Description

A focused course, tailored for you

The GVP's Course on Marketing Analytics When Legacy Risk Products Lose Market Relevance

Turn stagnant insurance risk offerings into data-driven growth engines before competitors capture your market share.

Stop rebuilding the risk register every month while leadership questions the relevance of your product portfolio.

$199 one-time

Tailored to your situation. Access within 24 hours. 30-day money-back.

Includes a hand-built implementation playbook delivered alongside course access, generated for your specific situation.

Why this course

You spend weeks pulling disparate sales dashboards, claim loss tables, and underwriting spreadsheets into a single spreadsheet that never updates in time for quarterly strategy reviews. The tooling is a patchwork of legacy CRM reports, manual Excel pivots, and ad-hoc PowerBI tiles that break whenever a new product line is added. When senior leadership asks for insight, you scramble, and the conversation stalls, risking budget cuts for the portfolio.

Your current process forces you to chase data owners, reconcile inconsistent naming conventions, and manually validate risk scores, all while the market demands faster product iterations. The lack of a unified analytics framework means you cannot prove the ROI of new risk products, leaving you vulnerable to strategic obsolescence and missed revenue opportunities.

What you walk away with

Produce a single, refreshed marketing analytics dashboard for insurance risk products each month.
Identify three high-potential product enhancements backed by data within the first six weeks.
Cut the time to compile evidence for strategy reviews from days to under two hours.
Create a repeatable scoring model that predicts product adoption rates with 80% accuracy.
Communicate clear, data-driven business cases that secure funding for new risk offerings.

The 12 modules

Module 1. Mapping Product Data Sources

Define and connect the key underwriting, claims, and sales feeds needed for analytics.

Module 2. Cleaning and Normalizing Risk Data

Apply techniques to standardize disparate data sets into a single source of truth.

Module 3. Building a Core Analytics Model

Construct a reusable model that scores product performance across risk dimensions.

Module 4. Designing Interactive Dashboards

Create visual dashboards that surface actionable insights for senior leadership.

Module 5. Automating Data Refresh Pipelines

Set up scheduled processes that keep the analytics environment up-to-date without manual effort.

Module 6. Benchmarking Against Market Trends

Integrate external market data to contextualize product performance.

Module 7. Prioritizing Product Enhancements

Use the model to rank feature ideas by projected revenue impact.

Module 8. Crafting Data-Driven Business Cases

Translate model outputs into compelling narratives for funding reviews.

Module 9. Running A/B Experiments on Risk Products

Design and evaluate controlled tests to validate new product concepts.

Module 10. Establishing Governance and Ownership

Define roles and processes for ongoing analytics stewardship.

Module 11. Measuring ROI and Continuous Improvement

Track key metrics post-launch to iterate and refine product offerings.

Module 12. Scaling the Analytics Framework

Extend the methodology to new product lines and emerging market segments.

How this addresses your situation

Specific modules that map to what you said you are dealing with.

Module 1 covers Mapping Product Data Sources , exactly the data-hunt you face when underwriting, claims, and sales teams store files in separate systems.

Module 5 covers Automating Data Refresh Pipelines , precisely the manual refresh you dread each month before the strategy deck is due.

Module 8 covers Crafting Data-Driven Business Cases , the exact step you need when senior leaders ask for evidence of product ROI.

What you get with this course

A pre-populated risk data mapping template.
A cleaned data checklist for underwriting and claims feeds.
A reusable analytics model workbook.
An interactive product performance dashboard prototype.
A scheduled data refresh playbook.
A market benchmarking comparison sheet.
A product enhancement prioritization matrix.
A business case storytelling guide.
An A/B experiment design worksheet.
A governance RACI table.
A post-launch ROI scorecard.
A scaling roadmap document.

What you will have in hand by Day 1, Week 1, Month 1

Day 1: tailored playbook in hand, risk data mapping template pre-populated for your environment, intake form ready for the next data request.

Week 1: first version of the product performance dashboard live and shared with the finance lead.

Month 1: recurring reporting cadence established, with a clean evidence pack ready for each executive review.

Before and after

Before

Your analytics live in fragmented Excel files, separate PowerBI tiles, and stale CRM extracts. Evidence for strategy meetings is scattered across inboxes, and each quarter you rebuild the same risk register from scratch, losing hours and credibility with leadership.

After

You now maintain a single, refreshed dashboard that pulls from unified risk data, with a ready-to-present evidence pack for each review. A recurring cadence of data refreshes and governance meetings keeps the product portfolio aligned, and you can confidently discuss growth opportunities with the executive team.

What happens if you do not address this

If you ignore this gap, the next quarterly review will arrive with no unified evidence, forcing you to defend the product line with guesswork. The risk of budget cuts rises, and the market will favor competitors with faster, data-backed product cycles.

Who it is for

An individual contributor leading security and risk product strategy, juggling daily product roadmaps, data collection from underwriting, claims, and sales teams, and responsible for translating market trends into actionable product insights without a dedicated analytics squad.

Who this is NOT for. This is not for someone who needs a basic introduction to insurance risk concepts or a generic marketing analytics overview.

How it arrives

Within 24 hours of purchase your account in the learning environment is provisioned and the tailored implementation playbook is delivered alongside it. The playbook is hand-built around your specific situation, not LLM-generated boilerplate.

Time investment. 6 hours of focused work spread over a week, saving an estimated 30-45 hours of internal data wrangling.

Why $199 is the right number

A half-day consultant would charge $2,500-$4,000 for the same scope, a generic analytics certification runs $1,200-$1,800, and building this yourself takes 60+ hours of trial-and-error. At $199 you get a proven framework, ready-to-use artefacts, and ongoing support for far less risk and cost.

FAQ

Do I need a data science background to complete this course?

No, the curriculum walks you through each step with practical templates and no coding required.

Will the course work with our existing CRM and claims system?

Yes, the modules focus on connecting to common enterprise data stores and can be adapted to any platform.

How much time will I need each week to stay on track?

Allocate about 3 hours per week and you’ll finish the 12 modules within a month.

What if my team already has a dashboard but it’s outdated?

The course includes a refresh plan that modernizes existing visuals and automates data pipelines.

30-day money-back guarantee. If after a week of working through the materials this is not what you needed, reply to the receipt email and a full refund is processed. No questions, no forms.

Within 24 hours your account in the learning environment is provisioned and the tailored implementation playbook is delivered alongside it.