1. © 2015 TM Forum | 1 Machine Learning Optimised Omnichannel (MLOOc) Professor Paul Morrissey

2. © 2015 TM Forum | 2 Who We Are  Participants  Ericsson  Knowesis  Liverpool John Moores University  MICTA  Sutherland Labs  Champions  Orange  AT&T  Globe Telecom

3. © 2015 TM Forum | 3 Customer Centricity & Omni-channel engagement Why?  Everything becoming a commodity to the consumer  Multi service domain  Cross Vertical Offerings  Collabitition  Customer choice overtaking loyalty  Digital Darwinism! “Evolve or Die!”

4. © 2015 TM Forum | 4 Introducing the ‘Interaction operational Centre’ (IoC) How?  Single point of customer interface for multiple services  Optimize interaction interface channel based on user preference  Application of machine learning techniques  Self learning knowledge base

5. © 2015 TM Forum | 5 The Interaction operations Centre (IoC) MLOOc  Customer effort minimization  Optimization of journey mapping  Introducing the human avatar  Learn from interaction behavioral based attributes  Customer preference based dynamic journey entrance and exit points  Minimize CES maximize NPS & CSI

6. © 2015 TM Forum | 6 Machine Learning MLOOc  The Neocortex story  Evidence based decision logic  Supervised and unsupervised learning models  Lean Learning Modelling:  Customer validated learning  Innovation accounting  MVP Modeling

7. © 2015 TM Forum | 7 Machine Learning MLOOc  The machine learning is continuous and based on multiple sources of data  Continuous model performance monitoring is done  Fall in performance leads to model(s) being re-built, therefore the model(s) continuously updated if required  Clustering is used to extract features and groupings from unlabelled data related to customers profile – to extract insights from the data  Used to track changes in customers profiles such as:  New emerging clusters  Customer drift from one cluster to another  Classification Methods are used to predict optimal interaction patterns given the incoming user profile  Uses ensemble methods – not depending on one model  Uses customer profile information derived from multiple sources

8. © 2015 TM Forum | 8 Scenario Overview MLOOc  Optimization of Interaction – predict the most effective interaction mechanism for a given user  Learning: extrapolate across all existing users to predict for new users  Inputs: Customer profile information – changes with use-case/vertical/region etc., survey pattern success data  Output: Suggested survey pattern to use with the new user  Optimization of Offer Delivery – predict the most effective channel and time of day for offer delivery  Learning:  Input: Customer profile with offer lookup and offer acceptance data  Output: Suggested Channel and Time of Day to initiate offer  Behavioural Change Detection – detect negative changes in users exercise patterns and choose appropriate escalation mechanism  Learning:  Customer profile, escalation effectiveness (with current escalation profile) pairs  Current effectiveness gives us a confidence measure for the pairing  Output: Prediction of most effective escalation profile for the new user

9. © 2015 TM Forum | 9 The Architecture MLOOc

10. © 2015 TM Forum | 10 The Benefits MLOOc  Customer  Customer experiences a seamless service model with a Trusted multi-service provider.  Single point of contact to resolve issues decreases Customer Effort.  Machine learning improve experience when interacting with the service provider  Reduction of nuisance aspect of interactions and lead to more first time positive outcomes  Service Provider  Increase revenue through expansion of services provided to the customer.  Own more of the customer’s digital home wallet.  Reducing Costs, complexity and delays of introducing new products and services into the IoC

11. © 2015 TM Forum | 11 The Next Phase MLOOc  AI, Machine Learning and Customer Centricity  The Maturity Model Metrics  Innovation Accounting Metrics  Channel Metrics  The Next Steps  Knowledge Base  The Ethics/Governance  Where Next  Vancouver TM Forum Action Week 11th/15th July 2016  TM Forum Catalysts December 2016.

12. © 2015 TM Forum | 12 Possible New Metrics in the AI/Knowledge Base Domain MLOOc  # Number of successful interactions with no human participation  # Number of interactions started via automation but handed over  % of successful interactions with no human participation  # Number of articles in the Knowledge platform  # Number of views/uses of Knowledge platform  # Number of likes of Knowledge platform  % Average likes of knowledge article