Retailers also use our Segment Overlap Insight to pinpoint commonalities and differences among segments, which informs their engagement efforts to high-value customers and helps them suppress messaging to low-value customers.įor example, Franklin Sports compares three distinct segments of customers in BlueConic: Many retailers then use these segment insights to execute real-time, cross-channel lifecycle marketing. ‘Combining’ different segments with common characteristics allows BlueConic customers to analyze trends and behaviors in their audience. Prospective shoppers who’ve abandoned an item in their shopping cart in the last day and have a recency, frequency, and monetary value (RFM) score greater than D Repeat visitors who haven’t purchased before but have C site sessions in the last week Subscribers with engagement scores below B with renewal dates less than 30 days away Shoppers with CLV scores greater than A who’ve visited the site in the last three days Retailers can then build multi-dimensional segments based on customer attributes (collected via ecommerce platforms, other technologies, and BlueConic ‘listeners’ - more on these shortly) as well as scores, like customer lifetime value (CLV), stored in profiles.Įxamples of multi-dimensional segments retailers can develop in BlueConic include: These out-of-the-box (OOTB) connections help retailers merge online order event data with in-store purchase data and other digitally collected data (website visits, email and ad engagement, etc.), which is then stored in persistent customer profiles. Pluggable connections in BlueConic enable our retail customers to sync first-party data from their ecommerce platform (Shopify, Magento, BigCommerce, etc.) and analytics and big-data tools (Google Analytics, Decibel, Power BI, etc.) in our CDP. CDP use case #1: Connect your ecommerce platform and Google Analytics account to your customer data platform to improve reporting and multi-dimensional segmentation. Here’s how retailers can expand their distinct CDP use cases over time - and continue to accelerate business growth. They must also adjust their use case ‘roadmaps’ as business goals and conditions change. That’s the optimal starting point for companies’ CDP journeys. Evaluate the return on investment in terms of revenue generated and operational efficiencies gained. Execute those use cases following CDP implementation. Define the “minimum viable data” required for your initial customer data platform (CDP) use cases.

Online retail store use case diagram