Automated Intelligent Merchandising
How is mobile and tablet usage influencing web purchases and revenue?
With Medio's Common Customer Profile (CCP), you ingest data from multiple sources and begin to track and optimize multi-channel customer engagement.
How many users are shopping via multiple channels?
You can see your customers in real-time and track them between channels.
What is the profile of our best customers? What can we do to make sure we're retaining them?
Out of the box, Medio's intelligent segmentation tool, Clustomers, identifies and provides insight into your highest value customers.
How do we drive more revenue from users who aren't currently monetizing well?
Use Medio's Clustomers to segment your non-monetizing users and serve them an offer to help increase their life-time value.
How can I effectively use product recommendations to drive revenue growth in the mobile channel?
Use Medio's intelligent recommendation system to maximize the return on each digital interaction.
- Using Medio’s real-time Data Collection Service (DCS), user data, tagged events and rich mobile attributes were automatically captured.
- Traditional web based ecommerce data and catalogue data were combined with the rich mobile data in Medio’s single repository for all user activity, Common Customer Profile (CCP). There, it was used to identify and reconcile activity across devices and channels, providing a 360° view of their customers.
- Medio’s intelligent segmentation tool, Clustomers, automatically analyzed this customer data and segmented their customers in real-time into the following retail predictive segments:
- Frequency and Monetization: segments based users’ frequency of visit and levels of monetization
- User Propensity: segments by Propensity to return, Propensity to Purchase,
- Shopping Behavior: Users with Specific Categories of Interest, Big Basket Shoppers, …
- Channel: Cross-Channel Users, Single-Channel Users, …
- Demographic: segments by Age, Gender, Home Address, …
- All of the user interactions (views, clicks and conversations) with products in their catalog are automatically loaded into the graph model used by the Medio Recommendation Service. Unlike traditional recommendation engines, the graph model enabled flexibility to store and weight a variety of correlations between users, items, and actions. These complex interrelationships are analyzed in real-time to serve up personalized recommendations.