PI-24.1: Complete MVP

PART 1

Customer/User Job-to-be-Done or Problem

MVP JTBD: As a curriculum director or program owner, I want to understand what occupations my portfolio of offerings prepares learners to do, and how my institution can quickly adapt our offerings to align with promising market opportunities (occupations) in my area. 

Usability limitations in the current tool limit users’ ability to accomplish their job to be done and use the Skillabi-in-Analyst MVP effectively. Specifically, table packet data bugs, difficulty for the user to view the information most relevant to them, and challenges navigating the many filters limit the ability for institutions to use the tool with ease.

Value to Customers, Users, and Lightcast

Customers/Users: 

• The Skillabi-in-Analyst MVP will help institutions develop and strengthen academic programs to align with skills needed for promising career opportunities in their area, ensuring programs are valuable to employers and helping learners gain the skills needed for career success. 

• Usability improvements addressed in this epic will help customers get maximum value out of the MVP by enabling them to:

  • Successfully access and use data 

    • By fixing bugs found in table packets within the tool

    • Ensuring that inputs are “sticky” from page to page, to lock user selections

  • Navigate throughout the tool easily and clearly

    • Clarifying which filters apply to occupation data, program data, or both 

    • “Favoriting” or saving occupations to prioritize and come back to

    • Searching a specific skill to add it to the program or find it in occupations

  • Set preferences specific to their institution or user’s needs

    • Allowing users to organize their programs by college or department, so they can see only those relevant to their unit

    • Allowing users to set their preferences for how occupation-to-program skill alignment is calculated

Lightcast:

• Increased Reach and Revenue: Skillabi 2.0’s increased accessibility for institutions and focused use case will allow Lightcast to reach a greater number of institutions who currently struggle to address the core problems that exist with the original Skillabi. Addressing usability challenges in the MVP will provide users with an improved experience and derive greater value from the tool, resulting in a higher likelihood to purchase.

• We anticipate the following revenue projections from renewals of current Skillabi + new sales from a planned Q1 launch of Skillabi-in-Analyst (see revenue estimate calculations):

  • Projected Skillabi-in-Analyst cumulative new sales by EOY 2024 =  ~$500k (ranging from $250k worst case - $1M best case) 

  • Projected Skillabi cumulative renewals by EOY 2024 = $535k (ranging from $353k worst case - $704k best case)

    • Oct 2023 ARR for Skillabi is $1.3M

  • Projected Cumulative Total Income from Current & New Skillabi by EOY 2024: $1.035M (ranging from $0.6- $1.7M) 

• From a feature perspective, the ability for our Lightcast team members to be able to easily switch sites will enable us to set up sites for prospects as we need them to support conversations, and support multiple current clients by viewing their data easily.

Success Criteria & Metrics

Definition of Done, PI-24.2: Address urgent product functionality and usability needs, to enable institutions to engage with data relevant to them and effectively work within the product MVP. 

• Development Priorities:

  • Cleanup / Bugs - Table Packet Data Issues

  • Add College or Department Filter

  • Add Occupation Skills Filter

  • Add Occupation “Favorites”

  • Enable ability for internal users to switch sites easily through the admin

  • Emergency Fund LOE Buffer*

• Secondary Priorities: 

  • Expandable input categories

  • Skill Search on Create and Edit Program Page

  • Editable College and Type Input

  • Advanced Market Alignment Settings

*PI-2 will take place throughout the MVP’s soft launch Pilot with institution customers. It is crucial that Lightcast is able to quickly address unforeseen challenges and respond just-in-time while we receive feedback from users during this period. Therefore, prioritizing some LOE (e.g., 6-10 LOE) as “buffer” would be beneficial; if this buffer ends up not being needed, we can proceed with tackling secondary priority issues outlined in this epic.

Success metrics 

PI-2 occurs throughout our 2-month Pilot with early adopter institutions. 

• Rolling out fixes during this time period will enable us to quickly get feedback from pilot users. 

• Completing all issues within this epic will set us up to move from pilots to selling the product, such that core navigation and usability challenges will be resolved.

The following are defined engagement metrics that we will monitor to assess pilot success, and the functionality we will put in place to track engagement.

• Types of metrics/feedback we will track:

  • Engagement with aspects of the tool (e.g., page views, weekly active users or accounts, repeat usage, use cases and workflows [qualitative])

  • Satisfaction (e.g., Net Promoter Score, level of effectiveness at supporting users in accomplishing goals, impact for the user on [time saved, prioritization decisions made, etc])

  • Functionality pain points & delighters (e.g., ease of navigation, confidence in knowing how to use the tool, challenging features, most loved features, functionality wish list, functionality deal breakers)

• Engagement tracking approaches and tools used to capture metrics: 

  • Pendo, to monitor page views, interactions in the platform, level of engagement, usage patterns and trends, etc.

  • PlaybookUX, to asynchronously capture feedback by recording the user’s interactions in the tool and their live reactions while using.

  • Qualitative feedback collected via 1-1 conversations and email correspondence with users periodically throughout pilot period, as well as potential group conversations

  • Survey via Google Forms or similar to supplement interview/meeting feedback with quantitative data 

• The pilot period will be used to inform the level of success of the product in the current MVP state, and where changes are needed before rolling out via full launch. Product success at this stage would mean…

  • High Adoption: Pilot institutions use all primary features of this software. Upon completion of the pilot, they choose to purchase an annual license of the product.

  • High Retention: pilot institutions using the software on a regular cadence throughout the pilot, indicating that they will find continual value and are therefore likely to renew.

  • High Satisfaction & Impact: pilot institutions express the value of the software and the positive impact it has had - or that they expect it will have - on their ability to accomplish central goals.

Aspects that are out of scope 

NA

PART 2  

Early UX (wireframes or mockups)

Figma - MVP

Non-Functional Attributes & Usage Projections

n/a

Dependencies

Potential dependencies on the Talent Transform team to support executing changes to the API when adding/modifying features such as the new College/Department filter.

Legal and Ethical Considerations

No legal or ethical concerns.

High-Level Rollout Strategies

• Go-to market plan [see details]

Risks

• Potential risk of bugs or concerns surfaced by initial users, due to this being the first release of the entire tool and first experience with customers engaging live in the tool. We will need to be able to react to these possible issues quickly and with the resources to do so.

• These revisions will be made throughout the pilot, where users are engaging live with the tool. Product needs to effectively communicate with customers that this MVP will include frequent live updates, and has the potential to change fairly quickly as it evolves so they are not caught off guard.

Open Questions