MD Portfolio

case study

Automated Profile Setup Assistance

This project was truly cross-collaborative as its success spanned across 3 departments. Our team worked to streamline the user profile update process for experts, increasing their chances of effective matches. This was achieved by reducing the time it took to complete a profile and improving completion rates. Success was measured by tracking the impact of completed profiles on SOW wins. Our full UX team participated by diagramming the current process in a service design blueprint.

What problem were we solving?

  • The onboarding team was being over-taxed with new freelancers joining the network every week. Resource funding was unable to be properly forecasted.
  • Operations teams were stuck waiting with email requests to Experts asking for their effort in completing their profiles. Complete profiles are the only way the Experts can become active. 
  • The Matching App and sales teams alike required complete profiles. 
  • The existing onboarding UI was pretty bad.

Figma prototype demo

Impact and ROI

  • The Expert profile completion time was reduced from 14 days to 10 by providing a checklist of items that needed to be completed before they could proceed.
  • The percentage of completed profiles among existing Experts was also improved from 68% to 83%, a significant increase supporting their chances of getting matched at a higher rate.
  • Internal teams were immediately relieved of time-taking tasks that were now automated each time the Expert logged into the platform. Alleviating the team and Expert of unnecessary email correspondence.
  • Resources within a single department were reduced by 3 persons.

Constraints & Trade-offs

Impact to Matching App
The Matching App algo references inputs from the Expert profile. The way in which it weighs these values at the time of the match drove the order in which we requested the information within the workflow. For example, past work experience is an area that rarely changes and it’s automated upon registration using a PDF resume. Allowing that tab to be pushed toward the end of the flow kept the expert focused on entering new data. Due to this dependency, we worked closely with the developers of the Matching team.

Data Governance Ruling
Our proposed concept included reworking the way we categorize past work experiences. The rework was going to require backend data table changes that were not approved by the greater committee. The denial caused us to scrap a quarter of our final prototype.

UX Insights

Our goal was to achieve positive business outcomes by offering efficient product support, which ultimately reduced the costs of hiring experts. Our commitment to providing reliable product support also enabled experts to increase their earnings. We were confident that these measures would enhance overall performance and help us successfully achieve our objectives.

  • internal teams (sales, onboarding, operations) were all frustrated with the lack of profile completeness
  • experts lacked a cohesive workflow guiding them through the necessary stages for proper profile setup
  • enablement needed a more thought-out user experience to better support the expert

Determining User Needs

As an expert, it is essential to understand what updates are necessary and the reasons behind them. Additionally, they require a user-friendly way to modify their profile efficiently. It is crucial experts are able to make changes to their profiles that can enhance their chances of being matched more effectively. Therefore, in addition to having clear indicators that guide experts to the appropriate sections of the platform for making relevant updates, a simplified architecture of information is essential.

  • Experts are unaware of profile update requirements and how that affects their win rate
  • Make it easy for Experts to make changes to their profiles for effective matching.
  • Provide clear indicators that guide experts to the right sections for making updates.
  • Use a simpler layout of information for a better user experience.

 

UX Tasks –

  • Diagram the process in place – determine problems needing to be solved
  • Research the user’s needs, and understand the business needs
  • Map out the dependencies, determine user task priorities for each screen
  • Understand the feature’s impact on the entire funnel and strategize the best approach
  • Determine the depth of enablement required for the release

Iterative Level

  • Wireframe feature for stakeholder review and feedback
  • Prototype design for user testing and leadership review
  • QA production feature on desktop and mobile views

UX Metrics tracked

  • Feature engagement by service line
  • Time to complete tasks
  • Team vs individual engagement