When package dimensions are missing or wrong, it doesn\u2019t just slow down label printing\u2014it throws off the entire delivery strategy.\u200b<\/p>\n\n\n\n
\u200bThis gap in weight and dimension data for shipments prevents us from being able to accurately compare carrier rates or choose the best route to the extent we\u2019d like to for clients.<\/p>\n\n\n\n
\u200bPackage Predictor fills in the gaps by learning how products are actually packaged\u2014not theoretical predictions based on how they\u2019re measured in product catalog data. \u200b<\/p>\n\n\n\n
It looks at how these items have been shipped before, both for single-SKU and multi-SKU shipments where items are combined into a single package and then uses those historical patterns to predicts the package size.\u200b<\/p>\n\n\n\n
With that info, we can now pick the best shipping method\u2014picking the best balance between speed and cost automatically. No more overpaying because a package was misclassified or choosing a slower carrier when faster was just as affordable.\u200b<\/p>\n\n\n\n
On the warehouse floor, Package Predictor cuts out repetitive work. No more needing to enter the same weights and dims data where it\u2019s missing for SKUs over and over again. \u200bPredicted dimensions flow directly into the label printing process, helping teams move faster with automation.\u200b<\/p>\n","protected":false},"featured_media":0,"template":"","product":[71,620],"class_list":["post-4515","product_update","type-product_update","status-publish","hentry","product-dropshipping","product-private-marketplaces"],"acf":[],"yoast_head":"\n