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

32 min readMar 23, 2021

We’ve just been awarded the runner-up prize of £80,000 by Nesta and the Department of Education in the CareerTech challenge! Emma and I are thrilled and honoured to receive this prize.

Tris Dyson, Managing Director of Nesta Challenges, shared these kind words about our app:

“Would You Rather Be is an incredibly exciting solution. By presenting highly personalised insights and recommendations, this app can help people reimagine their futures, and plan their next move.”

“Even before the major employment concerns caused by the pandemic, automation was transforming the skills required for a whole range of jobs. Faced with this challenging employment landscape, it’s critical that workers have access to the information and tools they need to thrive. While our judges were hugely impressed by all the innovations put forward, Would You Rather Be was set apart by the wealth of insights provided to users, and their focus on career happiness — something which is often missing from the conversation about employment support.”

In celebration of the prize, we’re inviting everyone to explore their career options for free for the next month at our website www.wouldyouratherbe.com.

And in the spirit of transparency, I’m sharing our final prize submission video and full application (with a few small parts redacted) below. And here’s a PDF version of our final written application too.

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.

Our solution is an Artificial Intelligence (AI) powered career discovery app, to help people find and get into a future-proofed career they’ll love.

Our target users are the same as our beneficiaries: people who want to start a new career.

Innovation — Our Top Features

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.
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.
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.

In all, we have tested our solution with 23,258 users, ranging from interviews and job clubs to in-app and email surveys. Over the past two weeks alone, we tested our final product with 1,196 National Retraining Scheme (NRS) users. 83% of those surveyed said Would You Rather Be directly helped them:

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.

Since finalising our app, we have robustly tested engagement with 4,133 users within the app. 83% completed our quiz and we’ve had a 300% increase in users voluntarily emailing themselves their results.

We then conducted 18 interviews, in which every single participant said they found the app easy to use.

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.

As our solution is pure software, we can scale rapidly with marginal operational costs and very high-profit margins. This means we can achieve sustainability quickly and maximise our social impact.

Our goal is to democratise career happiness and support. Our app will be ‘freemium’, so that everyone will have access to our interests quiz. Other features, like Pathways, will be paid access so that we can scale sustainably.

The market is huge, with 54% of the 32m working adults in the UK wanting to change careers, and 800,000 entering the workforce every year as they leave education.

Everyone deserves to be happy in their career. Our mission is to make that happen.

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:

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

Our new features are specifically built to solve these by combining LMI and software in entirely new ways. We have rapidly innovated, tying in career adaptability principles throughout, and here is what we’ve created so far:

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.

We include the time and cost of each pathway to that individual user, taking into account funding, whether courses are part-time or full-time, and if tasks can be done in parallel. We display minimum time and costs for entry into that career in their final results so users can compare this information across careers.

No-one else on the market offers this level of functionality or uses LMI in this way — and the impact is phenomenal.

We’ve had over 4,000 users in the past two weeks alone, and over 50% of those surveyed said they now have a better understanding of pathways into a new career from the IAG we’ve provided. We interviewed a further 18 users, including the NRS cohort, and every single one said it was invaluable.

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.

Funding availability, for example, is hyper-personalised and factors in age, education, employment status, salary, location and the specific course to determine whether government funding is available. We specifically prioritised developing this following DfE’s Decisions of Adult Learners Report, which found that the support most useful for learners was ‘the availability and means of accessing financial aid’.

Next steps, too, are tailored to the user — their postcode is used to link to their local Further Education (FE) college for recommended vocational courses, for example.

We automatically tick off qualifications we know the user has (e.g. we auto-tick GCSEs if they tell us they have A-Levels). Users can tick off tasks as they go, which updates the time and cost remaining for all pathways into all careers in the app.

We’ve also integrated free, relevant courses from the Learning Curve Group, and low-cost courses from Udemy.

For our full user flow, please see Appendix pages 40–42.

IAG Methodology

Using NCS data as a jumping-off point, we generated IAG through deep-dives into each career. This involved analysing and validating IAG from hundreds of specialist websites, reports, videos, blogs, podcasts, and Facebook community groups for people in each profession.

We tested the validity of our data by conducting an additional 22 Zoom interviews and 9 written interviews with professionals in a wide range of careers to ensure accuracy. This was incredibly beneficial, and we applied learnings on the importance (or often unimportance) of formal qualifications for users of a certain age when changing careers.

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.

To solve this, we ask users their current and past careers. Our autocomplete captures this information, enabling us to match careers precisely to ESCO.

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.

The algorithm instantaneously performs half a million computations to generate a user’s skills match to careers of interest. This gives users helpful insight into how transferable their skills are to careers they are exploring.

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.

82% of our users have rated it 4 or 5 stars out of 5 and its power has been astounding — we’ve had over 51,000 users complete our quiz, yielding 5.01 million data points. This has led to the creation of our own proprietary source of LMI, which feeds back into itself to continuously improve career discovery.

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.

This uses ‘comparative judgement’ as research shows that people are very good at picking between one of two options, which is an effective way of helping people make complex decisions. But with a database of 504 careers, there are 126,756 possible pairs we could show.

Since being selected as a finalist, we improved our pair selection algorithm by using past data to determine careers that are most dissimilar. Now, our algorithm makes 32 million computations for each user. The improvements have led to a 4-fold increase in the number of final results careers (from 4 to 17) and a 55% increase in users emailing results to themselves — demonstrably helping careers exploration and raising aspirations.

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.

We display these jobs directly in-app, with job title, location, company, salary and description — in one click users can apply. Jobs are ordered from lowest to highest salary, as lower salaried jobs are more relevant to career changers.

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.

Pathways help to grow confidence by showing users exactly what they need to do to overcome qualification and funding obstacles, and details free courses and additional support from marketplace providers. These all include heavy personalisation.

Career concern, given Covid and the growing pace of automation, is central. We created Demand Labels, empowering users to think critically about their future career by providing IAG on long and short-term viability.

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.

Please see detailed future plans on page 30.

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.

Our outcomes, therefore, are to help people 1) discover and 2) get into their dream jobs — we built our solution during the Nesta prize to directly solve these. Within this, we identified our target users (who are also our beneficiaries) as people starting their first career or changing careers. Please see Appendix pages 36–37 for our full ToC and Indicators & Targets.

We have spent 9 months testing our activities, assumptions and the when/how/who/why to address these — and here are our insights and impact.

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.

Initially, we interviewed 23 users, 10 of which were from the NRS cohort. We recruited via broad Facebook ads to our app to ensure strong diversity. We interviewed each user for an hour and provided an Amazon voucher as an incentive.

The goal of the interviews was to identify how we can help them change careers. We prepared a script ahead of time to give us focus and direction, and this is what we learnt:

Our target users’ main motivations for changing careers were to find a job they enjoy more.

Our solution:

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

We learnt that our target users struggled to access real-world information and guidance about careers, so they would often talk to people they knew to explore further. They were particularly attracted by work experience opportunities to try jobs out.

Our solution:

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.

We learnt that our target users struggled to find a way into a career and are often overwhelmed by the volume of generalised information available. They wanted clear, personalised steps to follow that would guarantee them a job.

Our solution:

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

We learnt that our target users struggled to identify open roles that matched their current skills, experiences and qualifications. This was particularly pronounced for users in the NRS cohort.

Our solution:

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

We then interviewed 30 potential customers to better understand their needs and requirements to identify viable commercial models. These included 17 recruiters (most recruiting for entry-level roles), 6 training providers and 7 career professionals (more on this in section 5). You can read more about this product discovery phase in this blog post.

After conducting these 53 interviews, we ran a week-long design sprint to create an initial ‘Minimal Viable Product’ (MVP), so that we could begin testing.

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.

We started by creating a series of questions in our app to ask users before they completed the interests quiz, so we could better understand their needs.

We generated 1,500 survey completions in two days, gaining plenty of insights. We learnt:

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).

This insight gave birth to our Skills Matching and Pathways features.

We also learnt:

The average happiness of people who wanted to stay in their careers was 40% higher than those who wanted to change careers.

This learning was confirmation that our product strategy aligned with our mission of helping people find career happiness.

We then tested a series of propositions aligned with the most popular features people voted on to measure responses and interest, which included commercial partnerships:

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%).

Read the full run-down of the experiments we ran in this blog post.

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.

To test the impact of our complete product, we conducted a further 29 user interviews. These were a mix of Zoom calls and in-person job clubs, and the majority were NRS users. We additionally measured impact in-app through surveys before and after the interests quiz, enabling us to test with thousands of users.

This varied approach enabled us to collect and analyse both quantitative and qualitative data, which we view as critical in well-rounded learning that directly stemmed from our ToC. We used this to form the impact questions and metrics in our testing.

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.

Of these, 12–13% took the survey — therefore the numbers of people we directly helped with our final product will be approximately x8 higher than represented below. Please find screenshots of our before and after surveys in Appendix pages 43–44.

Have we helped target users access more IAG about careers, received tailored information or had their career horizons expanded?

335 vs goal of 300 said yes

Do target users feel they better understand Pathways into a new career?

223 vs goal of 150 said yes

Have target users found the current and future job demand labels helpful?

257 vs goal of 150 said yes

Have we helped target users improve their expectations of one day finding a job they’ll love?

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:

We propose to continue focusing on robustly measuring impact with randomised control trials to measure actual career change outcomes in the long-term and users’ career happiness over several years as a result of using our app. Further metrics will include:

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.

Our product has been designed to work with all these groups. We deeply personalise the experience based on a user’s location, employment status, age, salary, education and career history which further drives engagement. We ask users to provide us with these details so we can enable our level of functionality.

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.

We have also worked with local jobs clubs throughout the prize with organisations like Christians Against Poverty, running in-person sessions when Covid has permitted.

The quiz takes about 10 minutes, then users are free to explore their careers and Pathways indefinitely, and can email their results link to themselves for future reference.

Typical User Journey Map

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

And here’s our new homepage with our new branding and design applied:

We then designed and built a thoughtful and streamlined UX that is as simple as possible for users to navigate. We ask users upfront if they have a degree or not and whether they aspire to get one. If not, we filter out all careers that require a degree throughout the app.

We have four main input screens to capture all necessary user details to power our features, covering their location, situation, education and experience. The questions we ask are context-specific and we only ask what is absolutely needed. We embed autocompletes to qualification subjects and work experience to make it easy for users and to ensure we can map to our structured data sets (which power our features). This process only takes users 1–2 minutes.

Accessibility

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.

This ensured strong diversity in the users we were testing across age, gender, socio-economic background, educational and professional levels, and ethnic groups. We were also cognisant of testing our app with people who had a wide range of digital literacy, which we tested by asking them to share their screen on a zoom call. Whilst several found this a challenge, we observed all were very easily able to use our app — and everyone specifically commented on this too.

The interviews were designed to test the accessibility of our final product, and its impact (discussed in question 3). We gained hundreds of valuable pieces of feedback from the interviews and were able to iterate immediately on many of the issues raised. For example, over 18 user tests, we made 17 improvements to fix issues — meaning we were able to also validate our fixes as we went. All our users said that they found the app easy to use and accessible, and we received overall feedback such as this.

To ensure accessibility, users can access our product independently on their own device of preference. We have robustly tested with users on smartphones, tablets and computers as well as across all operating systems to ensure access for all.

We chose Overpass as our font as it is sans serif, making it more readable for people with dyslexia, as well as choosing a larger font size. A specialist in accessibility for people with visual impairment also reviewed our app and advised that this group especially struggled with obtaining careers IAG. Their feedback was invaluable and we plan to implement their suggestions in the near future.

We identified other areas we can improve upon in the future, such as sorting of final career results. For us, this is the start — not the end — of ensuring maximum accessibility.

Engagement

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.

We apply hyper-personalisation to the IAG we provide through our interest-based Career Matching, Pathways and Skills Matching features.

Throughout, we proactively encourage our target users’ engagement. In Pathways, for example, this is by showing a checklist of items they need to complete, which updates the time and cost remaining as they tick off the items. They can save the link to return to their Pathway at any time, which remembers their stage so they can continue to tick off the items they have remaining.

In our marketplace feature (called ‘Helping hands’ within the app), we connect our users to a curated set of partners who can provide further support such as coaching, mentoring and support in getting a job.

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).

For context on how easy people find our solution to use, 83% of users who started the quiz completed it (the first phase), and 82% of users who started the input screens completed them (the second phase, providing us with their postcode, employment status, age, salary, education history and career history).

Since finalising our solution, our engagement metrics across the app have consistently surpassed our expectations by a significant margin, demonstrating our value to users. We are able to confidently validate this, as during our learning and experimentation phases, the opposite was certainly true as our metrics were far lower than our goals!

Here are our engagement metrics over the past two weeks, since finalising our solution:

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.

We shall continue to talk to and engage with our users as we continuously adapt our solution to meet their needs. We’ll reach out to more users through our Facebook ads, engage with them through services like local job clubs, as well as through future customers like DWP prime contractors, schools and universities. We’ll also continue to gather and analyse engagement and impact metrics.

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.

We also plan to supplement our business model with a B2C offering, as well as monetising affiliate links through our Marketplace.

During the prize, we explored four different business models.

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.

We deprioritised this approach in the short-term, but recognise there might be a strong long-term commercial opportunity here as we expand our product offering to help people at all stages of their career journey, including flourishing in their existing career. That way we could better retain users and capture experienced professionals who are more attractive to employers.

We then explored selling directly to consumers. We ran an experiment to charge consumers to use our early career discovery app, but learnt that we hadn’t created enough value for users to pay. We then asked 28 users during interviews whether they would pay for a solution that helped them with their career challenges — there wasn’t a strong indication that many would.

This was further validated by talking to careers experts, who confirmed that people don’t tend to pay for much in the career space today, with the exception of training. This approach, therefore, would likely be challenging in the short-term until our solution creates significant perceived value that users might then be willing to pay for.

Finally, we robustly tested an affiliate business model of generating revenue from sending our users to other companies. We started with affiliate ads by monetising clicks users make on the job ads we show in our “Jobs Nearby” feature through Adzuna’s API. This alone demonstrated up to a 40% return on our ad spend (partly due to the fact that our user acquisition costs are so low). We then tested affiliate links with Udemy and the Learning Curve Group for training courses.

While we had some positive results, it wasn’t financially promising in the short-term. We’ll continue to commercialise these clicks to supplement our business model, but we won’t focus on it for now (though we continue to measure user clicks to external companies in our “Helping hands” and our Pathways feature).

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:

These customers all have strong budgets and incentives to deliver IAG to their beneficiaries. We can add direct value, both in improved delivery of personalised careers IAG and cost reduction. We plan to start building a strong commercial foundation with prime contracts and schools..

We hope to build a strong pipeline of customers by the end of March.

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.

Moreover, as our solution is delivered purely through software, we have negligible marginal costs, meaning that as we scale, our costs only marginally increase. We can serve these large markets while maintaining very high-profit margins, which enables us to be sustainable and scale very quickly.

Forward Partners, a leading venture capital company in the UK, invested £500,000 into Would You Rather Be back in March. They strongly believe in the market opportunity, sustainability and scalability of our solution. The 80+ user and customer interviews, 18 product experiments and the thousands of impact and engagement metrics we gathered since becoming a Nesta finalist, all demonstrate that we are meeting an identifiable need in a meaningful way.

We are confident that we can successfully sell and scale our software in these markets.

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.

We have a range of evidence to suggest this is a serious opportunity. We spoke with Simpact (a Nesta-recommended strategy consultancy who specialise in this), and they confirmed the market opportunity here. Many of these providers receive provision of £3,000-£8,000 per user to deliver training and a range of other services, which often includes career provision. So if we can deliver this service better than our competitors and/or at a lower cost, then we should be able to generate strong commercial value from this target market.

This would also give us a commercially sustainable avenue to continue to serve the NRS cohort, as many of these contractors serve this target user group directly.

Schools

There are 4,188 secondary schools in the UK. Each state school receives £5,000 from the Careers and Enterprise Company for career provision, and schools should have additional budget for this too. Private schools and academies have even larger budgets. Moreover, through the Gatsby Benchmarks, all schools have a duty and responsibility to provide career provision to their students, and already do this today. This gives us confidence that there is Government-backed need and budget and that we can bring unique value in both improving their provision, and reducing their costs (or enabling their budgets to go further).

With 30–50% penetration in UK schools, at a conservative £1,000 per year revenue per school, we’d have an annualised £1.2m-2m/year run-rate. We believe we can reach this target within 2–3 years.

Moreover, we can multiply this revenue as we serve FE colleges, universities, primary schools and independent careers advisors.

We can multiply this revenue further as we consider launching and scaling in the US, which we plan to do within two years. As our solution is pure software, we would only need to update our models for the US labour market.

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.

However, this needs to be balanced with a sustainable commercial model.

Our plan is to have a ‘freemium’ app — our interests quiz and final results will be free, and accessing additional features will be paid premium, such as Skills Matching and Pathways.

Alternatively, another organisation would cover this cost, such as their school, college, university or a DWP prime contractor.

By taking this approach, we allow users to try part of our solution before buying it. This also means that everyone has access to some free careers IAG. Even if a user can’t access our full solution through another organisation (like their school), they can still pay to experience it themselves. This open access is important to us and our mission.

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.

Later in the year, we plan to develop psychometric matching so we can map a user’s personality and aptitude to careers of interest. We’ve hired an expert with a PhD in psychometrics who has already pulled together the most relevant research in the field for us.

We plan to develop our own proprietary dataset for personality mapping, by profiling a large range of users in different occupations. We then plan to use existing datasets to power aptitude mapping, such as comparing the Ofsted rating of their school against the grades they achieved. We also plan to map a user’s values to careers of interest too.

From 2022, we plan to build a library of video content. We hope to interview thousands of professionals in every career and deliver bite-sized, relevant videos to our users in the right context, at the right time, using software.

Beyond this, we plan to help people progress and flourish in their existing careers by building progression pathways, providing training recommendations and connecting people to coaches and mentors.

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.

We can achieve further scale with other channels, both direct to consumers (such as Google ads) and through other organisations and customers of ours in the future, like schools, colleges, universities and independent training providers.

In the UK alone, there are 32.5 million working adults — 54% of which were actively looking to change careers pre-pandemic, which is 17 million people in the UK. This is even higher for 25–35 year-olds — a shocking 72% of which want to change careers, which is 10 million people. These numbers are likely to be even higher in light of the coronavirus crisis due to the rise in redundancies and ‘unviable’ careers.

We will also factor in the 800,000 people entering the workforce every year as they leave school or university. Nearly everyone has to solve the problem of discovering and getting into the right career for them.

Consider that these figures represent only a fraction of the global working population, who we aspire to help long-term — starting with the US in the next two years, then into Europe and beyond.

Our solution has been designed to work for every person in the UK who wants to think, prepare and actively change careers. As it’s software-based, we can rapidly scale around the world.

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.

We can then all look forward to Monday morning more than Friday afternoon.

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.