VOZIQ AI is building an AI-powered business solution to help recurring-revenue businesses retain more customers profitably and proactively.

1. Using AI and ML Solutions for
Proactive Customer Retention
Vishwanath Gubba
Director, AI Center of Excellence
Voziq AI
NOV 2022

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
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“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. 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
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Built and Perfected Models using Millions of Active and Cancelled Customers

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
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More profitable retention driven by AI-optimized, personalized offers
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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. Natural language processing (NLP) implemented to extract relevant information for attrition & CSAT
analysis, and used as input to predictive models
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Pest Names
Competitor Names Text Categories (Tags)
Product Names

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
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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
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9. Automating discovery of the most profitable offers for each customer,
based on risk-based customer micro-segments and weighted NPV
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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
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Customer microsegments: Automatically put every customer
in a microsegment based on risk and risk drivers
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Customer Similarity Model: Automatically identify customers
who are similar to those who’ve accepted particular offers
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Offer Bank: Use risk drivers and microsegments to define and
prioritize offers for auto-recommendation
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Offer NPV Calculation: Automatically calculate financial
impact of every offer in terms of a weighted NPV
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Offer Recommendation Engine: It automatically recommends
the most targeted and profitable offers for every customer
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Built and Perfected the Solution Over 10 Years and Trained the Models using 10+ Million Recurring Revenue Business Customers

10. AI-Driven
Call Routing
AI-Driven
Agent Guidance
CUSTOMER AI ENABLED CONCIERGE
SERVICE SPECIALISTS
Risk scores and micro-segment
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Proactive offers for each customer, and offer NPV
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Health indicators that identify cancel drivers
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Relevant home pest info from area
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A.I.
IVR
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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
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