We are runner-up in Nesta’s CareerTech prize!

Our Video

Our Original Application Video

Our Written Application

1. Executive Summary

Innovation — Our Top Features

  1. Personalised Pathways is our USP feature — tailored to-do lists of every step a user needs to take to change career, including time, cost and funding. This is unique in the market, combining careers and Labour Market Information (LMI) to automatically generate actionable guidance.
  2. Skills Matching — our algorithm instantaneously performs half a million computations to generate a user’s skills match to careers, giving insight into their transferable skills for each career.
  3. Interests Matching — our algorithm suggests careers for people. We’ve developed and applied our own AI to existing LMI data, so this feature gets smarter over time.

Insight & Impact

  • access more careers information, advice and guidance (IAG),
  • access IAG tailored to them personally, or
  • expanded their career horizons.

Accessibility & Usability

Market Potential

2. Innovation

  1. Finding a new career matching their skills, experience and interests
  2. Moving into that career

Pathways Feature

How it works

Skills Matching Feature

How it works

Interests Mapping Feature — Artificial intelligence (AI)

How it works

Labour Market Information

Local LMI Feature

Demand Labels Feature

  • Lots of jobs
  • Affected by Covid
  • Future proof
  • Not future proof

Our Sources of LMI

Career Adaptability

Existing Solutions

Future Plans

3. Insight & Impact

Learning Phase: Insights from 50+ Interviews

  • Improving our Interests Matching algorithms using AI
  • Improving our career coverage
  • Building our Skills Matching feature to enhance career adaptability and show realistic options
  • Adding basic LMI like job descriptions, salaries and day-to-day tasks
  • Adding innovative LMI through our job demand labels
  • Adding a “Helping hands” marketplace section to our app with links to partners that offer work experience opportunities, mentorship, coaching and conversations with professionals. Please see more on this in Appendix page 35.
  • Developing our Pathways feature, giving users a to-do list of steps to get into each career of interest
  • Time and cost information into each career
  • Integrating Skills Matching feature into Pathways, along with experience and qualifications, helping to bridge that gap
  • Nearby Jobs feature shows the open roles, highlighting entry-level jobs first

Experimentation Phase: Insights from 18 Product Tests

  • 50% of users are exploring careers, rather than proactively trying to enter that career
  • The most common challenge at this stage is not knowing which career to pursue (75% of users)
  • The most popular of our features was mapping skills and qualifications to careers (>60% of users).
  • The average happiness of people who wanted to stay in their careers was 40% higher than those who wanted to change careers.
  • To show users nearby jobs, we collected their postcode and sent them to Adzuna. 42% of all users clicked on this link — exceeding our goal of 10% by more than x4. We signed up to Adzuna’s affiliate program to monetise these clicks and integrated with their API to show jobs nearby directly in our app.
  • To show users relevant courses, we sent them to Udemy. This proved popular too, so we signed up with Udemy’s affiliate program to monetise these clicks.
  • To maximise impact for NRS users, we wanted to provide as many free courses as possible. So we established a commercial partnership with the Learning Curve Group, one of the Department for Work and Pensions’ (DWP) 28 prime contractors. They provide free training courses to all users, giving them a GCSE-equivalent qualification. We mapped their library of 50 courses to our careers database and show users free, relevant courses for each career. These proved popular, with 6.6% of users who saw a course, clicking on one (goal of 5%).

Implementation Phase: Our Impact

Survey Impact Results

  • 335 vs goal of 300 said yes
  • 223 vs goal of 150 said yes
  • 257 vs goal of 150 said yes
  • 93 vs goal of 60 improved their expectations

User Interview Impact Results

  • All users loved Pathways and the level of personalisation. Many said they had never seen another product like it
  • All users felt that the time, cost and steps in Pathways allowed them to make realistic decisions
  • All users found the website very easy to use
  • Quotes we received through our email survey:
  • Improvement in employment results
  • Job retention
  • Job security
  • Salary

4. Accessibility & Usability

Our Users

  • NRS cohort (primary focus — product works particularly well with this group);
  • Working adults with degrees;
  • Students aged 16–24.

How Users Engage

Ease of Use

New Visual Design: Simple & Beautiful

  • Defining personas
  • Identifying our company characteristics (which are trusted, approachable, adventurous, optimistic, imaginative and fun)
  • Ideating and agreeing on idents, colours, typography, a high-level design, a new logo and a strapline

Accessibility

Engagement

Meeting our Users’ Needs

Engagement Metrics

  • 60% of users who reached the final results went on to explore at least one career (vs. our goal of 30%) — 2,467 users
  • 28% of users who had at least one career with pathways enabled went on to view at least one pathway (vs our goal of 10%) — 1,052 users. This was particularly high as we only support 16% of our careers with Pathways today
  • 16% of users who viewed a pathway ticked at least one task (vs our goal of 5%) — 170 users
  • 30% of users who viewed a pathway clicked on at least one button to take the next step (vs our goal of 5%) — 319 users
  • 26% of users who explored a career viewed jobs nearby (vs our goal of 10%) — 647 users
  • 24% of users emailed their results to themselves voluntarily (vs our goal of 10%) — 977 users. This was particularly significant as it yielded an improvement of 300% over our pre-prize solution, which was at 8%, showing the improvement in relevance and value we have created during the prize

Ongoing Engagement with Users

5. Market Potential

Business Model

Three business models we tested and deprioritised

B2B business model — Our primary focus

Sustainable Commercialisation

DWP Prime Contractors

Schools

Affordability to Users

Post-Nesta Product Roadmap

Scaling our Solution Post-Prize

Our Vision

6. Appendix

Glossary of Terms

Career Acronyms

  • DfE: Department for Education. Government department responsible for child protection, education, apprenticeships and wider skills in England.
  • DWP: Department for Work and Pensions. Government department responsible for welfare, pensions and child maintenance policy.
  • ESCO: European Skills, Competences, Qualifications and Occupations. Multilingual classification that identifies and categorises skills, competences, qualifications and occupations relevant for the EU labour market and education and training.
  • FE Colleges: Further Education Colleges. They provide technical and professional education and training for young people, as well as adults.
  • IAG: Information, Advice, and Guidance. This relates to the careers input we give our users when it comes to helping them with their careers.
  • LMI: Labour Market Information. For the purposes of this Nesta application, this is defined as any data that can be used to better understand the labour market (jobs, industries and the economy)
  • NCS: National Career Service. The publicly funded careers service for adults and young people (aged 13 or over) in England. Their website provides information, advice and guidance on learning, training and work.
  • NRS: National Retraining Scheme cohort. This is the target group for Nesta’s CareerTech prize, which is defined as “adults who work in insecure roles, are over the age of 24, without a degree qualification and based in England. These workers may be employed, furloughed or recently made redundant due to rapid labour market change, but should not be long-term unemployed.”
  • ToC: Theory of Change. Our theory for how our solution will have a positive change in people’s lives.

Business Acronyms

  • B2B: Business to Business. A business model describing how a company makes money by selling something to another company
  • B2C: Business to Consumer: A business model describing how a company makes money by selling to consumers (people)
  • Freemium: A business model that involves offering customers complimentary (free) and extra-cost (paid) services (combines the words “free” and “premium”)
  • USP: Unique selling proposition

Tech Terms

  • AI: Artificial Intelligence. For this application, and for Would You Rather Be, this is broadly defined as using large past datasets to enable software to generate more intelligent outputs.
  • API: Application Programming Interface. This is how two computer applications or systems talk to each other.
  • MVP: Minimal Viable Product. This describes the first version of a product with a minimal feature set that a user might find useful.
  • UX: User Experience. This describes the experience a user has when using a product (e.g. as they interact with Would You Rather Be).

Features

  • Pathways: These are tailored to-do lists of every step a user needs to take to change career, including time, cost and funding
  • Skills Matching: Matching a user’s past careers to generate a skills match score for each of their careers of interest
  • Interests Mapping with AI: Using past data of the answers users gave to the quiz questions, as well as their quality and career selections to make our interest-matching algorithm more intelligent
  • Local LMI Feature (“Jobs Nearby”): Show jobs near a user for their careers of interest, powered by Adzuna’s API
  • Demand Labels: These are labels we attach to career cards in a user’s final results, indicating current and future demand for jobs (e.g. “Affected by Covid” or “Future proof”)
  • Marketplace (“Helping hands”): These are sections below a user’s final results and at the bottom of the “Explore” page for a career. Our Marketplace is the Airbnb of careers; we provide personalised suggestions to hyper-relevant partners who directly support our users into jobs and training such as coaching, mentoring, work experience opportunities and help with CVs & interviews. Doing so maximises user value and raises the whole CareersTech industry.

Theory of Change

Indicators & Targets

Our Nesta Journey: Timelines of Prize

User Flow: Our end-to-end Solution

Our Impact Surveys: Before & After Using the App

Media Coverage & Blog Posts

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Tech for Good Entrepreneur

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Phil Hewinson

Phil Hewinson

Tech for Good Entrepreneur

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