Retail data analytics takes the guesswork out of merchandising
Retail data analytics is taking the place of educated guesswork in the industry, leading to tighter merchandising strategies and better financial performance.
Retailers are starting to make decisions based on their own data rather than relying on industry forecasts: FierceRetail explains that 26 percent of retailers with one to four locations plan to leverage data analytics by the end of 2016. As companies adopt these technologies, they will be able to implement smarter supply chain strategies that result in fewer out-of-stocks, higher profit margins and improved merchandising.
Retail supply chain decisions are traditionally based on professional demand predictions. While industry forecasters typically do a good job predicting trends, there is always a chance that they will be wrong, as consumer preferences can shift suddenly and predictions are made years in advance. When this happens, retailers can be left with too much of an item and forced to mark down merchandise dramatically. On the flip side, they might order too little and see popular items go out of stock, missing an opportunity to sell full-price goods.
In the age of omnichannel retailing, this complexity is only multiplied, as retailers must balance online, in-store and mobile demand, plan for promotional lifts and cope with shortened product life cycles.
Retail data analytics presents forecasting solution
If retailers want to improve financial performance and optimize merchandising strategies, they need to use retail data analytics to more accurately predict demand. Companies can use historical data on consumer purchasing habits to identify demand patterns, but advanced analytics tools can take this assessment even further.
For instance, Heng Xu, professor of information sciences and technology at Pennsylvania State University, explains that social media platforms provide valuable data that can be used for accurate, up-to-date trend prediction.
"We can use social media data to identify consumer engagement patterns and predict regional demand," Xu noted.
By combining in-house and social data, retailers can get a more accurate picture of upcoming merchandise demand, allowing them to order and stock optimal quantities of each SKU. Bringing external data into the mix also helps companies predict demand driven by bloggers and other social media buzz.
Analytics for cost containment
Retailers can also use analytics to minimize supply chain costs and improve pricing strategies. There are many variables related to supply chain management, including fuel prices, wages, commodity prices and credit freezes. As a result, supply chain executives often find themselves in reactive mode. For example, The Wall Street Journal reported that in early 2016, many companies are doubling down on supply chain cost management because industry trends are indicating a prolonged period of slow global growth.
In such an environment, companies need to make their existing supply chains more efficient and flexible. Fortune explains that data analytics can help retailers to this end. Supply chain data can be analyzed to find sourcing, scheduling and routing inefficiencies. This strategy can reduce supply chain costs by up to 15 percent.
Balancing data-driven insights with brand image
As with anything, there is a balancing act that must occur when retailers use analytics to drive their supply chain choices. As RSR Research Analyst Paula Rosenblum told The Wall Street Journal, "stores all start to look the same" when they're making decisions based on similar data. This may work for stores that stock a wide range of products, but specialty stores must keep brand positioning in mind when considering data-driven insights.
Nordstrom, for example, bases business decisions on data, but the retailer would not stock items that undercut its upscale brand image. Data analysis is a potent decision-making tool in retail, but it is most useful when combined with human strengths like intuition and taste.
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