Customer preference, willingness-to-pay and service portfolio design.
A research-led service strategy that translates EV ownership anxiety into three clear after-sales packages: Essential EV Care, Battery Confidence and Premium Mobility Protection.
Category
Academic research
Domain
Automotive & mobility
Method
Survey + statistical analysis
Year
2026
Executive thesis
The strongest EV after-sales proposition is not another maintenance schedule. It is a confidence architecture that reduces uncertainty around the battery, protects mobility when the journey is disrupted and makes service value visible before a failure occurs.
36
Modelled responses used to demonstrate the complete research workflow.
3
Customer segments with different motives, evidence needs and price tolerance.
EUR 199
Approximate median annual willingness to pay.
44%
Modelled preference for the Battery Confidence package.
Modelled portfolio data - not verified market findings
PROJECT 09 / 01 OF 12
02Why the problem matters
EVs reduced routine maintenance. They did not remove ownership risk.
The risk profile moved. Customers now worry less about oil changes and more about battery condition, electronic repair cost, public-charging reliability, software failure and the consequences of losing mobility at the wrong moment.
Traditional packages can therefore feel disconnected from what EV customers actually fear. A commercially relevant extension pack must protect both the vehicle and the customer's ability to continue the journey.
The new after-sales risk landscape
01
Battery uncertainty
Degradation, replacement cost and resale-value ambiguity create a long-term financial concern.
02
Digital complexity
Software and electronic failures are harder for customers to diagnose or interpret.
03
Charging dependence
A failed or unreliable charging event can immediately disrupt the journey.
04
Mobility continuity
Roadside support, towing, workshop priority and replacement mobility become part of the product promise.
The strategic shift: sell confidence, transparency and continuity - not a list of maintenance activities.
PROJECT 09 / 02 OF 12
03Decision question and hypotheses
The research begins with a business decision, not with a dashboard.
Main question: which combination of EV after-sales services creates the highest customer value and purchase intention at an acceptable annual price?
Decision chain
01
Customer context
Ownership stage, mileage, home charging and travel pattern.
02
Perceived risk
Battery, charging, repair cost, software and disruption.
03
Service value
What customers consider important enough to protect.
04
Price response
What they are willing to pay and how they prefer to pay.
05
Portfolio decision
Which package, promise and price should be tested first.
H5A clear bundle creates stronger intention than separate add-ons.
H6New EV users value confidence; experienced users value convenience and reliability.
PROJECT 09 / 03 OF 12
04Research model and survey design
A complete decision model, built from six survey sections.
The questionnaire combines profile, vehicle usage, perceived EV risks, service importance, package comparison and willingness to pay. This creates a traceable path from customer context to a recommended portfolio.
How customer characteristics and perceived EV risks translate into package choice.
Customer Characteristics
- EV ownership
- Annual mileage
- Home charging
- Long-distance travel
Perceived EV Risks
- Battery degradation
- Unexpected repair cost
- Charging failure
- Roadside disruption
→
Service Preferences
- Battery monitoring
- Roadside support
- Charging assistance
- Diagnostics
Price Response
- Willingness to pay
- Price sensitivity
- Monthly vs annual
- Bundle preference
→
Decision Outcomes
- Purchase intention
- Preferred package
- Renewal intention
- Perceived value
Research question: which combination of services creates the highest customer value at an acceptable annual price?
Illustrative portfolio dataset: n=36 modelled responses.Project 9 | EV After-Sales Value Architecture
PROJECT 09 / 04 OF 12
05Sample structure and evidence quality
The sample is exploratory. The workflow is complete.
The 36-response model is large enough to demonstrate a full research-to-decision process, but not large enough to represent the German EV market. The project therefore separates analytical demonstration from market claims.
Modelled sample
47%
Current EV owners
31%
Considering an EV
22%
Prospective first-time buyers
44%
Without reliable home charging
36%
Frequent long-distance drivers
53%
Driving over 15,000 km annually
03. Respondent Profile
Exploratory sample structure used to test the complete research and decision workflow.
Mileage above 15,000 km
53%
Frequent long-distance
36%
No reliable home charging
44%
Urban residents
58%
Prospective buyers
22%
Considering an EV
31%
Current EV owners
47%
Illustrative portfolio dataset: n=36 modelled responses.Project 9 | EV After-Sales Value Architecture
Technical confidence creates more value than cosmetic benefits.
Battery-health monitoring ranks first, followed by roadside assistance and charging-failure support. The pattern is consistent: customers value services that reduce financial uncertainty or protect mobility.
04. Service Importance Ranking
Technical confidence and mobility protection create more value than cosmetic benefits.
Battery-health Monitoring
4.38
Roadside Assistance
3.87
Charging-failure Support
3.70
Software Diagnostics
3.54
Annual EV Inspection
3.40
Priority Workshop Booking
3.29
Replacement Mobility
3.22
Digital Service History
3.01
Cosmetic Benefits
2.21
Average importance score (1 = not important, 5 = extremely important)n=36
Roadside and charging support protect the journey when the vehicle cannot.
Digital tools must explain value
Reports and diagnostics matter when they make risk visible and actionable.
Cosmetics dilute the proposition
Low cosmetic scores show that the package should remain technically credible and focused.
Positioning implication: the package should be sold as confidence and mobility protection, not as routine maintenance.
PROJECT 09 / 06 OF 12
07Price, bundling and purchase friction
Customers want protection. They still compare the price.
The strongest acceptable annual range sits between EUR 150 and EUR 249. This makes one high-priced package risky and supports a simple three-level value ladder.
Bundle preference
69%
prefer a structured service bundle.
Bundles reduce decision complexity, make coverage easier to understand and create a predictable annual cost. The bundle must remain simple: more features do not automatically create more value.
05. Willingness-to-Pay Distribution
The strongest acceptable range sits between EUR 150 and EUR 249 per year.
22%
Below EUR 150
39%
EUR 150-199
22%
EUR 200-249
11%
EUR 250-299
6%
EUR 300+
Median modelled WTP: ~EUR 199 annually (n=14 in peak band)n=36
Pricing signalMedian modelled WTP: approximately EUR 199 | Average: approximately EUR 213 | Most attractive range: EUR 149-229
PROJECT 09 / 07 OF 12
08Exploratory statistical evidence
Purchase intention rises with perceived risk and mobility need - but falls with price sensitivity.
The statistical layer is used to strengthen the decision logic, not to claim national causality. With 36 modelled responses, coefficients are directional evidence that must be validated with a larger real sample.
06. Correlation Analysis
Purchase intention rises with perceived risk and mobility need, but falls with price sensitivity.
Standardised coefficients indicate which variables are most strongly associated with purchase intention.
Illustrative model R-squared: 0.87
Battery Concern
0.32
Charging Dependence
0.30
Long-distance Need
0.21
EV Experience
0.03
Price Sensitivity
-0.42
Standardised coefficientn=36
Correlation signal
Battery concern, charging dependence and long-distance need move positively with purchase intention.
Regression signal
Price sensitivity is negative; battery concern and charging dependence are the strongest positive modelled predictors.
PROJECT 09 / 08 OF 12
09Customer segmentation
One market. Three different reasons to buy.
Segmentation converts the statistical evidence into a service strategy. Each group requires a different promise, proof of value and price point.
Confidence Seekers
39%
Dominant concern
Battery uncertainty
Recommended package
Battery Confidence
Profile
New or prospective EV users; want transparent health evidence, early anomaly detection and predictable cost.
Mobility Protectors
33%
Dominant concern
Journey disruption
Recommended package
Premium Mobility Protection
Profile
High-mileage, long-distance drivers; value roadside assistance, towing, replacement mobility and fast workshop access.
Value Optimisers
28%
Dominant concern
Affordability
Recommended package
Essential EV Care
Profile
Price-sensitive, moderate risk; want essential technical coverage, digital diagnostics and a simple predictable price.
Segmentation determines the promise, evidence, service design and price each customer receives.
PROJECT 09 / 09 OF 12
10Recommended service portfolio
A three-level value ladder keeps customer choice clear.
The architecture progresses from essential technical visibility, to battery confidence, to complete mobility continuity. Each step adds a distinct customer outcome - not random feature volume.
01 · ESSENTIAL EV CARE
EUR 129 / yearFor Value Optimisers
Annual inspection, software diagnostics, digital health report and reminders.
Promise: simple and affordable EV care.
02 · BATTERY CONFIDENCE
EUR 219 / yearFor Confidence Seekers
Adds battery-health score, degradation monitoring, anomaly detection and condition reports.
Promise: understand and protect the most valuable EV component.
Understand and protect your most valuable EV component.
Premium Mobility Protection
EUR 349/yr
Target segment
Mobility Protectors
Included services
All Battery Confidence services · 24/7 roadside assistance · Charging-failure support · Priority workshop booking
Customer promise
Protection for the vehicle and continuity for the journey.
Illustrative portfolio dataset: n=36 modelled responses.Project 9 | EV After-Sales Value Architecture
PROJECT 09 / 10 OF 12
11Preference, commercial logic and pilot
Battery Confidence is the lead concept. A pilot must prove the economics.
The middle package receives the strongest modelled preference because it offers visible protection at a price accepted by a large share of respondents. The result is a starting hypothesis, not a launch approval.
09. Package Preference Simulator
Battery Confidence creates the strongest balance between protection and acceptable price.
33%
n=12
Essential EV Care
44%
Battery Confidence
23%
Premium Mobility Protection
Recommended lead package: Battery Confidence at EUR 219 annuallyn=36
Illustrative commercial scenario
10,000
Eligible customers in the test population.
18%
Assumed adoption rate.
1,800
Illustrative subscribers.
EUR 408,600
Gross annual contract value before service-delivery costs.
Controlled pilot decision gates
01
Understand
Can customers explain the package and its exclusions?
02
Adopt
Do they purchase at the proposed price?
03
Use
Which services are actually used and at what cost?
04
Renew
Does experienced value translate into renewal intention?
A launch decision requires evidence that the package creates customer value after service cost, operational complexity and renewal behaviour are included.
PROJECT 09 / 11 OF 12
12Strategic recommendation and credibility
Lead with battery confidence. Build the right to scale.
The first commercial test should make battery health visible, keep entry pricing accessible and treat premium protection as a mobility-continuity proposition. Scale only after customer understanding, service cost and renewal behaviour are proven.
Recommended actions
01
Use a three-level portfolio to preserve clarity.
02
Keep Essential EV Care close to EUR 129 to lower adoption barriers.
03
Make value visible through battery reports, alerts and service updates.
04
Position premium protection around journey continuity, not feature count.
05
Validate price with pilot offers, dealer trials and a formal conjoint study.
06
Measure renewal and actual service cost before broad expansion.
Measurement system
CustomerConversion, understanding, perceived value, satisfaction and renewal intention.
CommercialAdoption, package mix, revenue per subscriber, lifetime value and contribution.
ServiceDiagnostic use, roadside frequency, workshop conversion, service cost and resolution time.
ResearchResponse quality, scale reliability, model power and package-choice consistency.
Credibility boundary
This is an academic and self-directed portfolio project. The 36-response results are modelled to demonstrate the analytical process. They are not verified market findings and must be replaced by cleaned, validated responses before any external market claim.
What the project demonstrates
Quantitative research design, statistical interpretation, customer segmentation, willingness-to-pay analysis, service-package architecture, pricing logic, business-case translation and evidence-based launch governance.
PROJECT 09 / 12 OF 12
Go beyond the overview. Explore the complete project documentation, process, and supporting files on GitHub.