Postgraduate Diploma in Elasticity and Customer Segmentation in Insurance
Online 800-hour postgraduate diploma in elasticity and Customer Segmentation in Insurance. 8 modules covering Delegated Regulation (EU) 2017/2358 (product governance), Directive (EU) 2016/97 (IDD, customer demands and needs) and R. Assigned tutor and enrolment is open.
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What you will be able to do
Explain the role of segmentation in insurance management.
Apply statistical and machine learning segmentation techniques.
Interpret the theory of insurance demand.
Estimate price elasticities by segment.
Segment customers by value and behaviour.
Apply segmentation to specific commercial actions.
Ensure regulatory compliance and fairness in the use of segments.
Who it's for
- Customer segmentation analyst who wants to specialise in Elasticity and Customer Segmentation in Insurance.
- Pricing actuary who wants to specialise in Elasticity and Customer Segmentation in Insurance.
- Marketing data scientist who wants to specialise in Elasticity and Customer Segmentation in Insurance.
- Customer intelligence manager who wants to specialise in Elasticity and Customer Segmentation in Insurance.
- Insurance CRM analyst who wants to specialise in Elasticity and Customer Segmentation in Insurance.
- Commercial strategy consultant who wants to specialise in Elasticity and Customer Segmentation in Insurance.
- Engineering or science graduates looking to join insurance companies.
What you'll work with
- Delegated Regulation (EU) 2017/2358 (product governance)
- Directive (EU) 2016/97 (IDD, customer demands and needs)
- Regulation (EU) 2016/679 (GDPR, profiling)
- Directive 2004/113/EC (equal treatment in goods and services)
- Law 15/2022 on equal treatment and non-discrimination
- EIOPA supervisory statement on differential pricing practices (2023)
- ISO 20252 (market research)
8 modules · 800 h
1.1Objectives of customer segmentation1.2Risk versus commercial segmentation1.3Demographic and behavioural variables1.4Internal and external data1.5Insured customer lifecycle1.6Customer lifetime value1.7Traditional market segments1.8Regulatory limits on variables1.9Ethics in segmentation1.10Case study: segmentation of a home insurance portfolio
2.1Variable preparation and scaling2.2k-means clustering2.3Agglomerative hierarchical clustering2.4Gaussian mixture models2.5Segmentation with decision trees2.6Dimensionality reduction with PCA2.7Segment validation2.8Profiling and interpretation2.9Temporal stability of segments2.10Practice: customer clustering in Python
3.1Insurance demand and risk aversion3.2Price elasticity of demand3.3Elasticity in new business and renewals3.4Cross-elasticity between products3.5Non-price factors of demand3.6Inertia and switching costs3.7Consumer behaviour3.8Behavioural economics in insurance3.9Comparison tools and price transparency3.10Workshop: demand curve for a motor insurance product
4.1Quote and conversion data4.2Logistic conversion models4.3Relative price variables4.4Price endogeneity4.5Randomised price experiments4.6Double machine learning4.7Hierarchical Bayesian models4.8Elasticity by segment4.9Uncertainty intervals4.10Laboratory: estimation of elasticity by segment
5.1Purchase propensity models5.2Lapse propensity models5.3Expected customer value5.4Value and risk matrix5.5Channel segmentation5.6Segmentation by price sensitivity5.7Microsegmentation and personalisation5.8Uplift models5.9Next best action5.10Practice: value matrix and elasticity
6.1Product design by segment6.2Differentiated pricing strategies6.3Acquisition campaigns6.4Retention programmes6.5Cross-selling across lines of business6.6Personalisation of the digital experience6.7Channel management6.8Measuring commercial results6.9Culture of continuous experimentation6.10Case study: commercial strategy by segment
7.1Prohibited variables and the Test-Achats ruling7.2Indirect discrimination and proxies7.3AI Regulation in life and health insurance7.4POG product supervision7.5Transparency towards the customer7.6Data protection in profiling7.7Auditing segmentation models7.8Documenting decisions7.9Product and pricing committees7.10Workshop: auditing segmentation variables
8.1Defining the business objective8.2Preparing customer data8.3Portfolio segmentation8.4Elasticity estimation8.5Value and sensitivity matrix8.6Strategies by segment8.7Compliance analysis8.8Experimentation plan8.9Report and presentation8.10Final review with the tutor
How you'll learn
100% online training on the SEIUM Institute campus, with downloadable material, recorded classes and an assigned technical tutor. There are no fixed timetables: students progress at their own pace within the intake, which makes it possible to combine the programme with work.
Each module combines theory, guided reading of the original standard and applied exercises. The working software is R, Python (scikit-learn), SAS, Earnix, Tableau, in free, academic or evaluation versions. The case studies are based on real portfolio segmentation procedures, covering clustering techniques, elasticity estimation with price experiments, and customer value analysis by segment. Assessment is continuous across each module, plus the final integrative project.
Build it end to end
Your final project follows the real engineering workflow, step by step, reviewed by your tutor.
Explain the role of segmentation in insurance management.
Apply statistical and machine learning segmentation techniques.
Interpret the theory of insurance demand.
Estimate price elasticities by segment.
Segment customers by value and behaviour.
Where this takes you
Requirements
- Prior technical training or professional experience in the sector. No university degree is required.
- Internet connection and a computer capable of running the programme's software.
- Sufficient level of Spanish to follow technical regulatory texts.
Ready to start?
Send your application today. Our admissions team replies within 72 hours with your study plan and funding options.
Frequently asked questions
The programme totals 800 hours, spread over 6 to 8 months. That is between 10 and 15 hours per week on average, compatible with a full-time job.
The programme uses R, Python (scikit-learn), SAS, Earnix and Tableau. Free, academic or evaluation versions are used, and the tutor indicates the free alternative where one exists.
A diploma issued by SEIUM Institute upon passing the continuous assessment and the final integrative project. This is in-house training, not an official Ministry qualification.
Yes. Modules passed in a course or postgraduate diploma in the same area can be recognised towards the advanced programme. Check the recognition policy when you enrol.
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