Method, apparatus, and computer-readable medium for predicting sales volume

An apparatus for predicting sales volume, the system comprising:

• one or more processors;
• and
• one or more memories operatively coupled to at least one of the one or more processors and having instructions stored thereon that, when executed by at least one of the one or more processors, cause at least one of the one or more processors to:
• receive historical sales information corresponding to a plurality of stock keeping units (SKUs), the historical sales information including a sales volume for each SKU in the plurality of SKUs;
• group the plurality of SKUs into a plurality of sales tiers, wherein each SKU is assigned to a sales tier in the plurality of sales tiers and wherein each sales tier corresponds to a range of sales volumes;
• generate a feature vector for each SKU in the plurality of SKUs, wherein each feature vector comprises a subset of a plurality of attributes associated with each SKU;
• generate a statistical model based at least in part on the plurality of SKUs and their corresponding assigned sales tiers and feature vectors;
• and
• determine one or more projected sales tiers corresponding to one or more new SKUs based at least in part on the statistical model, wherein each projected sales tier in the one or more projected sales tiers corresponds to a range of projected sales volumes.

At least one non-transitory computer-readable medium storing computer-readable instructions that, when executed by one or more computing devices, cause at least one of the one or more computing devices to:

• receive historical sales information corresponding to a plurality of stock keeping units (SKUs), the historical sales information including a sales volume for each SKU in the plurality of SKUs;
• group the plurality of SKUs into a plurality of sales tiers, wherein each SKU is assigned to a sales tier in the plurality of sales tiers and wherein each sales tier corresponds to a range of sales volumes;
• generate a feature vector for each SKU in the plurality of SKUs, wherein each feature vector comprises a subset of a plurality of attributes associated with each SKU;
• generate a statistical model based at least in part on the plurality of SKUs and their corresponding assigned sales tiers and feature vectors;
• and
• determine one or more projected sales tiers corresponding to one or more new SKUs based at least in part on the statistical model, wherein each projected sales tier in the one or more projected sales tiers corresponds to a range of projected sales volumes.