e-Commerce
Global Retail e-Commerce sales are expected to reach $6.5 trillion by 2023. To gain from this growth, eCommerce businesses will need the competitive advantage that comes from on-demand access to the most relevant and actionable insights on their category, product, suppliers, consumers, channels and end-to-end supply chain infrastructure.
Course5 Discovery, an AI-powered Augmented Analytics solution, uses Intelligent Automation, Retail and CPG-focused Knowledge Graph, Machine Learning (ML) and other AI technologies to drive effective multi-channel marketing, customer experience, dynamic pricing, and hyper-personalization decisions by empowering the e-commerce operational and executive teams with curated, relevant, actionable and humanized insights.
Use Cases
Enhanced Multichannel Marketing & Selling
- Deep integration of cross-channel campaign data
- Unified cross-channel campaign analysis
- Curated and humanized insights for marketers
- Identify high-target remarketing segments
- Channel and Consumer specific content recommendations
- Smarter management of premium inventory
- Simulation and Spend Optimization
Advanced Personalization
- Deeper integration of cross-channel interactions
- Enriched audience profiles & micro-segmentation
- ML-driven content & product recommendations
- Automated closed feedback loop
- Augmented customer experience and expedited conversion journeys
- Higher customer satisfaction and LTV
Dynamic Pricing
- Deeper integration of transactional, behavioral, PFV, and supply chain data
- Hybrid Pricing Optimization Engine (leveraging ML and Business Rules)
- Smarter demand sensing and highly responsive price adjustments
- Real-time experimentation
- Automated closed feedback loop analysis
- Configurable rules engine for 4P team
Predictive Product Recommendations
- Smarter blending of behavioral, marketing, transactional & supply chain data
- Consumer profiling & micro-segmentation
- Look-alike audience modeling
- Hybrid product recommendation engine (leveraging ML and Business Rules)
- Smarter placement of recommendations across multiple devices
- Automated closed feedback loop analysis
- Higher percentage of repeat business and profit maximization
Predictive Behavioral Modeling
- Smarter integration of structured and unstructured data
- Deeper psychographic profiles of known customers
- Spot emerging trends and predict unknown customer behavior
- Predict future interactions with the content and catalog
- Optimization of cross-device and cross-channel journeys
- Automated closed feedback loop analysis
- Better engagement, with higher conversion and CSAT
- Enable smarter sales and marketing plans
Maximizing the potential of your Customer Communities
- Deeper integration of engagement, transactional and post-purchase data
- Enable on-demand engagement and sales bots with-in digital communities
- Predict member’s intentions and facilitate resonating experience
- Predict buying signals and drive consumers to the stores
- Predict signals for lower engagement and community drop-outs
- Recommend contextual and actionable content to community members
- Bring the power of ML and AI to drive members up in the value chain
Related Resources
Our People
Sidharth Sivasailam
VP, Digital and Advanced Analytics
- Over 18 years of experience in Leadership roles across Technology, Retail, Banking and Telecom Industries
- Core areas of expertise include Product Management, Digital Strategy & Transformation and implementation of Analytics & Insights Programs
- Led the Product development and launch of several Market Leading Products across multiple geographies