• Scale: Algorithmic retail establishes a common foundation from which all business units can build AI applications, share data and cross-leverage outputs.
• Value: Working from a common platform allows the organization to maximize ROI in the form of improved organizational agility, cross-function collaboration, operational efficiency and cost savings.
• Speed: Working from a common platform greatly improves the pace at which the organization can build and productionize models, while also allowing increased experimentation.
• Collaboration: Apps built on the platform can be integrated, which can help improve overall visibility within the organization and across the global network.
• Efficiency and cost savings: The cross-function platform eliminates the need for each business unit to duplicate initial project phases, while also streamlining management, maintenance and operations.

While many retailers are accustomed to using transactional data, it is less common for them to use contextual and customer data to inform business processes. Combining these elements greatly enhances the relevance and accuracy of the model.

Case Study: Demand Forecasting

Publicis Sapient worked with one organization to improve the accuracy of their demand forecasting process by incorporating cross-function and cross-channel data. Traditionally calculated at the showroom level with sales history, we added browsing and clickstream data based on IP location, which improved overall precision and accuracy, leading to:

• Reduced supply chain costs through enhanced visibility into variables, such as color choice and model features
• Reduced inventory and manufacturing costs by responding to actual customer demand, as opposed to the previous year’s performance
• Increased conversion as customers were more likely to find the exact product they were looking for
  

Retailers can employ these same principles, particularly if they have a significant e-commerce platform. Even modest improvements in demand forecasting accuracy could present significant cost reductions. One model we produced showed that if a $10B retailer improved demand forecasting accuracy by as little as .25-.75 percent, they could realize a savings opportunity of $60M annually.

For most retailers, e-commerce is the fastest growing sales channel. It is also the least profitable. A big reason for that is a high rate of returns, with digital customers returning items up to three times as often as brick-and-mortar shoppers.  

In addition to creating both inbound and outbound shipping costs, this negatively impacts multiple areas of the business, including mismanaged seasonal inventory, out-of-stocks, overstocks, refurbishment and repackaging—not to mention time. It takes 30 days to get a returned item reconditioned and ready to be resold.

An enterprise-wide approach to AI holds tremendous power to help realize this change, as models can take data from various points in the customer journey as well as cross-functional business metrics to predict return rates on various products, as well as for individual customers. In this way, organizations can address the risk of returns at the right point in the customer journey, either by offering incentives or deterrents to high-return customers, or by identifying needed updates to product descriptions or photos.