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Using AI and ML Solutions for Proactive Customer Retention.pptx

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Using AI and ML Solutions for Proactive Customer Retention.pptx

  1. 1. Using AI and ML Solutions for Proactive Customer Retention Vishwanath Gubba Director, AI Center of Excellence Voziq AI NOV 2022
  2. 2. Problem: 30% To 50% Customers Who Call to Cancel Are Not Savable, Even by Best Agents Most retention programs fail to surface attrition risk early enough to allow meaningful actions, resulting in lost customers and/or expensive offers 2 “Mentioned competitor” “Complained about service issues” “Inquired about contract” “Called 3 times for same issue” “Competitive Geo, Low Usage” Called to cancel Nearing End-of-Term Missed payments Traditional Retention Blindspot Customers don’t decide to cancel overnight. Multiple experiences, customer care interactions, and better competitor offers all contribute to the risk of losing a customer.
  3. 3. Enrichments & Predictions Insights & Actions Data Integration & Pipelines AI Center of Excellence (ACE) Solution: Enable Existing Customer Communication Channels With AI Technology by Bringing AI Platform, AI Implementation Knowledge, and Home Security Industry Knowledge Email Campaigns Field Service Proactive Outreach App Offers Web Offers IVR Call Routing Call Center Agents SMS Campaigns AI-Enabled Multichannel Interactions 3 Built and Perfected Models using Millions of Active and Cancelled Customers
  4. 4. Multi-Model Approach to Drive Significant Increase in Customer Lifetime Value (CLV) Care Grow Retain Winback Calculate NPS for every customer and differentiate Promoters from Detractors Use CLV to identify targets for Upgrades, Add-Ons, Referrals Use predicted risk to proactively intercept high-risk customers & their extend lifetime Use customer history to identify top targets for outbound win- back campaigns VALUE LIFETIME Lift in CLV due to AI/ML strategies Benefits Better visibility into top at-risk customers and actionable drivers of risk Millions of AI-enabled actions through emails, calls and website/mobile app before it’s too late to act 4 More profitable retention driven by AI-optimized, personalized offers 1 2 3
  5. 5. 5 Customer Life Cycle Alarms Retention Care Moves Onboarding Bad Debt Others Winback Millions of call center agent notes (human- interpreted data) about risks and opportunities Powerful Interaction Analytics to Uncover Customer Frustrations That Pose Revenue Risk Customer very upset about monthly payments going up. Emotional about not getting same quality of service. Asked about contract end date. customer called in about price match option with new customer offer on website. Wants to switch to basic plan. Complained about not making offers available to long term customers Customer frustrated about unresolved issue. Complained about tech behavior. Wants to cancel and go back to previous provider Terminix for better service. Cancellation Intent Sentiment Behaviors Price Competitor Dissatisfaction Contract
  6. 6. Natural language processing (NLP) implemented to extract relevant information for attrition & CSAT analysis, and used as input to predictive models 6 Pest Names Competitor Names Text Categories (Tags) Product Names
  7. 7. Achieving Significantly Better Predictions and Business Impact Higher model accuracy Addressable attrition drivers Explainable AI recommendations Significantly lower-cost retention Sophisticated feature engineering ▪ NLP for significantly more powerful diagnosis and predictions (e.g. identification of customer intent, pests discussed, hot topics, sentiment) ▪ VOZIQ data enrichments (e.g. unified contact record, event sequencing, customer intent and effort identification through call and service history) Additional data sources ▪ Incorporated and tested external data from IRS and Census (e.g. household income, and house prices by zip code for model performance and explanations) Operational all-in-one solution ▪ Prescriptive offers and NPV calculations to ensure profitable retention ▪ Followed industry best-practices for AI driven retention solution with all-in- one implementation of predictive models, prescriptive offers, agent guidance and value calculation to ensure best possible results Best-practice ML model design ▪ Focus on early detection and addressable churn for effective results through NPS and customer experience analytics (care calls) ▪ Refined modeling population Non-pay/late-pay customers excluded to avoid overlapping with collections workstream 7
  8. 8. Data Transformation and Feature Engineering Highlights This gives a structure to unstructured voice of customer data and helps in creating actionable features Natural language processing (NLP) ▪ Extract Themes and entities (e.g. Hot Topics like ‘reservice’ or Pests like ‘Ants,’ ‘Spiders’ and Competitors like ‘Terminix’) ▪ Keyword based Categorization to tag agent notes to pre-defined topics (‘Billing Issues’ or ‘Relocations’) ▪ Bag of words techniques like count vectorizer and TF-IDF (Frequency and Impact) Helps in mapping sequence of events from disparate data sources Time series transformations ▪ Transformed time-series information for better modeling (e.g. dates transformed relative to end of contract (in/out contract, and for how long) ▪ CX event proximity (e.g. Reservice, Initial Service dates transformed to count of occurrences in last 90, 180, 365, 540 days to identify impact) Helps in developing targeted offers for customer demographics External data sources ▪ Home and ZIP Code-level Modeling to identify home and location specific differences (e.g. high/low attrition zip code modeling to catch customer behavior differences or competitor activity) ▪ IRS and Census Data Integration for explainable predictions (e.g. Household Income Level/ Age/ Home Ownership) Other transformations ▪ Outliers and Missing value imputations ▪ Handling correlated features Standardizes continuous variables and bins them 8
  9. 9. Automating discovery of the most profitable offers for each customer, based on risk-based customer micro-segments and weighted NPV 9 Risk Score: 71 Risk Reason: Equipment Trouble NPS Risk: MEDIUM Measurement and Iterative Improvements VOZIQ Churn Prediction Model Customer Micro- Segments Offer Bank Offer NPV Calculation Offer Recommendation Engine Customer Similarity Model Offer Optimization AI Continuous Improvement: Next best offers are made available in the agent guidance app and acceptance outcomes are captured for continuous improvement 6 Customer microsegments: Automatically put every customer in a microsegment based on risk and risk drivers 1 Customer Similarity Model: Automatically identify customers who are similar to those who’ve accepted particular offers 2 Offer Bank: Use risk drivers and microsegments to define and prioritize offers for auto-recommendation 3 Offer NPV Calculation: Automatically calculate financial impact of every offer in terms of a weighted NPV 4 Offer Recommendation Engine: It automatically recommends the most targeted and profitable offers for every customer 5 Built and Perfected the Solution Over 10 Years and Trained the Models using 10+ Million Recurring Revenue Business Customers
  10. 10. AI-Driven Call Routing AI-Driven Agent Guidance CUSTOMER AI ENABLED CONCIERGE SERVICE SPECIALISTS Risk scores and micro-segment 1 Proactive offers for each customer, and offer NPV 2 Health indicators that identify cancel drivers 3 Relevant home pest info from area 4 A.I. IVR 10 Millions of AI-Enabled Interactions to Maximize Customer Lifetime Value (CLV) on EVERY Call Predictively route every customer call to the best agent who is empowered with guidance and offer prescriptions 1 2 3 4

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