Delivering Customer Insights & Experience in the Shipping Industry

Objective:

• Develop customer analytics, inclusive of purchase behaviors, store vs. eCom engagement and channel
• Demonstrate the scalability and elasticity of MS Azure cloud services for data and analytics

Approach:

• Employ customer transaction data, customer payment data and associated customer identities captured in interactions, to isolate a business ontology of the customer data domain
• Adopt the Cross-Industry Standard Process for Data Mining (CRISP-DM) for navigating through the use-case analysis – thus defining specific touchpoints for reviews and feedback
• Migrate the client’s customer data assets to MS Azure data science virtual machine (DSVM) instance, employing Azure SQL for data management and Azure ML services for advanced analytics

Insights:

• A majority of transactions contained PII, which allowed us to reduce the number of customer IDs
• Predictability of incremental revenue increase as a result of addressing the top 20% and 80% of customers, respectively
• Clustering on RFM and NADR features yielded a Customer Segmentation that indirectly differentiated customers by other characteristics that were not exposed to the algorithm – and were inferred through profiling the segments
• Among customers who made 2+ purchases in the first half of the month, Recency and Frequency features explained most of the variance in their likelihood to return in the second half of the month

Impacts:

• Established customer identification as a critical step in the client’s data and analytics journey
• Provided a roadmap for operationalizing the customer identification methodology developed as part of this POC, and leveraging the same to develop the client’s customer analytics capabilities
• Defined an add-on initiative that further scaled learnings from this POC and advanced the client’s operationalization of advanced analytics