Amperity: A Different Approach to Customer Data
Because the typical methods just don’t cut it.
Blog
Keynote: Adapting to Consumer 3.0
Jackson Jeyanayagam, Chief Marketing Officer, Boxed. com discusses how his brand understands and communicates with its clients, and how treating employees well can actually be a brand-building strategy.
Panel: What Drives Loyalty and How Much Effect Do We Have On It?
Andrew Cochrane (CCO, SBTech), Francesco Hugony (Online Gaming CRM Strategy, Sisal), Rinat Halifi (Head of CRM, Fiverr), and Daniel Wiesenfeld (Sr. Director, Customer Analytics, J.Jill) discuss driving loyalty.
Keynote: Innately Intelligent Marketing – From Concept to Reality
Pini Yakuel, CEO and Founder of Optimove, discusses the past year, the current state of affairs, and looks into the future, taking innately intelligent marketing from concept to reality.
How Curology Delivers High-performing, Personalized Ad Campaigns
Learn how Curology uses Personas to build more targeted campaigns and decrease their wasted ad spend by more than 10% while maintaining the same returns.
How Frame.io Delivers a Seamless Customer Experience with Personas
Learn how Frame.io uses Segment’s newest product, Personas, to reduce campaign coordination time by 50% and time to resolve complex support tickets by 80%.
Engage Time Optimization
Send emails or other outbound messages at optimal time for each customer. Process: Execute model based on CDP data; store result on profile for use in CDP or other systems.
Loyalty Incentive Selection
Select loyalty incentive using model based on CDP data. . Process: Execute model based on CDP data; store result on profile for use in CDP or other systems
Unsubscribe Prediction
Estimate likelihood of unsubscription using model based on CDP data. . Process: Execute model based on CDP data; store result on profile for use in CDP or other systems
Value Prediction
Estimate future value of customer using model based on CDP data. . Process: Execute model based on CDP data; store result on profile for use in CDP or other systems
Churn Prediction
Estimate likelihood of customer churn using model based on CDP data. . Process: Execute model based on CDP data; store result on profile for use in CDP or other systems
Real Time Location-based Personalization
Select best message in real time based on current customer location and past data. Process: Receive real time stream of visitor locations and IDs from channel system; use location to look up relevant information (e.g. local weather); use ID to find visitor’s profile in the CDP; use location plus profile to infer customer context (e.g. work, home, vacation); apply rules or predictive models to select best message using all data; send to channel system for delivery; track behavior during session and make new adjusted selections.