Redesigning the EV charging support system at Shell Recharge

Executive summary

Shell Recharge operates one of the world’s largest electric vehicle charging networks, supporting enterprise fleet clients and individual consumers in multiple markets. When I joined as Principal Product Designer the team in Amsterdam, the customer support operation was in its inception and very inefficient (as highlighted to me). A 30-person team managing B2B and B2C queries across three tiers in Salesforce Lightning Console, with no routing logic and standardized information handling. Agents frequently had to contact customers a second time to collect information they had missed to capture during the initial call.

The objective was to take care of the entire customer, technical, and financial support services. Eliminate reactive, labour-intensive support through automation, reducing resolution times, cutting cost-per-ticket, and rebuilding the support architecture around a single, streamlined channel.

I held design ownership, operating as the senior UX voice within a cross-functional team spanning sales, support operations, engineering, and Salesforce platform specialists. My scope covered the complete support journey, from customer-facing touch-points through to agent tooling and internal frameworks, across a two-year contract.

The support model redesign covered customer intake, technical escalation, and finance-related service flows across B2B and B2C channels.

Discovery & strategic insights

Conducting tests and getting familiar with existing processes

To build an accurate picture of the support operations, I ran discovery sessions with 20 participants, combining individual interviews, group sessions, observation, and shadowing. Represented divisions included Customer Care, Sales, Tech Support, and Finance.

Group workshops and whiteboard sessions helped me map the end-to-end process, producing journey maps and service maps that made the full picture visible.

What surfaced was consistent across every group: there was a lot of manual effort and workarounds. Information was passed via multiple disconnected tools rather than a centralised system, and there was no standardised way of handling data between colleagues.

The findings directly informed the case for consolidation and automation that shaped the redesign strategy.

Diagnosing the problems

Discovery revealed four core problems with the support operation: missing intake tooling, disconnected systems, no standardised processes, and a misaligned escalation model.

Missing intake tooling. Agents had no structured way to capture diagnostic information at first contact. Critical details were routinely missed, triggering follow-up calls, extended resolution timelines, and growing backlogs.

Disconnected systems. Information moved across multiple tools with no central source of truth. Data was passed between colleagues and divisions through fragmented channels, creating gaps and duplication at every handoff.

No standardised processes. With no shared way of handling or transferring information, each agent developed their own approach. Inconsistency was baked into the operation - structural rather than behavioural.

Misaligned model. Mapping the full support journey revealed that the three-tier escalation model did not reflect actual query patterns. The majority of queries followed predictable, automatable flows. Only a minority required specialist intervention. Yet every query was routed as if it needed human handling, compounding workload across all tiers unnecessarily.

Translating problems into a design strategy

The support handled fairly complex cases such as EV charging infrastructure, hardware diagnostics, and installations. Mapping query patterns against resolution paths identified a clear segmentation: the majority of queries followed predictable logic flows. Only a minority required specialist intervention. This became the foundation for the redesign. Rather than optimising a three-tier escalation model, the strategy shifted to automating the majority and specialising the few, consolidating human support to a single tier reserved for complex cases.

Roadmap prioritisation

To move product decisions from opinion to objective evidence, I introduced a prioritisation framework to the design roadmap. Using RICE scoring, MoSCoW analysis, and Impact vs. Effort mapping, every UX initiative was evaluated against business value and feasibility before entering the backlog. When user needs conflicted with business objectives, the framework made trade-offs visible, giving stakeholders a transparent basis for prioritisation rather than competing intuitions.

The solution

Omni-Channel routing

I re-designed the support journey built on Salesforce Einstein Omni-channel with smart routing integrated. Instead of connecting customers to an agent, the system captured intent before connection. Customers typed or spoke their issue; keyword analysis and classification triggered automated flows that either resolved the case through self-service or routed it to the appropriate specialist.

The structure was consolidated into a single line, reserved for non-English speaking markets like Spain and France where infrastructure was still maturing and automation was limited.

Automating customer requests with Einstein - detecting query type and routing to the right support group, reduced resolution times and improved user satisfaction.

Smart forms for agent capturing of information

To eliminate the double-call problem at its source, I customised the Salesforce Lightning Console with pre-built forms. Front-line agents were guided through a self-explanatory form to capture every data point during the interaction with no exceptions. Completed data was immediately available in the technical team’s queue, giving specialists full context. Follow-up calls became rare.

Accessibility remediation

I led a full accessibility programme across all customer-facing touch-points, spanning the sales, installation, account management, and payment flows. This ensured the platform met WCAG compliance standards across markets and user groups, and embedded accessibility as a design requirement.

Impact & business value

Results were validated across the full two-year contract period.

Quantifiable outcomes

• Support team reduced from 30+ agents to 5 specialists handling complex escalations only; all other query types resolved through automation
• 80% of support tasks fully automated, resolving without human intervention
• 98% automation success rate achieved on automatable query types
• UX initiatives tied directly to KPIs, CLTV, and retention metrics through the prioritisation framework
• Double-call eliminated through structured Salesforce Lightning forms

Qualitative outcomes

• A data-driven prioritisation framework shifted roadmap from subjective stakeholder opinion to evidence-based decision-making, giving the product team a process for evaluating design investment
• Accessibility across all touchpoints established compliance as a design standard embedded into the product lifecycle.
• The single-tier support model created a leaner, more focused team with higher case complexity and clearer criteria