In July 2018, I had the opportunity to work with a not-for-profit start-up that wanted to make a difference in the competitive health insurance market.

Care To Compare offer an ethical alternative for health comparison while supporting charities who work actively to relieve the burden of disease in Australia. 100% of their profits will be donated to these charity partners.

Working closely with three other designers, each of us worked on multiple aspects of the project. While we all shared in user research and synthesis, ideation through to , I specifically looked after the data modelling and value stream mapping tasks.

Duration: 14 days


Techniques and tools

  • User surveys
  • User interviews
  • Heuristics and usability testing (v2 prototype)
  • Data synthesis
  • Affinity map
  • Mental model
  • Competitor analysis
  • Feature prioritisation
  • Online research
  • Card sorting
  • Sitemap
  • Data model
  • Value stream map
  • Design Studio
  • Prototyping
  • Wire-framing
  • Usability testing

The opportunity

During the project kick-off, we spent time with Care To Compare’s founder Roberto and a member of his team Felicity, to understand their requirements and identify the success criteria and scope of work.

In our meeting, we identified that Care To Compare already have a live marketing website, a minimum viable product, and are looking to launch in six months. Importantly, their v2 prototype already went through an iteration based on preliminary user feedback they obtained prior to the project.

Our main objectives were:

  • To validate and recommend changes to the v2 prototype, and
  • To establish personas that will help test and measure our prototype

The outcomes from our engagement will inform the development of their market ready platform.

Research and Synthesis

In the first week, we looked at analysing Care To Compare’s previous research to the extent that we generated new and creative insights. We also conducted a survey participated by over 100 respondents, 6 user interviews and usability testing with a separate set of 6 users.

Key insights from the previous research, user interviews and survey were:

  • Each user had different circumstances and needs, but ultimately all wanted to accomplish the same task: to buy the correct policy that fits their needs.
  • Even for ethically minded users, ethics was not a primary motivator when searching for a policy (no mental connection).
  • Working within the constraints of being a completely web based enterprise, we realised we needed to leverage strengths of the platform and why people use web based comparison sites: ease of use.
  • As a result, we decided to focus on providing the best possible web experience based on user needs, with ethics as a marketing bonus.

Key themes from the usability testing were:

As part of the research, we conducted an extensive competitor analysis using the following principles:

  • In depth view of competitors, paying attention to common elements and their strengths
  • Overview of competitor ethos, key points, heuristics and points of interest
  • Analysis of companies with a similar operating structure, their goals and impact, and how they accomplish what they do

Direct competitors that we looked at were: iSelect, Members’ Own, Compare the Market, Finder and Canstar.

Indirect competitors included: Thank you, Who gives a crap, Tsuno, Toms, and service delivery platforms: Ecosia and Humble Bundle

Key findings from direct competitors —

  • Customers changed over their health cover if it didn’t cost anything, it was right for their needs and if it was easy to compare
  • Customers were engaged by plain language, price promise, expert knowledge, no mark up, and unobtrusive end-to-end assistance

More general findings —

  • Customers were provided a happy path to channel their social conscience towards a worthy cause
  • Customers were confident with the product or service because of the level of transparency they receive regarding how the business or social enterprise model works, i.e. How It Works, How It Runs page.
  • Customers understood their value proposition because it was clearly communicated and presented on their website


Through the insights from our research, we discovered that potential customers were not necessarily grouped based on living status (single, couple, family, empty nesters) or age as suggested in the previous research.

New Customer vs Returning Customer

Rather potential customers were more broadly grouped between first time policy holders and those who have or held a health insurance cover and are looking to change.

Reclassifying Data

Using a bottom-up approach, we looked at simplifying the search input process for the customer by structuring how health insurance data can be best stored and retrieved for comparison.

Using a cross-section of data that we scraped from sample SISs in the v2 prototype, visiting some of the insurance partner websites, and on the basis that the government are rolling out changes to simplify the policy groupings — we designed a data model with five basic entities:

Data Model: Search Engine
  • Insurer. Used to store insurance partner information.
  • Product. Used to store insurance policies based on type and level of cover offered by insurance partners in each state.
  • Benefit. Used to store a master list of insurance benefits each of which is associated to a type: hospital, extras, hospital and extras.
  • Product Benefit. Used to store insurance benefit records covered under each insurance policy.
  • Product Policy. Used to store different variations to insurance policies based on who they are for, excess factors and premiums.

Although we have not been able to fully validate the model, we had it reviewed by a software engineer who works in that space.

For scalability, the data model provides the most efficient way to administer the addition of new policies and insurance providers on the platform.

To improve findability and affordance for the customer, we ran a card sort exercise to organise and label the insurance benefits.

  • In our first iteration, we conducted an open card sort based on heuristics of word/term associations. From this, we tagged the card sort into meaningful and plain language headings.
  • In our second iteration, we conducted a closed card sort by reverse engineering, i.e. we provided our (new) headings to an external person for them to sort the terms and categories under.
  • In our final iteration, we re-sorted/renamed the headings to further simplify the process, and attached questions to each category which would allow us to determine the policies to compare.

To enhance the navigation experience, we carried out a separate card sort of the existing marketing website and came up with our recommended sitemap below.

Clarifying Content

To address the themes that we have particularly uncovered in our usability testing, we mapped our for updating the website’s content and used this as guide in our ideation and brainstorming of revised content.

Content Strategy

Restructuring the Compare and Buy Process

With a fresh insight from our research, data model, and content strategy we proceeded to map the current process flow the customer takes to compare and buy with Care To Compare.

Through value stream mapping, we identified:

  • Unnecessary steps that can be removed or displaced to simplify the search and refine stages into one stage: start. With minimum input, the search engine can instead feedback data that was originally being asked of the customer and therefore reducing their cognitive burden.
  • Steps that should be moved to a later stage, where it made more sense for the customer within the context of the process.
  • New steps and current steps that required elaboration to inspire trust and improve transparency in the compare and buy process.

The data model enabled us to introduce filters and facets as a way of elaborating the steps in the compare stage of the process. This provided the option for customers to refresh their comparison results instantly without the need to restart the process. We also added a shortlist feature in this stage, which was something that came up in the user interviews and competitor analysis as a high impact — mid expected element of a comparison site.

Value Stream Map: Compare and Buy Process

In the new process flow, we moved the “select policy” step into a separate stage: choose — for clarity. This allowed us to introduce a product page with detailed information on the cover to help the customer make an informed decision. We added the email quote feature to give customers an option to exit the process but still go away with information they can use to go back to the system at a later stage when they are ready.

We found in our research that customers were likely to abandon the buy stage if personal information is requested without much context. Elaborating the steps and updating the content enabled us to address this concern, as validated in our usability testing.

The finish stage has been introduced to neatly complete the compare and buy process by providing customers information on what to expect next as well as the option to go back to the homepage.

Prototype and Design

When sketching our paper prototype, we started our design ideation process with:

  • Each member sketching their ideas for the compare and buy process
  • Group review and critique
Initial Paper Prototypes
  • Identified key features present across different versions
  • Voted on other features to determine key inclusions, and
  • Group sketch and development incorporating voted elements
Iterated Paper Prototypes

We tested our iterated version of the paper prototype with two participants obtaining generally positive results and minor refinements to the flow suggested including some refinement to microcopy.

We then moved our prototypes into wireframes and conducted further usability testing with 3 other participants obtaining affirmations on the improved flow and functionality. We applied further iterations to the UI like the use of symbols and placement of the shortlist before the final client showcase.

View Prototype

Source link—-eb297ea1161a—4


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