Data Analytics Stack for Pet Insurance MGAs: What to Measure and How to Build It
Data Analytics Stack for Pet Insurance MGAs: What to Measure and How to Build It
You cannot manage what you cannot measure. A pet insurance MGA generates data from dozens of touchpoints marketing, quoting, policy management, claims, billing, and retention. Turning this data into actionable insights requires the right tools, the right metrics, and the right architecture.
What Is the Analytics Maturity Model for Pet Insurance MGAs?
The analytics maturity model for pet insurance MGAs has five levels, from manual spreadsheets (Level 1) through basic PAS and GA4 reporting (Level 2), intermediate BI tools with a data warehouse (Level 3), advanced full-stack automation (Level 4), to predictive ML and real-time analytics (Level 5). Most new MGAs should target Level 2 at launch and Level 3 by year 2.
1. Where Are You?
| Level | Description | Tools | Team |
|---|---|---|---|
| 1: Manual | Spreadsheets, manual reports | Excel, Google Sheets | Founder does it |
| 2: Basic | PAS reports, GA4 | PAS reporting, GA4 | Part-time analyst |
| 3: Intermediate | BI tool, basic warehouse | Metabase/Looker, BigQuery | 1 analyst |
| 4: Advanced | Full data stack, automated | Snowflake, Tableau, dbt | Data team (2–3) |
| 5: Predictive | ML models, real-time | + ML tools, real-time | Data science team |
Most new MGAs should target Level 2 at launch and Level 3 by year 2.
What Are the Most Important Metrics to Track?
The most important metrics for a pet insurance MGA are the "Big 5": loss ratio (target 55–65%), customer acquisition cost or CAC (target under $150), lifetime value or LTV (target 3x+ CAC), retention rate (target 85%+), and claims turnaround time (target under 3 days). These five metrics tell you whether your MGA is healthy, growing, and sustainable.
1. The Big 5 Metrics
| Metric | What It Tells You | Target | Frequency |
|---|---|---|---|
| Loss ratio | Claims cost / Earned premium | 55–65% | Monthly |
| CAC | Marketing spend / New policies | <$150 | Monthly |
| LTV | Lifetime revenue x margin | 3x+ CAC | Quarterly |
| Retention rate | Renewed / Up for renewal | 85%+ | Monthly |
| Claims turnaround | Average days to pay claim | <3 days | Weekly |
2. Full Metrics Dashboard
Marketing Metrics:
- Website traffic (by source)
- Quote start rate
- Quote-to-bind conversion rate
- CAC by channel
- ROAS by channel
- Brand search volume
Underwriting Metrics:
- New policy count
- Average premium
- Premium by state, breed, species
- Mix by plan tier
- Add-on attach rates
Claims Metrics:
- Claims frequency
- Average claim size
- Loss ratio (by cohort, breed, state)
- Denial rate
- Claims turnaround time
- Claims satisfaction score
Retention Metrics:
- Retention rate (by cohort)
- Churn rate and reasons
- NPS score
- Involuntary vs voluntary churn
- Renewal rate
- Revenue retention
Financial Metrics:
- Gross written premium
- Earned premium
- Commission income
- Operating expenses
- Profit margin
- Cash flow
For KPI metrics detail and benchmarks, see our comprehensive metrics guide.
What Analytics Tools Should You Use?
The analytics tools you should use depend on your MGA stage: pre-launch and launch (0–500 policies) requires only free tools like GA4, PAS reporting, and Google Sheets; growth stage (500–5,000 policies) adds Metabase or Looker Studio, BigQuery, and dbt; scale stage (5,000+ policies) upgrades to Snowflake, Tableau or Power BI, Fivetran, and data quality monitoring tools.
1. Pre-Launch and Launch (0–500 policies)
| Tool | Purpose | Cost |
|---|---|---|
| Google Analytics 4 | Website and marketing analytics | Free |
| PAS built-in reporting | Policy and premium data | Included |
| Google Sheets | Manual analysis and modeling | Free |
| Excel/Google Sheets dashboards | KPI tracking | Free |
2. Growth (500–5,000 policies)
| Tool | Purpose | Cost |
|---|---|---|
| GA4 + Google Tag Manager | Full marketing attribution | Free |
| Metabase or Looker Studio | BI dashboards | Free–$500/month |
| BigQuery or PostgreSQL | Data warehouse | $0–$500/month |
| dbt (data build tool) | Data transformation | Free (open source) |
| PAS reporting | Policy/claims data | Included |
3. Scale (5,000+ policies)
| Tool | Purpose | Cost |
|---|---|---|
| Snowflake or BigQuery | Enterprise data warehouse | $1K–$5K/month |
| Tableau or Power BI | Enterprise BI | $35–$70/user/month |
| dbt Cloud | Data transformation | $100–$500/month |
| Fivetran or Stitch | Data integration/ETL | $500–$2K/month |
| Monte Carlo or Great Expectations | Data quality | $500–$2K/month |
How Should You Design Your Data Architecture?
Your data architecture should connect eight core data sources (PAS, claims system, CRM, payment processor, website, marketing platforms, email platform, and phone system) through ETL pipelines into a central data warehouse, with dbt for transformation and a BI tool for visualization. Key data models include a policy cohort model for retention and LTV analysis, and a customer 360 model for a unified customer view.
1. Core Data Sources
| Source | Data | Integration Method |
|---|---|---|
| PAS | Policies, premiums, endorsements | API or database replica |
| Claims system | Claims, payments, decisions | API or database replica |
| CRM | Leads, contacts, interactions | API |
| Payment processor | Transactions, failures | Webhook/API |
| Website | Traffic, behavior, conversions | GA4, event tracking |
| Marketing platforms | Ad spend, impressions, clicks | API |
| Email platform | Opens, clicks, unsubscribes | API |
| Phone system | Call logs, recordings | API |
2. Data Warehouse Architecture
Data Sources → ETL/ELT (Fivetran/dbt) → Data Warehouse (BigQuery) → BI Tool (Metabase)
↓
Analytics Models
- Cohort analysis
- LTV calculation
- Churn prediction
- Loss ratio trending
3. Key Data Models
Policy Cohort Model: Track policies by month of origination to understand:
- Retention curves over time
- Loss ratio development by cohort
- LTV by acquisition channel
- Revenue by vintage
Customer 360 Model: Combine all data sources for a single customer view:
- All policies and pets
- All claims and outcomes
- All communications
- All payments
- Engagement score
- Predicted churn risk
How Should You Design Your Dashboards?
You should design three core dashboards: an executive dashboard showing the Big 5 metrics with trends updated daily to weekly, a marketing dashboard with funnel visualization and CAC by channel, and a claims dashboard with open claims aging, turnaround time histograms, and loss ratio trends. Each dashboard should be automated and accessible to relevant stakeholders without manual intervention.
1. Executive Dashboard
| Metric | Visualization | Update Frequency |
|---|---|---|
| Total policies in force | Single number + trend | Daily |
| Monthly new policies | Bar chart + target line | Daily |
| Loss ratio | Gauge + 12-month trend | Weekly |
| Blended CAC | Single number + trend | Monthly |
| Retention rate | Single number + cohort view | Monthly |
| Revenue | Line chart (MTD, YTD) | Daily |
2. Marketing Dashboard
| Metric | Visualization |
|---|---|
| Traffic by source | Stacked area chart |
| Quote starts and conversions | Funnel visualization |
| CAC by channel | Bar chart |
| Campaign performance | Table with key metrics |
| Top converting pages | Ranked list |
3. Claims Dashboard
| Metric | Visualization |
|---|---|
| Open claims count | Single number with aging breakdown |
| Claims turnaround time | Histogram + average trend |
| Loss ratio | Gauge + trend by month |
| Top conditions by cost | Horizontal bar chart |
| Denial rate | Single number + trend |
| Claims satisfaction | NPS gauge |
What Does the Implementation Roadmap Look Like?
The implementation roadmap spans 12 months in four phases: set up GA4 and PAS reporting with a spreadsheet dashboard in month 1; deploy a free BI tool connected to PAS with automated reports in months 2–3; build a data warehouse with ETL pipelines and core models in months 4–6; and add predictive models, automated alerts, and data quality monitoring in months 6–12.
1. Month 1: Foundation
- Set up GA4 with conversion tracking
- Configure PAS reporting for key metrics
- Build initial spreadsheet dashboard (Big 5 metrics)
- Define data ownership and access
2. Month 2–3: Basic BI
- Deploy Metabase or Looker Studio (free options)
- Connect to PAS database for automated reporting
- Build executive, marketing, and claims dashboards
- Set up automated email reports (weekly/monthly)
3. Month 4–6: Data Warehouse
- Set up BigQuery or PostgreSQL warehouse
- Build ETL pipelines from key sources
- Create core data models (cohort, customer 360)
- Migrate dashboards to warehouse-connected BI
4. Month 6–12: Advanced Analytics
- Add predictive models (churn, claims)
- Build automated alerts (loss ratio threshold, churn spike)
- Create self-service analytics for team
- Implement data quality monitoring
For CRM integration with your analytics stack, see our guide.
Frequently Asked Questions
What analytics tools should you use?
Start: GA4 + PAS reporting. Growth: add Metabase/Looker + data warehouse. Scale: enterprise BI + data team.
What are the most important metrics?
The Big 5: loss ratio, CAC, LTV, retention rate, and claims turnaround time.
How do you build from scratch?
Phase 1: GA4 + PAS reports. Phase 2: BI dashboards. Phase 3: data warehouse. Phase 4: automated reporting. Cost: $0–$50K.
When to hire a data analyst?
At 1,000+ policies. Before that, leadership can manage dashboards. At 5,000+, consider a data team.
What is the analytics maturity model?
Five levels from manual spreadsheets to predictive ML. Target Level 2 (PAS + GA4) at launch and Level 3 (BI + warehouse) by year 2.
How do you design an executive dashboard?
Include policies in force, new policies, loss ratio, CAC, retention rate, and revenue. Update daily to monthly depending on the metric.
What data sources should feed the analytics stack?
Eight core sources: PAS, claims system, CRM, payment processor, website (GA4), marketing platforms, email platform, and phone system.
How much does the analytics stack cost?
Free at pre-launch (GA4, Sheets). $0–$500/month at growth stage. $1K–$5K+/month at scale with enterprise tools and data team.
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