Creates unified customer profiles by stitching together data from multiple sources in real-time, providing a comprehensive view of each customer across all touchpoints.
A standardized, extensible schema framework that normalizes data from diverse sources into a common structure, ensuring consistency across the entire platform.
A visual interface for creating customer segments using drag-and-drop logic, combining demographic, behavioral, and predictive attributes to define target audiences.
Pre-built AI/ML models for common marketing use cases like customer lifetime value prediction, churn risk scoring, and marketing attribution analysis.
Comprehensive tools for managing data usage policies, tracking consent, and ensuring compliance with regulations like GDPR and CCPA across all customer data.
A SQL-based interface that allows data analysts to query customer data directly within AEP, enabling advanced analysis and data science workflows.
Marketing teams use AEP to create unified customer profiles from website visits, mobile app usage, email interactions, and in-store purchases. These profiles enable the delivery of consistent, personalized messages across all channels—showing customers relevant product recommendations on the website, sending targeted email offers based on recent browsing behavior, and displaying customized content in mobile apps. The result is increased engagement and conversion rates through coordinated customer experiences.
Analysts leverage AEP's journey orchestration capabilities to map complete customer paths across touchpoints and identify drop-off points. By analyzing these journeys, teams can optimize the sequence and timing of interactions, test different pathways, and automate next-best-action recommendations. This helps reduce customer churn, improve conversion funnels, and allocate marketing resources more effectively based on actual customer behavior patterns.
Data science teams use AEP's built-in AI services to generate predictive scores for customer lifetime value, churn risk, and purchase propensity. These scores are automatically added to customer profiles and can be used in segmentation rules to identify high-value customers for premium treatment or at-risk customers for retention campaigns. This enables proactive rather than reactive customer management based on data-driven predictions.
Enterprises in financial services, healthcare, and other regulated sectors use AEP's data governance framework to manage customer consent and enforce data usage policies. The platform tracks consent changes, applies usage restrictions automatically, and provides audit trails for compliance reporting. This allows organizations to leverage customer data for personalization while maintaining strict adherence to privacy regulations.
E-commerce companies implement AEP to power real-time website and mobile app personalization. As customers browse, the platform updates their profiles with current session data and immediately applies segmentation rules to determine which content, offers, or product recommendations to display. This creates dynamic shopping experiences that adapt to each customer's immediate interests and behavior, increasing average order value and reducing cart abandonment.
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15Five operates in the people analytics and employee experience space, where platforms aggregate HR and feedback data to give organizations insight into their workforce. These tools typically support engagement surveys, performance or goal tracking, and dashboards that help leaders interpret trends. They are intended to augment HR and management decisions, not to replace professional judgment or context. For specific information about 15Five's metrics, integrations, and privacy safeguards, you should refer to the vendor resources published at https://www.15five.com.
20-20 Technologies is a comprehensive interior design and space planning software platform primarily serving kitchen and bath designers, furniture retailers, and interior design professionals. The company provides specialized tools for creating detailed 3D visualizations, generating accurate quotes, managing projects, and streamlining the entire design-to-sales workflow. Their software enables designers to create photorealistic renderings, produce precise floor plans, and automatically generate material lists and pricing. The platform integrates with manufacturer catalogs, allowing users to access up-to-date product information and specifications. 20-20 Technologies focuses on bridging the gap between design creativity and practical business needs, helping professionals present compelling visual proposals while maintaining accurate costing and project management. The software is particularly strong in the kitchen and bath industry, where precision measurements and material specifications are critical. Users range from independent designers to large retail chains and manufacturing companies seeking to improve their design presentation capabilities and sales processes.
3D Generative Adversarial Network (3D-GAN) is a pioneering research project and framework for generating three-dimensional objects using Generative Adversarial Networks. Developed primarily in academia, it represents a significant advancement in unsupervised learning for 3D data synthesis. The tool learns to create volumetric 3D models from 2D image datasets, enabling the generation of novel, realistic 3D shapes such as furniture, vehicles, and basic structures without explicit 3D supervision. It is used by researchers, computer vision scientists, and developers exploring 3D content creation, synthetic data generation for robotics and autonomous systems, and advancements in geometric deep learning. The project demonstrates how adversarial training can be applied to 3D convolutional networks, producing high-quality voxel-based outputs. It serves as a foundational reference implementation for subsequent work in 3D generative AI, often cited in papers exploring 3D shape completion, single-view reconstruction, and neural scene representation. While not a commercial product with a polished UI, it provides code and models for the research community to build upon.
