Elsevier

Designing a smarter, more integrated research platform

Services

Industries

Selling information and analytics

How to leverage existing products to create new value

  • Challenge

    Elsevier, a global information analytics business, wanted to leverage their existing suite of products, content, capabilities, and data to create a new product.

  • Solution

    Method designed a new product experience that manifests Elsevier’s updated value proposition and better supports the research community and its processes.

A new product addresses current needs and anticipates the future

  • Results

    Elsevier is a global information analytics business specializing in science and health. Combining content with technology, supported by operational efficiency, Elsevier helps institutions and professionals turn information into actionable knowledge, improving performance for the benefit of humanity.

    Following a period of acquisition and growth, the time was right for Elsevier to take a holistic view of their offering. With disruptions in the research and scientific community including the rise of Open Science, free access, and free tools, Elsevier’s challenge was to leverage their existing suite of products, content, capabilities, and data to create a product that adds substantial value to researchers, funders, and stakeholders.

    Method designed a new product experience that manifests Elsevier’s updated value proposition and better supports the research community and its processes. This new platform and portal leverages Elsevier’s existing suite of products, content, capabilities, and wealth of data to create a smarter system that not only supports but anticipates the needs of various research facets.

Discovering and delivering new value

  • Designing with real data

    Designing with real data was a key component of this project since the target audience was very attuned to academic material and were easily distracted by placeholder content or inaccurate text and numbers.

     

    Designing with realistic data had its set of challenges. It takes time to gather data and ‘anonymize’ it. We created simple frameworks to facilitate data collecting and streamlined the process internally to make sure we could easily swap data sets to generate a prototype for each scientific domain with minimum effort. The end prototype was built entirely with real content and its fidelity was also tested to ensure the right level of information was shown.

  • Collaborating with diverse stakeholders

    The project had eight key stakeholders – each a subject matter expert in a different segment of academia (researchers, institution managers, funding bodies). At the start of the project, their vision was disjointed and early conversations highlighted gaps and divergent agendas.

     

    By inviting all key stakeholders to actively take part in our process of discovery, exploration, definition, and refinement, and by sharing the progress and insights to all at every key stage of the project, we created alignment on the vision and experience.

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