning from a best estimate with poor accuracy to a highly efficient, math-based process.
Inventory planning for the holiday season actually begins months in advance. The retail landscape is so competitive and customer-focused that companies can ill afford to get inventory wrong. Every retailer should strive for balance —too much inventory leads to inflexibility and lost opportunities to shift inventory from in-store to online, or vice versa, while too little inventory can be detrimental to sales in every channel and damaging to brand loyalty.
Often, availability can mean the difference. Consumers seeking convenience and personalization may even be willing to place cost secondary to product availability, thus improving a retailer’s opportunity to up margins.
Forecasting Demand and Refining Inventory Decision Making
Using data analytics to improve inventory management is becoming increasingly important in the global marketplace. By streamlining the supply chain and enabling real-time decisions, companies can significantly reduce costs as well as improve crucial aspects of inventory management (e.g., rightsizing). It can also help predict and forecast demand, allowing for a first-mover advantage right from the sourcing of products to in-store delivery.
For example, with the help of analytics, social media data offers retailers the opportunity to listen to natural conversations of large populations of consumers. It provides an unbiased platform to understand consumer preferences and patterns of behavior.
These social conversations can then be combined with other available sources of data to predict the sales of a product with high accuracy. Models can also be built to predict behavior, such as which consumers are “critical” and about to lapse. Leading marketing analytics firms can use data to understand the online-offline behavior of consumers, who often research online but make their final purchase in-store. This is especially true for apparel retailers, as people like to view multiple options online, but get the look and feel of the fabric or material in person before they make a purchase.
These are just some of the examples illustrating how retailers can apply the power of data analytics to influence and refine inventory. The insights gained from analysis of a company’s own data and metadata from outside sources like social media can result in more accurate and profitable decisions, from subtle changes to inventory to larger shifts in strategic assets that impact the entire organization.
Venkat Viswanathan is co-founder and chairman of LatentView, a data analytics firm.