Retail inventory management data can prevent stores from selling out of products too quickly or having too many items that are slow to sell or may never clear from shelves.
It’s increasingly common for retailers to use big data to uncover insights they may otherwise miss about their inventory. Here are some examples of what’s possible.
Making decisions about new inventory
It’s common for retailers to look at various data sources and crunch the numbers to decide when to release new items. However, the Chinese fast-fashion brand Shein takes that concept to a whole new level.
It developed an in-house algorithm that allows digging through search engines and competitors’ sites to discover trending products. Shein representatives then use those results to decide what to produce and add to inventory. This approach reportedly allows the company to introduce up to 500 products per day. Some other fashion brands only expand their offerings that much per week or less.
Retailers can also look at societal trends, weather patterns or other specifics to decide when and why they should add more inventory. Cultural norms could also play a role, particularly in certain regions. The beauty of big data is that it can process information much faster than humans could without help. That means retailers can learn things that help them stay profitable in a challenging marketplace.
Stocking clothes that fit more people
Inventory management data can also be useful for helping retailers offer clothes that are more likely to fit the largest number of people. Individuals have various body types. However, big data could narrow down the apparel characteristics that will be the most flattering to potential customers. That lessens the likelihood of retailers putting things on clearance or taking other actions that may help them sell but cut into potential profits.
A McKinsey & Co. report about using data and analytics in the fashion and luxury industries suggested that companies could achieve a 10%-30% reduction in returns by using data analysis for fit prediction. A possible big data source could be the customer reviews that ask people to indicate how true to size the clothes were or how satisfied the purchasers were with the overall fit.
However, it’s also important to anticipate the overall demand for certain styles and products. Using a combination of big data and predictive analytics could help retail decision-makers ensure they have garments that fit people well and are most likely to be in demand for the current or upcoming season.
Unlocking local store trend data
Most retailers remain concerned with inventory turnover rates. They measure how frequently a store sells and replaces its entire stock within a certain period. This metric reveals efficiency with buying and selling products. A faster ratio is not always a good thing. It could mean that the retailer’s team miscalculated how much stock they’d need, meaning the store sold out too quickly.
Something to keep in mind when figuring out inventory turnover statistics is that sales can vary at stores in particular regions, states or communities. That variation is one of the reasons why H&M uses big data to learn about customer preferences at the local level. It then applied some of those insights to inventory management.
The team looked at the shopping preferences of female customers frequenting an H&M store in Stockholm. They discovered those consumers typically like fashion-focused pieces, such as floral skirts. They also gravitated toward higher-priced items. The data also indicated that adding a coffee shop to that location would be a profitable move. Giving people a place to enjoy beverages during a shopping break might encourage them to buy certain things before leaving.
Tackling overproduction issues
People concerned about sustainable practices may worry about what happens when retailers have more clothes than they can sell. Several years ago, many brands were in the spotlight for their practice of discarding unsellable clothes. Some consumers asserted that this approach was far too wasteful and could not continue. Accurate inventory management data could help curb this issue.
A company called Sustalytics provides a data-based platform that brands can use to predict what customers will want to buy during upcoming seasons. Those insights should reduce overproduction and help company representatives focus on making and selling the products that catch customers’ interest the most.
The company extracts data from customer surveys. The information tells apparel designers and others in the fashion industry what appeals to them about certain brands. The people who receive this info can then make stock allocation decisions or modify their designs accordingly.
Sustalytics also provides its clients with trend reports about everything from the top bra styles to the most in-demand activewear pieces. Using big data that way means retailers and others in the fashion business have the best chances of narrowing down what people will be the most eager to buy.
Accurate inventory management data supports success
These examples show how reliable inventory management data is critical for having the products people want and not dealing with overstock issues. Big data algorithms also help because they can reveal new details and avoid making costly mistakes.
