Articoli correlati a Reducing CAC in Insurance Using AI-Driven Marketing...
Reducing CAC in Insurance Using AI-Driven Marketing Infrastructure: Shows insurers how AI-powered intent modelling, automation, and attribution systems reduce acquisition costs at scale - Brossura
Libro 7 di 14: Financial Services Marketing
Sinossi
Customer acquisition cost (CAC) is the single lever that determines whether insurance growth is profitable, scalable and defensible. If you work in insurance marketing, growth, product, data science, ops or compliance, this book gives you a practical, operational blueprint for lowering CAC without courting regulatory, underwriting or operational risk.
What this book is
This is not a manifesto or theory-first textbook. It’s a practitioner’s handbook built from years of real-world experience at the intersection of insurance, marketing and technology. Christian Strutt lays out patterns, implementation choices and trade-offs that teams actually face — then shows how to translate models, signals and tools into measurable business outcomes. The emphasis is on repeatability: AI-driven segmentation, automated channels, rigorous attribution and production-ready infrastructure that scales.
Why it matters
Insurance economics are unforgiving. Vanity metrics won’t save you—segment-level CAC versus LTV, contribution margin and payback must drive decisions. When CAC is controlled through better audiences, better channels and better measurement, growth becomes sustainable and defensible. This book shows you how to get there while preserving compliance and customer trust.
What you’ll find inside
- A clear framing of the CAC equation for insurance: the levers you can pull, constraints you must respect, and how to prioritize interventions.
- Practical data foundations: consent, deterministic identity and quality controls that prevent wasted spend and regulatory exposure.
- AI-driven segmentation: separate propensity, LTV and risk scores and combine them in decision matrices rather than building one-size-fits-all targets.
- Intent scoring and signal engineering: translate first-, second- and third-party signals into calibrated, actionable probabilities.
- Channel orchestration and automation: how to serve the right channel at the right time and turn individual wins into portfolio CAC reduction.
- Creative and offer experimentation: modular creative, factorial tests and metrics that measure downstream economics (not just starts).
- Conversion architecture: friction reduction, high-signal attribute capture, and fast handoffs to sales and partners.
- Attribution that works for insurance: practical guidance on MMM, MTA and incrementality experiments and how to reconcile them.
- Compliance-by-design: governance, model risk management and privacy controls embedded into every change.
- Scalable infrastructure patterns: CDP, feature store, MLOps, MAP and reliable pipelines that make outcomes reproducible.
- An operating model playbook: teams, SLAs, dashboards and KPIs to make experimentation repeatable and learning compound.
A 90-day, hands-on blueprint
The book concludes with a compact 90-day plan divided into three 30-day sprints: Assess & Stabilise, Test & Iterate, Scale & Harden. Each sprint includes objectives, concrete activities, owners and SLAs, success metrics, rollback triggers and compliance guardrails.
Who should read this
- Heads of Growth, Marketing, Product and Data Science at insurers and MGAs
- Marketing ops, data engineering and MLOps practitioners
- Agencies, vendors and consultants working with insurance clients
- Compliance and risk teams who need pragmatic, auditable controls
Outcomes you can expect
- A single source of truth for CAC and cohort LTV
- Measurable CAC reduction through validated experiments
- Production controls for models and data that satisfy regulators
- A repeatable operating model that converts ideas into sustained economics
Contact Christian Strutt at his financial services marketing agency www.MiltonKeynesMarketing.uk
Le informazioni nella sezione "Riassunto" possono far riferimento a edizioni diverse di questo titolo.
Risultati della ricerca per Reducing CAC in Insurance Using AI-Driven Marketing...
Foto dell'editore
Reducing CAC in Insurance Using AI-Driven Marketing Infrastructure (Paperback)
Print on DemandDa: Grand Eagle Retail, Bensenville, IL, U.S.A.
Valutazione del venditore 5 su 5 stelle
Paperback. Condizione: new. Paperback. Customer acquisition cost (CAC) is the single lever that determines whether insurance growth is profitable, scalable and defensible. If you work in insurance marketing, growth, product, data science, ops or compliance, this book gives you a practical, operational blueprint for lowering CAC without courting regulatory, underwriting or operational risk. What this book isThis is not a manifesto or theory-first textbook. It's a practitioner's handbook built from years of real-world experience at the intersection of insurance, marketing and technology. Christian Strutt lays out patterns, implementation choices and trade-offs that teams actually face - then shows how to translate models, signals and tools into measurable business outcomes. The emphasis is on repeatability: AI-driven segmentation, automated channels, rigorous attribution and production-ready infrastructure that scales. Why it mattersInsurance economics are unforgiving. Vanity metrics won't save you-segment-level CAC versus LTV, contribution margin and payback must drive decisions. When CAC is controlled through better audiences, better channels and better measurement, growth becomes sustainable and defensible. This book shows you how to get there while preserving compliance and customer trust. What you'll find inside- A clear framing of the CAC equation for insurance: the levers you can pull, constraints you must respect, and how to prioritize interventions.- Practical data foundations: consent, deterministic identity and quality controls that prevent wasted spend and regulatory exposure.- AI-driven segmentation: separate propensity, LTV and risk scores and combine them in decision matrices rather than building one-size-fits-all targets.- Intent scoring and signal engineering: translate first-, second- and third-party signals into calibrated, actionable probabilities.- Channel orchestration and automation: how to serve the right channel at the right time and turn individual wins into portfolio CAC reduction.- Creative and offer experimentation: modular creative, factorial tests and metrics that measure downstream economics (not just starts).- Conversion architecture: friction reduction, high-signal attribute capture, and fast handoffs to sales and partners.- Attribution that works for insurance: practical guidance on MMM, MTA and incrementality experiments and how to reconcile them.- Compliance-by-design: governance, model risk management and privacy controls embedded into every change.- Scalable infrastructure patterns: CDP, feature store, MLOps, MAP and reliable pipelines that make outcomes reproducible.- An operating model playbook: teams, SLAs, dashboards and KPIs to make experimentation repeatable and learning compound. A 90-day, hands-on blueprintThe book concludes with a compact 90-day plan divided into three 30-day sprints: Assess & Stabilise, Test & Iterate, Scale & Harden. Each sprint includes objectives, concrete activities, owners and SLAs, success metrics, rollback triggers and compliance guardrails. Who should read this- Heads of Growth, Marketing, Product and Data Science at insurers and MGAs- Marketing ops, data engineering and MLOps practitioners- Agencies, vendors and consultants working with insurance clients- Compliance and risk teams who need pragmatic, auditable controls Outcomes you can expect- A single source of truth for CAC and cohort LTV- Measurable CAC reduction through validated experiments- Production controls for models and data that satisfy regulators- A repeatable operating model that converts ideas into sustained economics Contact Christian Strutt at his financial services marketing agency This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Codice articolo 9798244585919
Compra nuovo
EUR 22,73
Spedizione gratuita
Spedito in U.S.A.
Spedito in U.S.A.
Quantità: 1 disponibili
Foto dell'editore
Reducing CAC in Insurance Using AI-Driven Marketing Infrastructure
Da: PBShop.store US, Wood Dale, IL, U.S.A.
Valutazione del venditore 5 su 5 stelle
PAP. Condizione: New. New Book. Shipped from UK. Established seller since 2000. Codice articolo L2-9798244585919
Compra nuovo
EUR 22,74
Spedizione gratuita
Spedito in U.S.A.
Spedito in U.S.A.
Quantità: Più di 20 disponibili
Foto dell'editore
Reducing CAC in Insurance Using AI-Driven Marketing Infrastructure: Shows insurers how AI-powered intent modelling, automation, and attribution ... costs at scale (Financial Services Marketing)
Print on DemandDa: California Books, Miami, FL, U.S.A.
Valutazione del venditore 5 su 5 stelle
Condizione: New. Print on Demand. Codice articolo I-9798244585919
Compra nuovo
EUR 22,99
Spedizione gratuita
Spedito in U.S.A.
Spedito in U.S.A.
Quantità: Più di 20 disponibili
Foto dell'editore
Reducing CAC in Insurance Using AI-Driven Marketing Infrastructure (Paperback)
Print on DemandDa: CitiRetail, Stevenage, Regno Unito
Valutazione del venditore 5 su 5 stelle
Paperback. Condizione: new. Paperback. Customer acquisition cost (CAC) is the single lever that determines whether insurance growth is profitable, scalable and defensible. If you work in insurance marketing, growth, product, data science, ops or compliance, this book gives you a practical, operational blueprint for lowering CAC without courting regulatory, underwriting or operational risk. What this book isThis is not a manifesto or theory-first textbook. It's a practitioner's handbook built from years of real-world experience at the intersection of insurance, marketing and technology. Christian Strutt lays out patterns, implementation choices and trade-offs that teams actually face - then shows how to translate models, signals and tools into measurable business outcomes. The emphasis is on repeatability: AI-driven segmentation, automated channels, rigorous attribution and production-ready infrastructure that scales. Why it mattersInsurance economics are unforgiving. Vanity metrics won't save you-segment-level CAC versus LTV, contribution margin and payback must drive decisions. When CAC is controlled through better audiences, better channels and better measurement, growth becomes sustainable and defensible. This book shows you how to get there while preserving compliance and customer trust. What you'll find inside- A clear framing of the CAC equation for insurance: the levers you can pull, constraints you must respect, and how to prioritize interventions.- Practical data foundations: consent, deterministic identity and quality controls that prevent wasted spend and regulatory exposure.- AI-driven segmentation: separate propensity, LTV and risk scores and combine them in decision matrices rather than building one-size-fits-all targets.- Intent scoring and signal engineering: translate first-, second- and third-party signals into calibrated, actionable probabilities.- Channel orchestration and automation: how to serve the right channel at the right time and turn individual wins into portfolio CAC reduction.- Creative and offer experimentation: modular creative, factorial tests and metrics that measure downstream economics (not just starts).- Conversion architecture: friction reduction, high-signal attribute capture, and fast handoffs to sales and partners.- Attribution that works for insurance: practical guidance on MMM, MTA and incrementality experiments and how to reconcile them.- Compliance-by-design: governance, model risk management and privacy controls embedded into every change.- Scalable infrastructure patterns: CDP, feature store, MLOps, MAP and reliable pipelines that make outcomes reproducible.- An operating model playbook: teams, SLAs, dashboards and KPIs to make experimentation repeatable and learning compound. A 90-day, hands-on blueprintThe book concludes with a compact 90-day plan divided into three 30-day sprints: Assess & Stabilise, Test & Iterate, Scale & Harden. Each sprint includes objectives, concrete activities, owners and SLAs, success metrics, rollback triggers and compliance guardrails. Who should read this- Heads of Growth, Marketing, Product and Data Science at insurers and MGAs- Marketing ops, data engineering and MLOps practitioners- Agencies, vendors and consultants working with insurance clients- Compliance and risk teams who need pragmatic, auditable controls Outcomes you can expect- A single source of truth for CAC and cohort LTV- Measurable CAC reduction through validated experiments- Production controls for models and data that satisfy regulators- A repeatable operating model that converts ideas into sustained economics Contact Christian Strutt at his financial services marketing agency This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Codice articolo 9798244585919
Compra nuovo
EUR 25,54
Spedizione EUR 43,71
Spedito da Regno Unito a U.S.A.
Spedito da Regno Unito a U.S.A.
Quantità: 1 disponibili