PART 1

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

As a Program Owner/Chair/Director, I want to see, with greater accuracy/confidence, what occupations my program aligns to based on its unique skills and strengths so that I can identify new avenues for program growth, confirm strengths, or realign weaknesses.

Value to Customers & Users

• Updated suggested occupations unlock new insights for owners/users based on the unique skill makeup of education offerings. This helps customers:

  • Advocate for continued funding

  • Guide students towards better career outcomes

  • Market offerings better to prospective learners

• Relevant matches give customers useful data and insights without requiring input from them first. 

• Will improve confidence in making decisions based on insights gained from Skillabi. Users can get started right away without second guessing the validity of occupations suggested.

Value to Lightcast

• Builds a higher level of trust in the quality of data provided by Skillabi. This helps remove uncertainty in the sales process, reducing the length of the sales cycle as well as improving initial reception with key stakeholders at the institution during implementation. Good first impressions improve the chances of Skillabi providing value and thus the chances of renewal. 

• Unlocks the potential for us to confidently make suggestions and be more prescriptive with our insights, leading to an improved customer experience and increased value from Skillabi.

Target User Role/Client/Client Category

Program Owners/Directors. To a lesser degree, this epic also supports academic leadership roles, such as Deans and Provosts, as well as Faculty Members.

Delivery Mechanism

The improved model will be used in Skillabi to improve the results of suggested occupation benchmarks for a program.

Success Criteria & Metrics

Definition of Done: Top Occupation Matches in the production version of Skillabi are displayed using the improved model. 

Success Metrics (Guesstimates at this point):

• Customers review and make new selections from their Top Matches >=10% more often

  • Baseline: About 20% of users who login review their Benchmarks

  • Need to add additional analytics to measure which type of benchmark is selected. Should be able to do so after changes made in PI-1

• Suggested occupations are “relevant” 95% of the time

  • This will have to be a qualitative measure reported by customers, or based on CIP Code

Aspects that are out of scope (of this phase)

We are proposing that initial research benign in PI-2 and further implementation begin in PI-3 or beyond based on research outcomes.

PART 2

Solution Description

Research:

Use training data available from Skillabi to investigate ways to improve the top occupations suggested for a given program. This may include, narrowing the skills included from the program by some factor to focus on “key” skills, improving the similarity model for curriculum, incorporating CIP Code/Program type into the model, or other avenues. Hypothesis can be tested against customer testimony, CIP Code occupation mappings, and qualitative skill analysis. 

Final Solution:

TBD - dependant on research outcomes

Early UX (wireframes or mockups)

N/A

Non-Functional Attributes & Usage Projections

N/A

Dependencies

N/A

Legal and Ethical Considerations

No legal or ethical concerns.

High-Level Rollout Strategies

• Customers will see “smarter” occupation matches when looking for benchmarks to compare against their program.

• Stretch: Customers will be able to see suggestions about which occupations to target based on their program’s unique set of skills taught.

Risks

• TBD

Open Questions

• Is CIP Code Mapping a relevant set of comparisons to benchmark matches against, or should we be looking to give results without the bias of a program code?

• How can we know when we have “accurate” matches objectively?

• How can we ensure a certain level of quality on skills data coming into the model?

Complete with Engineering Teams

Estimated Costs

Direct Financial Costs

Are there direct costs that this feature entails? Dataset acquisition, server purchasing, software licenses, etc.?

Team Effort

Each team involved should give a general t-shirt size estimate of their work involved. As the epic proceeds, they can add a link to the Jira epic/issue associated with their portion of this work.