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Postgraduate DiplomaInsurance, Risk & Actuarial Data Applications open

Postgraduate Diploma in Revenue Management Applied to the Insurance Sector

Online postgraduate diploma of 800 hours in revenue management applied to the insurance sector, structured in 8 modules covering customer analytics, demand elasticity, price optimisation, distribution channels, retention and portfolio value, and regulation, ethics and technology, with an assigned technical tutor.

Duration800 hours · 6–8 months
ModeOnline
LanguageEnglish
Start datesSet by the faculty
AwardSEIUM Institute diploma
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Learning outcomes

What you will be able to do

01

Adapt the principles of revenue management to the nature of insurance.

02

Segment customers using value and behaviour analytics.

03

Estimate demand elasticity at new business and renewal.

04

Optimise prices under technical, commercial and regulatory constraints.

05

Manage profitability by distribution channel.

06

Design retention strategies based on customer lifetime value.

07

Assess the ethical and regulatory limits of differential pricing.

Profile

Who it's for

  • Insurance revenue managers.
  • Pricing and elasticity analysts.
  • Customer retention managers.
  • Distribution channel managers.
  • Commercial data scientists.
  • Pricing strategy consultants.
  • Engineering or science graduates looking to join a non-life insurer.
Standards & tools

What you'll work with

  • Directive (EU) 2016/97 (IDD) (product oversight and governance)
  • EIOPA supervisory statement on differential pricing practices (2023) (fair pricing supervision)
  • Directive 2005/29/EC (unfair commercial practices)
  • Directive (EU) 2019/2161 (modernisation of consumer protection)
  • Regulation (EU) 2016/679 (GDPR) (data protection)
  • Ley 50/1980 (Spanish Insurance Contract Act)
  • Earnix
  • Python (PuLP, scikit-learn)
  • Gurobi
  • Tableau
  • SAS Customer Intelligence
Syllabus

8 modules

  1. 1.1Origins in airlines and hotels
  2. 1.2Adaptation to the insurance sector
  3. 1.3Differences between RM and actuarial pricing
  4. 1.4Customer lifetime value
  5. 1.5Customer and channel segmentation
  6. 1.6Policy life cycle
  7. 1.7Indicators: written premium, retention and margin
  8. 1.8Organisation and roles of the RM function
  9. 1.9Regulatory and reputational risks
  10. 1.10Case study: RM diagnosis of a motor insurer
  1. 2.1Purchase propensity models
  2. 2.2Lapse and churn models
  3. 2.3Actuarial customer lifetime value
  4. 2.4Segmentation with clustering
  5. 2.5Cohort analysis
  6. 2.6Digital interaction data
  7. 2.7Attribution of acquisition channels
  8. 2.8Customer data quality and governance
  9. 2.9Visualisation for decision-making
  10. 2.10Laboratory: CLV model for a home insurance portfolio
  1. 3.1Demand theory and elasticity
  2. 3.2Estimation with quote data
  3. 3.3Logistic conversion models
  4. 3.4Effect of competition and price comparison sites
  5. 3.5Renewal elasticity
  6. 3.6Heterogeneity across segments
  7. 3.7Design of price experiments
  8. 3.8Endogeneity and estimation bias
  9. 3.9Causal and uplift models
  10. 3.10Practical exercise: estimating elasticities by segment
  1. 4.1Objective function: volume, premium and profit
  2. 4.2Business and regulatory constraints
  3. 4.3Non-linear programming and heuristics
  4. 4.4Portfolio efficient frontier
  5. 4.5Individual and segment-level optimisation
  6. 4.6Scenarios and sensitivity analysis
  7. 4.7Ban on price walking
  8. 4.8Fair price and customer value
  9. 4.9Approval and change control
  10. 4.10Workshop: constrained renewal optimisation
  1. 5.1Pricing by channel: direct, agents and bancassurance
  2. 5.2Commissions and acquisition cost
  3. 5.3Price comparison sites and aggregators
  4. 5.4Bundling and cross-selling
  5. 5.5Commercial discounts and their control
  6. 5.6Campaigns and promotions
  7. 5.7Underwriting capacity management
  8. 5.8Customer experience and perceived price
  9. 5.9Commercial indicators by channel
  10. 5.10Case study: pricing strategy on a comparison site
  1. 6.1Proactive retention strategies
  2. 6.2Next-best-action models
  3. 6.3Personalised retention offers
  4. 6.4Managing counter-offers
  5. 6.5Portfolio value and profitable growth
  6. 6.6Impact on the combined ratio
  7. 6.7Reinsurance and risk appetite
  8. 6.8Premium and claims forecasting
  9. 6.9Revenue management in life insurance
  10. 6.10Practical exercise: CLV-based retention campaign
  1. 7.1Product oversight and governance (POG)
  2. 7.2Fair treatment of customers and Consumer Duty
  3. 7.3EIOPA guidance on pricing practices
  4. 7.4GDPR and profiling
  5. 7.5EU AI Act
  6. 7.6Real-time decision platforms
  7. 7.7Automation and monitoring
  8. 7.8Pricing model governance
  9. 7.9Audit of results
  10. 7.10Case study: review of a differential pricing practice
  1. 8.1Portfolio diagnosis
  2. 8.2Customer segmentation
  3. 8.3Demand and retention models
  4. 8.4Optimisation strategy
  5. 8.5Simulation of results
  6. 8.6Regulatory analysis
  7. 8.7Implementation plan
  8. 8.8Monitoring dashboard
  9. 8.9Report and presentation
  10. 8.10Final project: final review with the tutor
Methodology, software & labs

How you'll learn

The programme is delivered 100% online on the SEIUM campus, with downloadable material, recorded classes and an assigned technical tutor. There are no fixed timetables: you progress at your own pace within each intake, so it fits around your job.

Each module combines theory, guided reading of the original standard and applied exercises. The working software is Earnix, Python (PuLP, scikit-learn), Gurobi, Tableau and SAS Customer Intelligence, in free, academic or evaluation versions. Practical work is based on real procedures: estimating renewal elasticity, price optimisation with business constraints and A/B experiments in the digital channel. Assessment is continuous by module, plus the final project.

Standards covered: Directive (EU) 2016/97 (IDD); EIOPA supervisory statement on differential pricing practices (2023); Directive 2005/29/EC; Directive (EU) 2019/2161; Regulation (EU) 2016/679 (GDPR); Ley 50/1980.

Assigned technical tutor:every module ends with a deliverable that your tutor marks individually.
Standards-based learning:guided reading of the IDD, the EIOPA supervisory statement on differential pricing and Directive 2005/29/EC applied to real cases, not as a theoretical annex.
Industry software:Earnix, Python (PuLP, scikit-learn), Gurobi, Tableau and SAS Customer Intelligence, in free, academic or evaluation versions.
Final project:a revenue management project for a real insurance portfolio, from segmentation and demand models to optimisation strategy, regulatory analysis and monitoring dashboard, reviewed by your tutor.
Flexible format:100% online, no fixed timetables, compatible with full-time work.
Capstone project

Build it end to end

Your final project follows the real engineering workflow, step by step, reviewed by your tutor.

Portfolio diagnosis and segmentation
Diagnose the portfolio and segment its customers by value and behaviour.
Demand and retention models
Build the conversion, elasticity and retention models.
Optimisation strategy
Define the price optimisation strategy under business and regulatory constraints.
Simulation and regulatory review
Simulate the results and review compliance with fair pricing rules.
Final project
Deliver the complete revenue management project with implementation plan and dashboard, reviewed by your tutor.
Career opportunities

Where this takes you

Insurance revenue managerPricing and elasticity analystCustomer retention managerDistribution channel managerCommercial data scientistPricing strategy consultant
Admissions & scholarships

Requirements

  • Prior technical training or professional experience in the sector. No university degree is required.
  • An internet connection and a computer able to run the programme software.
  • English sufficient to read technical standards.

Ready to start?

Send your application today. Our admissions team replies within 72 hours with your study plan and funding options.

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FAQ

Frequently asked questions

The programme totals 800 hours over 6–8 months. You set your own pace within each intake, and your tutor helps you plan the workload around your job.

You work with Earnix, Python (PuLP, scikit-learn), Gurobi, Tableau and SAS Customer Intelligence, using free, academic or evaluation versions. Your tutor indicates the free alternative wherever one exists.

A SEIUM Institute diploma, awarded on passing the continuous assessment and the final project. It is SEIUM's own qualification, not an official state degree; its recognition depends on the programme completed and the regulations of each country.

Yes. Modules passed are recognised in a higher programme in the same area (postgraduate diploma or master's). Ask about the recognition policy when you enrol.

Prior technical training or professional experience in the sector (no university degree required), a computer able to run the software and English sufficient to read technical standards.

Start dates are set by SEIUM's faculty for each intake. Our admissions team will confirm the next available date when you apply.

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