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

Our Video

Our Original Application Video

And here’s the video I put together back in January 2020 for our original Nesta CareerTech application, to give you the context on what we planned to do and how far we’ve come…

Our Written Application

1. Executive Summary

We are Would You Rather Be.

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

We divided our time during the prize between learning, experimentation and implementation phases. We spent months understanding our users’ challenges and learning from their insights, then rigorously testing different solutions, before measuring the impact of our final product.

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

Accessibility & Usability

We consciously designed our solution to be simple and beautiful, with nudges, gamification, and delightful touches throughout. We designed with accessibility in mind — especially for visual impairment and dyslexia — and our solution is well-optimised for all devices and operating systems.

Market Potential

Our business model is B2B — selling software access to DWP prime contractors for NRS-focused projects, along with schools, universities and colleges. The UK secondary schools market, for example, represents a £4m/year opportunity.

2. Innovation

Since becoming a Nesta finalist, we have interviewed 104 people, conducted 18 product experiments and tested with 23,192 users. We have learned that our users’ most challenging problems are:

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

Pathways Feature

The most innovative, USP feature we built over the last 9 months is personalised Pathways; tailored to-do lists of every step a user needs to take to change into a career from where they are now. This combines an unparalleled number of LMI sources in one place, automatically generating actionable IAG, ranging from qualifications to work experience, to joining professional bodies.

How it works

A user is presented with multiple pathways for each career. As they explore each pathway, they are given a list of required and optional tasks including the duration, cost, next steps and funding.

Skills Matching Feature

This new feature, powered by ESCO’s dataset, addresses one of the key struggles highlighted in our NRS user interviews: finding careers matching their skills and experience.

How it works

We exploded every career a user enters into the requisite skills with varied weightings. We then built an algorithm that matches these to the skills required for each of their final results careers — producing a skills match percentage. For context on complexity, we map 29,000 skills and 115,000 career-skill pairs.

Interests Mapping Feature — Artificial intelligence (AI)

To help users identify a career they would enjoy, we match them with careers they’d be most interested in. To do this, we’ve developed and applied our own AI to existing LMI data to match users’ interests to careers — a feature which gets smarter over time. During the prize, we’ve had 5 iterations of our AI algorithm.

How it works

The essence of our app is a series of ‘Would You Rather Be’ questions, asking the user to choose between two careers.

Labour Market Information

Local LMI Feature

One additional NRS concern was finding local jobs. To solve this, we integrated Adzuna’s API to show nearby jobs for their careers of interest, using their postcode.

Demand Labels Feature

Career concern was very real for many of our users, who were in high-risk jobs due to automation and Covid. We, therefore, developed labels on the current and future job demand for careers, covering:

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

Our Sources of LMI

We only use freely available datasets, with open government or creative commons licences. These are listed in our Terms, meaning all our sources of LMI are legal and ethical.

Career Adaptability

Ensuring users have agency is central to our app; whilst we implement IAG for all outcomes, all decisions rest with the user. This empowers users, putting them in control of their career choices and improves career adaptability.

Existing Solutions

For Interests and Skills Matching, almost all other products are based on Holland’s 6 personality types from the 1950s, which calculates results through 10,000 computations. As we’ve recently seen with the negative press surrounding the Government’s careers quiz — these do not offer genuine user value. We built our model from first principles. Each time someone uses our app we analyse more data points than any other, including interests, qualifications and skills, making us unique in the market.

Future Plans

We’ll continue to enhance the AI in our algorithms with the structured data we capture from users and will implement it in other ways. For example, we plan to apply AI in a new innovative way to improve our career selection algorithm over the next two months. We also hope to trial Nesta’s Mapping Career Causeways product for our Pathways and demand labels.

3. Insight & Impact

The impact goal of our Theory of Change (ToC) is for people to have greater satisfaction and happiness in their jobs. We see this as central to our mission because career happiness has a substantial positive impact on mental and physical health, productivity and retention — it’s a win for our users, employers and for Government. If we can help people get into careers they want to stay in, our research shows that their happiness increases by 40% — and how we solve this is our Nesta solution.

Learning Phase: Insights from 50+ Interviews

At the beginning of the prize, we interviewed 53 users and customers to gain insights into their needs, motivations and requirements so that we could apply the learnings to our solution. Once we built our solution, we continued to test it with users iteratively, conducting a further 29 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

Over an 8-week period, we expanded our MVP by running 18 product experiments — that’s an experiment every two days. We expanded our testing to over 14,000 users so that we could learn as much as possible from their insights and test our impact decisively.

  • 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

These experiments led to the creation of the feature set now live in the app — our final solution — as outlined in question 2.

Survey Impact Results

We had 4,133 users take our quiz, of which 3,396 (82%) were either unemployed or unhappy in their role — our overarching target users. This included 1,196 NRS users, of which 1,026 (86%) were either unemployed or unhappy in their role — our specific target users.

  • 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

Our users are the same as our beneficiaries — people who want to start a new career. We have three broad user profiles:

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

How Users Engage

We have attracted users to Would You Rather Be through proactive ads delivered on Facebook. As so many people struggle with finding and getting into a suitable career, our ads resonate well and we are able to acquire users for a very low cost. Most of these users interact with our product on their smartphone, so our website is well optimised for mobile, as well as desktop.

Ease of Use

Here’s what our app looked like, back in March:

New Visual Design: Simple & Beautiful

We worked with professional user experience (UX) and visual designers to rebrand and redesign our site from the above. We started by going through a rebranding exercise. This involved:

  • 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


Since the initial 53 user and customer interviews, we’ve conducted a further 29 user interviews, with the majority being part of the NRS cohort. These have been primarily over Zoom, to test prototypes and our solution as it evolved. We acquired these users from a variety of sources, including our own app, existing communities within Facebook groups and local job clubs.


We use gamification in the UX by asking a series of fun questions during the career discovery process, and by showing a countdown of required tasks they need to complete to get into each career. We use nudges and a progress bar throughout the quiz to provide glimpses of their results and to encourage them to complete the quiz. We also apply delightful touches, such as confetti to celebrate reaching the final results or completing a pathway. To date, 5,013,250 survey questions have been answered, validating our approach.

Meeting our Users’ Needs

We have been robustly measuring the engagement of our users throughout the process of building our solution to ensure it meets their needs. We have done this qualitatively through testing our solution with users over Zoom, and quantitatively by measuring engagement metrics in the app. Our learnings from this have directed our product strategy, improved our solution and validated that it is meeting our users’ needs.

Engagement Metrics

Since finalising our solution, we have robustly tested engagement with 4,133 users within the app (1,196 were NRS).

  • 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

We’ll focus on improving accessibility for users from a diverse range of backgrounds, and intend to implement recommendations for visually impaired people as soon as possible.

5. Market Potential

Business Model

Our primary business model is B2B. We plan to sell access to our software to DWP prime contractors so that we can continue to support the NRS cohort at a wider scale. In parallel, we plan to also work with schools, universities and colleges so that we can build a sustainable cash flow while we grow and cement our longer-term networks with prime contractors.

Three business models we tested and deprioritised

We started by exploring monetisation with employers, by charging them to access talent. We interviewed 17 recruiters, 10 who hired for entry-level roles (which aligned with our audience of career changers). During these interviews, we identified five direct commercial opportunities, but we learnt that we would need a large, engaged audience to begin to create sufficient value here.

B2B business model — Our primary focus

Our most promising business model is to sell access to our software to businesses who work with users that are planning to start a new career. The most commercially viable, sustainable target customers are:

Sustainable Commercialisation

We believe our target markets of training providers, schools, colleges and universities are large and highly sustainable. Each market is a multi-million pound annual revenue opportunity in the UK alone — and is magnified further as we consider expanding to the US and then globally.

DWP Prime Contractors

DWP has 28 prime contractors who each secure multi-million pound contracts to deliver services to users. Some of this provision is focused around careers IAG, which we deliver. So if we capture just a small percentage of the overall budget these contractors receive, it could represent a multi-million pound per year opportunity. Crucially, this will mean we can continue to have a significant impact with NRS users in a sustainable way, as this is the target user for many contracts.


Affordability to Users

Our goal is to democratise career happiness and support. We want everyone in the world to find career happiness, regardless of their income. We know from our research of talking to 52 users, especially those in the NRS cohort, that most don’t currently pay for careers IAG and many would struggle to afford to do so.

Post-Nesta Product Roadmap

We will complete Pathways for the remainder of careers by the end of April. We’ll also tailor our solution to better meet the needs of specific cohorts like school students and university leavers.

Scaling our Solution Post-Prize

We’ve already achieved a fair scale: in the past 14 months, we’ve had over 51,000 users complete our career quiz (each answering 50 or 100 questions). We achieved this by proactively reaching out to users with our own Facebook ads. As the need to discover the right career is so strong for people in the UK, our ads really resonate with them. This has led to extremely low acquisition costs. These were very broad ad campaigns targeting everyone in the UK between the ages of 25–44, so we are confident they will continue to perform well and would scale to hundreds of thousands or millions of users.

Our Vision

We believe that everyone deserves to be happy in their career, and we want everyone in the world to use our software to help them find career happiness.

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).


  • 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

Please zoom in to view.

Indicators & Targets

Please zoom in to view.

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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