Retailers need AI Cloud to meet customer demands and innovate across their operations

Retail must become multifaceted, connected, agile, competitive— and above all—responsive to the ever-changing demands of the empowered consumer. AI Cloud is built to provide retail leaders with actionable solutions to their biggest challenges and drive success.

Discover more AI use cases in Retail

• Inventory Forecasting

  Having the right products in the right place at the right time has never been more challenging. Through automation, advanced machine learning, and deep learning algorithms, AI can minimize out-of-stock and overstock issues by forecasting, at scale, the demand required for products. Machine learning models can create forecasts for up to 1 million series in each project.

• Customer Retention

  Retention is difficult, and the estimated annual cost of churn in the United States alone is $1.6 trillion (Accenture). Across retail, businesses have relied on a variety of methods to reactivate customers who churn. But, it is difficult to identify customers who will churn ahead of time and reactivate them before it is too late. AI helps your marketers develop intimacy across your customer base by predicting which customers are likely to churn within a defined period of time. AI will inform your marketers of the top contributing factors of each customer’s likelihood of churn, allowing them to have a thorough understanding of how these relationships can be improved.

• Determining Customer Lifetime Value (CLV)

  There is an implicit assumption that a future value is associated with a customer, however, this future value can be entirely unquantified and therefore carries an uncertainty associated with decisions- such as how large of a discount to offer, and whether loyalty points expire. Although retailers may already use data-driven approaches to help identify the higher overall value of customers, machine learning in retail offers a much more granular approach that can determine decision-making at a personalized level to ensure that short term costs are balanced against the value of a long term relationship.

• Optimizing Operations

  Many environments require work schedules several weeks in advance to support regulated environments. The combination of high variability and long lead times makes it difficult for supply chain managers to make accurate decisions on how many workers they should staff. According to McKinsey, staffing needs in distribution centers can have daily variabilities of up to 50 percent.

  With AI, have the power to more accurately forecast the volume of inbound shipments required, aligning proper resource and staffing levels. By learning the historical flow of goods through your distribution centers and understanding the most influential external factors, such as holiday demand and weather, AI will accurately forecast your demand in a way that takes into account the multi-dimensionality of the real world.