Vistaprint sought to enhance its email marketing strategy by introducing a recommendation system. The goal was to predict products customers were likely to purchase while also influencing customer behavior. The company wanted the system to self-improve over time and incorporate new products seamlessly. This ambition required a solution that went beyond traditional recommendation algorithms and could dynamically adjust to customer data. A seasoned machine learning and reinforcement learning consultant was brought onboard to lead the project. The consultant designed a recommendation system architecture that leveraged Vistaprint’s customer data, including transactional, basket, browsing, and segmentation data stored in HDFS. The approach used a Markov Decision Process (MDP) framework so the system continuously learned from customer interactions and adjusted recommendations accordingly. Pseudocode and reinforcement learning knowledge sharing were provided to support engineering deployment and upskill internal analysts. The implementation marked a significant milestone for Vistaprint’s email marketing efforts. It delivered personalized product recommendations that influenced customer behavior, evidenced by noticeable incrementality in control versus targeted groups. The system adapted to new products without manual intervention, enabling a more dynamic email marketing approach. It significantly outperformed previous strategies and helped set a new standard for customer engagement at Vistaprint.

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Keurig Green Mountain’s innovation team needed to support a direct-to-consumer auto-delivery subscription service with a constant product supply. Using data from a 6-month consumer research panel, they needed to predict long-term user consumption rates with 90+% confidence. They also needed to determine the minimum number of days of data required to make that prediction. These insights would determine when deliveries should be made. The team studied the panel data through exploratory analytics to understand consumer usage patterns. They created multiple models to generate long-term predictions of user consumption and provided confidence windows that depended on the number of days of data used. They also challenged the assumption that long-term forecasts were required and showed that a more dynamic system using short-term predictions produced better results. A stochastic logistics simulation was built using the consumer panel data to validate the recommended predictive approach and parameters. The work delivered consumption prediction capabilities with 90+% confidence from the 6-month panel data. The confidence windows clarified how prediction reliability changed with the number of days of data used, enabling better delivery-timing decisions. The simulation indicated users had less than a 1% chance of being left without product under the recommended approach. Together, these outputs supported the design of an auto-delivery system based on observed usage behavior.

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Macy’s faced inefficiencies in fulfillment packaging that made it difficult to optimize box capacity for customer orders. This led to using more boxes and bags than necessary. The excess packaging increased shipping costs and risked hurting the customer experience through less efficient delivery preparation. Macy’s brought in optimization and algorithms expertise with the ability to integrate into its Java 8 environment. The expert designed a custom algorithm to determine the most efficient packaging combinations based on item dimensions, weight, and volume, along with available box and bag capacities. The work included pseudo code and a Java implementation, plus mock CSV data and test simulations to validate performance under realistic conditions. Post-simulation analysis showed a significant reduction in the number of packages required per order. This improved packaging efficiency and reduced shipping costs by using box and bag capacity more effectively. The more compact packaging also supported a better customer experience and improved sustainability outcomes through reduced packaging use.

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