If you understand the common reasons why tech for good projects fail then you’ll feel more confident about how to avoid them.
Many charities fear failing at tech projects. That’s not surprising given the amount of money historically squandered on digital projects (NHS IT system anyone?).
But we’ve got to try. Or risk going to the wall if we don’t.
Trying starts by taking small steps and learning as you go. It means building digital services and organisational capacity to solve user problems aswell as social ones. It’s a journey every charity is going to have to make over the next decade. So it’s inevitable there will be failures. That’s OK.
What do we mean by ‘fail’?
For the purposes of this article lets focus on ‘digital services’. That’s the type of Tech for Good this blog talks about.
Lets define ‘failure’ as when a project fails to grow beyond its first grant. When its digital service or product either ends, stutters on with inadequate maintenance or fails to find follow-on funding.
The main reasons projects fail are either practice based (methods, approaches, teams) or systemic (funding programmes and other external factors). There’s always an interplay between practice and system.
Fail #1: The service or product doesn’t meet a user need
The tech for good world is littered with beautifully well-intentioned projects that didn’t understand their users’ behaviour. They met a social need but didn’t relate to users needs. So they never got adopted.
They failed to understand user needs or define the problem from their perspective.
Perhaps the best example of this is websites that aim to help people in communities give and receive help. They typically fail because they don’t understand the quite complex ways in which people ask for help and offer it to others.
What you can do
Fail #2: Project success metrics don’t match the grantmaker’s usual success metrics
Conventional ways of measuring charity service success don’t work in digital projects. That’s because a digital service proving its potential will deliver low outputs during its research and development phase.
However if that stage is successful then its outputs will steadily increase as its viability becomes stronger and it prepares to scale.
Then, when it establishes itself at scale, outputs rapidly accelerate. Here’s a crude comparison:
Human Powered Project
- Year 1: 50 users
- Year 2: 75
- Year 3: 75
- Year 1: 10 users
- Year 2: 50
- Year 3: 500
Funders shouldn’t expect grants to digital services to deliver the same level of outputs after 1 or even 2 years. They need to develop different ways of measuring success for digital grants. And we need to help them.
What you can do
Estimate the potential reach of your service when scaled and make this clear in your application. Be clear about your project’s milestones. Then educate your funder in what success looks like at each one.
Fail #3: Using the wrong growth model
Here’s a story.
Project A is given 9 months of funding to develop a digital service. It does well, hitting its most important milestones (indicators of success and future potential) at months 7, 8 and 9. “Hurrah” shouts everyone so the charity applies for further funding.
But while that’s happening there’s no money left to retain the team that bonded together, nurtured the idea and built a collective insight into the problem. Six months later when follow on funding is finally secured most of he original team have left the charity and the new folk are left starting from scratch.
The problem is that successful projects, who have hit their milestones, usually lack access to quick funding. Quick funding that would enable them to maintain momentum and retain expertise and capacity. Conventional grant making programmes move too slowly to provide this funding and charities fail to take this into account when building their team and planning continuity.
What you can do
Apply for funding early. Build relationships with freelancers who care. Plan minimum contingency funding. Build digital capacity across your organisation.
But there is hope
Of course there is. Charities are learning how to understand user needs then define the problem through their eyes. Funders are learning how to adapt their funding models and use different success metrics. The numbers of successful Tech for Good projects are increasing.