Transformation Planner

Leveraging data to find the best case for electrification

  • Year: 2024
  • My role: Product designer

Problem

  • Transitioning from diesel to electric at scale is complex and we need to  process large amounts of transport data from customers.
  • Without proper software support and algorithms, this process becomes time-consuming and inefficient.

The impact for us was that each customer sales process was expensive (on average 8 months) and labor-intensive. The manual process also made it difficult to scale and improve.

Together with the business analysts, we set out to build a tool that would standardize their workflow, support them in analyzing large cases with data from multiple customers, and algorithmically identify the best potential for electrification.

Solution

  • A solution that seamlessly help business analysts move from customer data in google sheets to an overview of a transport network in a UI.
  • Algorithmic support in finding areas with high density of transport demand.
  • Easy to use tooling for high level analysis and support down to the nitty gritty details.

Outcome

  • From spending days on manually processing, the same task could be completed by our users in minutes (depending on the quality of the source data).
  • A seamless flow from raw data in Google Sheets to getting a first overview of the opportunities.
  • Positive response from the users.

My role and contribution

  • Collaborated with the Product Manager and Engineering Manager on product discovery and established an iterative feedback loop between the development team and our internal users.
  • Worked closely with the product strategy team to ensure we prioritized functionality that would meet both current and future user needs.
  • My main contribution was leading user research and facilitating a user-centered approach, where we focused on identifying clear problems and, together with the development team of engineers and data scientists, explored multiple solutions.

Learnings from this case:

  • Even internal, data-savvy users are not immune to poor user experiences. The value of powerful features is diminished if they are not well-crafted and intuitive to use.
  • A collaborative approach is highly effective when working with internal expert users. Co-creating solutions in a way that would be less feasible with external users builds trust, fosters empathy, and—most importantly—establishes a strong, positive team culture.