5 essential pre-build activities for social tech teams
Despite the extraordinary achievements of mission driven technology like Wikipedia and Khan Academy, we seem to be stuck with very high rates of failure in tech for good innovation. Good data from this fuzzy-edged sector does not yet exist, but after 10 years of being exposed to tech for good projects with big ambitions, it is clear that most either struggle to move beyond early stage R&D funding or find themselves stuck on a plateau of modest scale and impact.
Tech for good is faced with unique challenges: it must solve both user and social problems with its products and services; it must measure the real world impact of digital interventions, not just traction and revenue; it must deliver high quality user experiences with much smaller budgets in an ever more expensive market for engineering talent; it must establish sustainable revenue streams whilst often providing services for lower income user groups or the increasingly impoverished public sector.
Given these challenges, and many others, there is an even greater need for funders and innovators to work together more closely and explore how stronger foundations can be put in place for successful social tech projects.
When we set out on a 3 year social innovation partnership with the Nominet Trust in 2013, we included an explicit ambition to develop, refine and codify a more rigorous R&D process, with greater attention, investment and resources dedicated to the kind of pre-build activities that are normally so difficult to fund on a project-by-project basis.
This has been transformative for our work and, as part of codifying and sharing our progress, we have identified 5 activities that we work through before we embark on any new development.
These aim to not only reduce the initial assumptions that often undermine projects, but also identify the technical development that doesn’t need to happen at all - something that the rush to build can often miss.
1. Develop an evidence-based theory of change
Purely commercial technology can follow its nose through design cycles,responding to user needs and driven by traction and conversion to sales or ad clicks. But social technology has a second, even harder task - it must deliver and measure impact against its social objectives. If assumptions and flaws are built into a product from the outset, it doesn’t matter how much traction or attention it gets, it won’t deliver its mission.
Social tech innovation needs a robust theory of change which can be tested and refined throughout the design process and used to provide an ongoing framework for measurement and evaluation at every stage. This provides a behavioural hypothesis that defines specific audiences and user actions and concrete, measurable outcomes, must be based on credible existing evidence.
2. Obsess about market segmentation
Market segmentation can transform the design and marketing of a new product, providing clear, specific insights on a highly targeted user and / or customer group.
Opower, the hugely successful US energy efficiency software company, for example, has built its business around segmentation of energy customers and a lot can be learnt from their approach.
The default alternative to thorough market segmentation is a vague nod towards a large mass of potential users, about which very little is understood beyond socioeconomic group, location, age or social need.
Tech for good funders have a particularly important role in helping support best practice around market segmentation, investing in resources for projects to divide up their target markets into specific groups, based on common needs, interests, and priorities.
3. Inform product concepts with objective data on user needs
Cheap, quick ways of engaging with user needs, like focus groups and consultation sessions, are certainly part of the design process, but they only provide a glimpse of real needs and habits - and sometimes completely obscure them.
For example, in a UK study on exercise, 67% of men reported meeting recommendations for physical activity. A later study, which collected objective data from accelerometers, found that only 6% of men actually met the recommendations. Access to the subjective data alone would have been highly misleading for the design of any intervention for this audience.
Objective research activities, like observation, ethnography and the analysis of raw behavioural data, take more time and investment but they can transform design insights and development priorities.
4. Run tests with what already exists
Armed with an early product concept, what social tech teams need most is a chance to test their assumptions and start to accumulate evidence of impact. Where early stage social tech investment can often push them, however, is towards the development of a new prototype, sometimes with very small pots of funding to do so.
It may often be better to spend small, one-off amounts on running high quality test activities with tools, products and features that already exist. This level of investment would allow a team to identify and adapt an existing tool, set-up a test environment, develop an evaluation framework and work through several cycles of testing, measurement and refinement, generating vital user and impact data.
5. Conduct a thorough competitor analysis
It can often be difficult for social tech teams to identify competitors for their product - or even a relevant existing market. But this is one of the reasons why competitor analyses are so valuable.
The best tech for good products solve social problems and meet user needs. If the latter is a real need, then it is very likely that there is existing market for products and services that meet it, however niche and underdeveloped it might be. This process, which involves a comprehensive analysis of relevant markets, categories, products and technologies, emerging with a specific and clearly identified rationale for new development that is within reach of the team, plays a big role in assessing the commercial viability of a proposition and informing its business plan.
This process also - crucially - helps to identify opportunities to leverage or build upon existing technology, which might massively reduce the technical scope of the project - or the need for any new technical development at all.