Collect the dots, Connect the dots, Predict the next dot Optimize with PureEngage G-NINE Analytics

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2 Collect the dots, Connect the dots, Predict the next dot Optimize with PureEngage G-NINE Analytics Graeme Provan, Global Director of Solution Strategy - Analytics Rob Blane, EMEA Solutions Architect

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4 Why

5 Why should I consider adaptive learning? 73% of data is left unused by companies for business insights and decision-making #1 trend in next 5 years is analytics driven personalization 200% Increase in advanced machine learning inquiry calls between 2015 and 2016 Forrester Dimension Data Gartner Dimension Data 2017, Global Contact Center Benchmarking Report Top 10 Strategic Technology Trends for 2017:AI and Advanced Machine Learning The Forrester Wave 2016, Customer Analytics Solutions

6 Connect the dots

7 Adaptive Learning or Machine Learning is a type of data analysis technology thatextracts knowledge without being explicitly programmed to do so

8 What

9 Business Challenge Your Customer Customer To Agent Best Agent Will this agent give you the BEST Business Outcome?

10 What if you could PREDICT THE BEST CUSTOMER AGENT MATCH TO NPS, FCR AND REVENUE AHT AND TRANSFERS CREATE EXCEPTIONAL CUSTOMER OUTCOMES CONTINUOUSLY GAIN KNOWLEDGE FROM YOUR DATA

11 What is Predictive Matching? VIP Customers Sarah ALL AGENTS Billing BUSINESS RULES FILTER SKILLS FILTER Sales PREDICT OUTCOMES Leverage omnichannel data and adaptive learning to pick the most qualified agent and deliver optimal business outcomes BEST MATCH Julianne Score = 10 Marcus Score = 4

12 How

13 USE CASE: Move the needle on NPS Increase Net Promotor Score (NPS) with Predictive Matching Improve customer satisfaction Increase agent efficiency Decrease costs

14 Use Case: Increase Revenue Leverage omnichannel contact center and third party data to optimize outcomes Predict agent best suited to drive sales and increase revenue Optimize existing resources

15 Use Case: Improve Collections Leverage omnichannel contact center and third party data to optimize outcomes Predict collections agent best suited to work with the customer to setup a payment plan and reduce credit Optimize existing resources

16 Use Case: Reduce AHT and increase FCR Leverage omnichannel contact center and survey data to optimize outcome Predict agent best suited to reduce handle time while improving First Contact Resolution Improve customer satisfaction and meet service level commitments Optimize existing resources

17 USE CASE:OPTIMIZE BY OUTSOURCER What if you could deliver the right interaction to the right outsourcer Closely align your interaction distribution with business performance Improve customer satisfaction and meet service level commitments Operate within your existing contracts

18 When

19 How do I select my Use Case? Business needs to identify metrics that need to meet goals Data should be available for past performance There should be an agent pool available to model Success criteria should be measurable HANDOVER TO ROB

20 What Does it Take? Data Collection Analysis Testing Use Case Selection Data Collection Data Preparation Value Assessment Business Drivers Business Sponsors NPS, CSAT, Churn, Sales Identification Sourcing Assessing Record Linking Data Cleansing Inter Agent Variance Interaction Context Variance Feature Generation Feature Selection Quality Assessment Impact Assessment Start Simply Domain Insight Automated Methods Standard Measures AUC RMSE Simulation Production A/B Testing

21 Data Collection and Preparation Skill NPS Location Agent Priority Interaction Intention Campaign Predictive Model Segment Customer Location Credit Score NPS Outcome Revenue FCR

22 Model Training and AB Test 01 Source Data Eliminate Collect Validate 02 Relevant Data Context Features Action Features 03 Train Model 04 A/B Test Model Analysis Time Slicing Agent Interleaving

23 ARCHITECTURE Media Gateway Predictive Matching Engine Agent State Connector Routing Strategies SIP Server IVR Customer URS/ORS Data + Context Routing Strategies Stat Server Agent State Connector Business Analyst Predictive Matching Engine Existing Predictive Matching

24 Demo

25 Summary 1 Predictive Matching provides the finest grain matching between customer and agent to optimize business KPIs Use case definition is the most important step in deployment Agent, Customer, Interaction and Outcome data is input for model training Predictive Matching works with existing Genesys routing to predict the next dot 5 A/B Testing is built into the Predictive Matching application

26 Visit the Analytics Demo Booth to see Genesys Predictive Matching first hand

27 Graeme Provan Global Director for Solution Strategy Analytics and Predictive Engagement phone: Rob Blane EMEA Solutions Architect phone: Where to get more information. Today Here at CX17! Come and see us at the Business Optimization Analytics booth! We will be happy to take you through our products or discuss employee engagement strategies. When you are back at the office Product whitepapers Omnichannel Customer Engagement Playbook Customer case studies Speak to Genesys staff, we are here to help