Course on Customer Segmentation in Mobility Insurance
Online course of 300 hours in customer segmentation in mobility insurance. 6 modules covering Regulation (EU) 2016/679 (GDPR), Regulation (EU) 2024/1689 (Artificial Intelligence Regulation) and Python (scikit-learn). Assigned tutor and open enrolment.
Course on Customer Segmentation in Mobility Insurance — studying from Canada
Canada has a major insurance and financial services sector, with headquarters and operations concentrated in Toronto and Montréal. Insurers and reinsurers rely on actuarial science, risk modelling and data analytics for pricing, reserving and capital management. Federal and provincial regulators oversee solvency, conduct and consumer protection. Engineers and data specialists with risk and modelling skills find roles at insurers, brokers, consultancies and regulatory bodies.
Fees for Canada: C$1,010 (Canadian dollar), with instalments and employer sponsorship available.
Relevant bodies in Canada
Typical roles
Questions from Canada
Is this programme recognised by professional bodies in Canada for insurance and actuarial data work?
SEIUM's qualifications are private, not official state degrees, and are not accredited by a national body in Canada. Actuarial credentials in Canada are governed by the Canadian Institute of Actuaries, and actuarial practice is regulated under provincial and federal rules. A private engineering qualification does not confer these credentials, so confirm recognition with employers or the relevant body yourself.
How might this qualification be used by employers in Canada in insurance or risk roles?
Insurers, banks and risk teams in Canada may value engineering-based data analysis and risk modelling skills, but how they regard a private qualification is their decision. Hiring in these sectors often depends on experience, professional exams and specific credentials. Your final project can serve as evidence of applied skills, though it does not guarantee any role or salary outcome.
What are the practical demands of studying this online from Canada?
Study is fully online and in English, and no student visa is required if you study from home. Programme lengths range from 300 to 1,900 hours, so consider how many hours a week you can commit alongside work. You are assigned a technical tutor for support. Fees can be paid in instalments, and some students receive employer sponsorship, which is possible but not guaranteed.
Take the controls.
Run a real engineering trade-off from this field. Move the sliders, watch the model react in real time and beat the challenge.
What you will be able to do
Explain the value of segmentation in mobility insurance.
Prepare and enrich customer, policy and claims data.
Apply segmentation techniques such as k-means, hierarchical clustering and trees.
Identify value-based and behavioural segments.
Put segmentation into practice in campaigns, retention and product design.
Ensure compliance with data protection, AI and non-discrimination rules.
Present an actionable segmentation of a real or simulated portfolio.
Who it's for
- Data scientist in insurance who wishes to specialise in customer segmentation in mobility insurance.
- Customer marketing analyst who wishes to specialise in customer segmentation in mobility insurance.
- CRM manager at an insurer who wishes to specialise in customer segmentation in mobility insurance.
- Pricing actuary who wishes to specialise in customer segmentation in mobility insurance.
- Business intelligence analyst who wishes to specialise in customer segmentation in mobility insurance.
- Data governance manager who wishes to specialise in customer segmentation in mobility insurance.
- Engineering or science graduates seeking to enter mobility insurers.
What you'll work with
- Regulation (EU) 2016/679 (GDPR)
- Regulation (EU) 2024/1689 (Artificial Intelligence Regulation)
- Directive 2004/113/EC (equal treatment between men and women in goods and services)
- Law 15/2022 on comprehensive equal treatment and non-discrimination
- Delegated Regulation (EU) 2017/2358 (product governance and oversight)
- Directive (EU) 2016/97 (IDD)
6 modules · 300 h
1.1Technical, commercial and service segmentation: three distinct objectives1.2Which specific decision each segmentation should support1.3Data available on the mobility customer1.4Difference between a segment and a rating factor1.5The risk of segmenting by what cannot be used1.6Prohibited variables and indirect proxies1.7A reasonable number of segments1.8Segment stability over time1.9Documenting the segmentation1.10Workshop: defining the objective of a segmentation
2.1Policy, claims, contact and payment data2.2Vehicle and declared use data2.3Digital and channel behaviour data2.4Telematics data and how it fits2.5Aggregated geographic and socioeconomic enrichment2.6Data quality and customer deduplication2.7Single customer view2.8Data minimisation and legal basis for processing2.9Preparing variables for analysis2.10Practical: building the customer table
3.1Rule-based segmentation3.2K-means clustering and its limitations3.3Hierarchical methods and dendrograms3.4Mixture models and probabilistic assignment3.5Prior dimensionality reduction3.6Choosing the number of groups3.7Profiling and interpreting each group3.8Stability and validation of the solution3.9Supervised segmentation by value or risk3.10Practical: segmenting a mobility portfolio
4.1Customer lifetime value by segment4.2Propensity to lapse by segment4.3Price and coverage sensitivity4.4Channel usage and contact preferences4.5Claims behaviour4.6Multi-policy holding and cross-selling potential4.7Unprofitable customers: options and responsibility4.8Vulnerable customers and appropriate service4.9Prioritising segments4.10Practical: value and risk matrix by segment
5.1Differentiated value proposition by segment5.2Communication and messaging by segment5.3Specific product design where justified5.4Differentiated service and its cost5.5Targeted retention actions5.6Ethical limits of personalisation5.7Controlled testing of each action5.8Measuring incremental impact5.9Coordination between marketing, actuarial and service teams5.10Practical: activation plan for two segments
6.1Data protection in segmentation and profiling6.2Automated decisions and information to the customer6.3Fairness and impact analysis by group6.4Governance of the segmentation model6.5Periodic updating of segments6.6Documentation and traceability6.7Dashboard by segment6.8Common mistakes in segmentation projects6.9Reporting results to management6.10Final project: complete segmentation with its usage plan
How you'll learn
Fully 100% online training on the SEIUM 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 course with work.
Each module combines theory, guided reading of the original standard and applied exercises. The working software is Python (scikit-learn), R, SAS, Tableau, Salesforce Marketing Cloud, in free, academic or evaluation versions. Trials are studied using real segmentation procedures on a motor insurance portfolio through clustering and customer lifetime value analysis. Assessment is continuous, module by module, plus the final integrating project.
Build it end to end
Your final project follows the real engineering workflow, step by step, reviewed by your tutor.
Explain the value of segmentation in mobility insurance.
Prepare and enrich customer, policy and claims data.
Apply segmentation techniques such as k-means, hierarchical clustering and trees.
Identify value-based and behavioural segments.
Put segmentation into practice in campaigns, retention and product design.
Where this takes you
Requirements
- No prior qualification 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 comprises 300 hours in total, spread over 3 to 5 months. That is between 10 and 15 hours a week on average, compatible with a full-time job.
You will work with Python (scikit-learn), R, SAS, Tableau and Salesforce Marketing Cloud. Free, academic or evaluation versions are used, and the tutor indicates the open-source alternative where one exists.
A SEIUM diploma upon passing the continuous assessment and the integrating project. It is in-house training, not an official Ministry qualification.
Yes. Modules passed in a course or postgraduate diploma in the same area are recognised in the higher programme. Check the recognition policy when you enrol.
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