Data analytics and machine learning in retail and consumer goods

The growth of e-commerce, volatility with suppliers, and the risk of global pandemics has shocked and accelerated the demands on supply chains. Companies have found existing models and approaches to predicting demand and managing inventory insufficient for the new normal in retail. A company may have run weekly or monthly aggregate forecasts with limited data sets in the past, but competing in the era of e-commerce where consumers can easily switch stores requires that companies have the ability to predict demand for a SKU at a day and store level.

Improving the speed and accuracy of time series analyses in order to better forecast demand for products and services is critical to retailers’ success. In this notebook we discuss the importance of time series forecasting, visualize some sample time series data, then build a simple model to show the use of Facebook Prophet. Once you’re comfortable building a single model, we’ll combine Prophet with the magic of Apache Spark™ to show you how to train hundreds of models at once, allowing us to create precise forecasts for each individual product-store combination at a level of granularity rarely achieved until now.

Natural disasters, pandemics, societal unrest and other factors have all recently caused disruptions to our global supply chains. Ensuring that we have enough product to serve demand, while not carrying too much inventory is a key challenge for every business. This solution provides a modern way of helping retailers and manufacturers identify the optimal safety stock to carry to prevent business disruption while freeing working capital.

Marketers want to invest their resources in the most engaged and valuable customers. Investing in these customer generates stronger growth and higher ROI. This solution focuses on how marketers can segment consumers by lifetime and value, and help to improve decisions on product development and personalized promotions.

Retailers and direct-to-consumer brands are increasingly offering subscription services to consumers. These services provide the consumer with convenience while building a steady source of annuitized revenue. As membership in subscription models increases, keeping those customers becomes crucial to maintaining profitability. This solution offers new ways of analyzing customers to understand what factors lead to greater retention and identify when and why customers churn.

Create a personalized experience for your customers to drive engagement and monetization. Solution includes notebooks for collaborative filtering and content-based recommenders.

Create more advanced customer segments to drive better purchasing predictions based on behaviors. Using historical data from point of sales systems along with campaign information from promotions management systems, this solution helps teams derive a number of features that capture the behavior of various households with regards to promotions in order to build useful customer clusters.