socially marketing

Using impact metrics to understand the social sector can take many forms, as seen in these current efforts:

performance measurement: evaluating non-profit effectiveness by measuring their outcomes.

outcomes taxonomy: creating a master list of non-profit outcomes to be used as a sector-wide standard for comparison.

social capital: broadening the definition of capital to capture environmental and other social values.

socialmarkets depends on all these trends, and also pushes them a little further into unknown territory.  This is especially true for the social capital model, like when we mix up ‘regular’ dollars (e.g. a donation to a charity) and social dollars (e.g. the SROI a donation produces).

The name socialmarkets itself goes a long way towards explaining what we do: applying the market model to the social sector.  We believe there are potential benefits to applying economic market principles to the non-profit sector, which is after all, itself an economy.

We do not believe that markets and non-profits are a match made in heaven, and our goal is not to force one onto the other.  Rather, our goal is to explore the boundaries of where the market and social sectors can come together constructively, and where they can’t.

This exploration includes stretching some of the classical market vocabulary.  Take ‘currency‘, for example.  Currency is the very foundation of modern economies, offering a neat method of assigning value to things, and of facilitating their exchange.  Dollars, euros, rubles and the like comprise the hard currencies that make traditional economies go around.

socialmarkets suggests an economy based on the soft currency of SROI (Social Return on Investment.)  Social currency is no good for shopping at the mall, but is excellent for capturing and communicating the social value of non-profit work.

Currency actually flows quite naturally in socialmarkets, but price does not - even though the two terms seem closely related.  We capture the value that non-profits add to society, in the form of social currency - which is not quite the same as coming up with a price.  This distinction illustrates how social markets don’t always mesh neatly with traditional ones.

Traditional economic markets depend on a neat line between profit-maximizing producers and penny-pinching consumers, to guide the market’s invisible hand towards a compromise called ‘price’.

Social markets are more complicated.  The line between the producers and consumers of social capital are fuzzy, and the currency that flows between them is more nuanced than plain dollars.

We are so used to traditional ‘prices’ that we forget how fickle they actually are.  From real estate on a global scale, to a cup of coffee on our own corner, prices often move along surprising trajectories.  There is an entire field of economics devoted to studying (and ironically, rationalizing) the irrational behaviour of prices.

This helps remind us that the price of any thing is just a snapshot of how people value that thing… which suggests a useful definition for social return of any given outcome: a snapshot of how valuable people believe the outcome to be.

When we have more experience with social markets we should be able to make better sense of how price - and other economic jargon - translates into the social economy.  For starters, we can recognize that the boundaries between social and economic currency aren’t all that well-defined in the first place.

When looking at the impact of a non-profit project, we typically talk about outputs and outcomes.  The difference between the two is a matter of scale: outputs are relatively short-term and small-scope; outcomes are longer-term and larger-scope.  Outputs, over time, help pave the way for outcomes.

Impact metrics like SROI (Social Return on Investment) are easier to manage with impacts that are easily quantified.  Outputs are more likely than outcomes to be expressed in neat countable units, which makes them an appealing starting point for calculating SROI.

Consider the large and growing number of environmental groups focusing on climate change.  Their energy conservation efforts can legitimately claim “a healthy and sustainable planet” as an outcome.  Such an outcome is clearly and immensely valuable, but challenging to nail down to a dollar figure.

Now consider a short-term output of those same conservation efforts: reduced CO₂ (carbon dioxide) emissions.  This is an easy result to quantify.  Even better for socialmarkets, it’s easy to monetize: financial markets have been pricing CO₂ for years now.

According to the ECX exchange, CO₂ is currently trading at roughly €14 per metric ton.  An output that saves 100 metric tons of CO₂ emissions has a related market value of around €1400, which is a good starting point for calculating SROI.

I say starting point, because the SROI of a project is rarely is simple as the market value of one particular output.  There can be many outputs, with many related outcomes.  In any case, there are questions to complicate even the simplest of impact metrics:

• How do we allocate the SROI of one outcome across many contributing agencies?
• Should we discount the SROI of an outcome over the time it takes to produce it?
• How does SROI account for the risk associated with a project falling short of its stated goals?
• Is the impact of not producing a negative output, e.g. a ton of CO₂, the same as producing a positive output, e.g. a gallon of fresh water?

We are a long way from having good answers to these questions, but countable outputs (like tons of CO₂) is one promising shortcut.  Here are some other volume-based outputs we find intriguing, especially if we can find a reasonable estimate of the corresponding social benefit per unit:

• Number of nutritious meals served at a soup kitchen
• Number of students obtaining a higher level of education
• Increase in fish population from a river clean-up

Because the fungibility of such data is good for SROI calculations, we are including projects with outputs like this in the set of listings for our beta release.  They should make an interesting testbed for examining if and how easily quantified outputs translate into better calculations of SROI.