The future of retail relies on the ability of retailers to harness and analyze consumer social sentiment data from diverse sources and use it to predict and influence consumer buying decisions (as well as streamline the supply chain). Retailers can use this data to drive demand, respond to customer needs, and gain consumer loyalty, according to online magazine Apparel.
Rather than merely analyzing sales, retailers can use predictive analytics to look into the future and identify patterns for crafting “effective and highly personalized customer engagement strategies,” the article notes.
Many retail and social commerce experts say that predictive analytics makes big data useful in retail environments. While descriptive analytics measure what has already happened, predictive analytics uses statistical modeling and data mining to study recent and historical data, allowing for more accurate forecasting.
For example, GPS-enabled technologies can be used to trigger features on smartphones, including targeted coupons and store maps, enabling consumers to receive instant offers and messages tailored just for them, the article notes. Retailers can leverage these technologies to gather real-time shopper data and use it to offer consumers location-based actions and promotions.
“With the consumer warming up to the idea of data as a way to expedite and personalize the shopping experience, an agile retail supply chain that is able to withstand constant readjustments dictated by consumer data becomes a necessity for omni-channel success,” according to the article.
Retailers developing predictive analytics strategies need highly accurate visibility into their inventories to know which products will move in which channels to anticipate future trends.
Retailers that integrate RFID into their supply chains have true item-level data, allowing them to develop more sophisticated merchandising, planning, and allocation systems.
“Item level RFID is an essential component to enable the supply chain visibility and inventory accuracy needed to know what’s available, where it’s located, and how to best deliver it – helping meet consumer expectations anytime, anywhere,” the article notes.
Using predictive analytics, retailers can build clear pictures of product performance so they can effectively manage purchasing, planning, assortment, storing, optimization, and discounts.
Knowing which products are underperforming or which will sell poorly at certain times of the year or in specific stores, frees up space for the products that perform better and improves inventory turnover.
Next Steps:
- Please view our on-demand webcast, “Real-Time Category Management,” where you will learn how predictive analytics for category management can help you connect with your customers, develop your brands, manage trade spending, optimize product placement decisions, and help grow your sales.
