The Spring 2013 issue of the Stanford Social Innovation Review has the first article on the use of analytics that I have seen in the magazine so far – specifically, on the use of analytics to guide and inform advocacy efforts.
The authors provide a systematic method that their employer, the Redstone Strategy Group, helped design to “plan, monitor and evaluate advocacy investments”. Such a method can help overcome the subconscious decision biases identified by Princeton’s Professor Emeritus Daniel Kahneman and winner of the 2002 Nobel Prize in Economics in his seminal work.
The analytical assessment framework is built around nine conditions that are viewed as “essential to a successful policy campaign.”
- Functioning venue(s) for adoption (i.e., functioning regulatory institutions and the like)
- Open policy window (demand for the solution)
- Feasible solution (a solution has been “shown to provide the intended benefits”)
- Dynamic master plan (“pragmatic and flexible advocacy strategy”)
- Strong campaign leader(s)
- Influential support coalition
- Mobilized public
- Powerful inside champions
- Clear implementation path.
These nine conditions can be used as (i) a checklist, to help “grantmakers and campaign leaders consider the full range of influential factors in creating a successful campaign”, (ii) a rubric, which “allows grantmakers and advocates to take a deeper look at the conditions and score them from 1 to 5 on a spectrum from “not at all” present to “fully” present”, or (iii) a quantitative estimator for likelihood of success, which “helps funders and campaign leaders judge returns on financial investments”. Method (iii) is the most quantitative approach, because costs of planned activities and performance measures have to be estimated with and without the contribution of the grantmaker to assess the grant’s potential impact.
The authors point out that “[a]ny formula that provides quantitative estimates of uncertain values, such as the likelihood of achieving policy change, is a decision-making aid, not a scientific truth”, a caveat that is useful to remember when faced with the temptation to blindly follow the conclusions of a mathematical model having required a number of hard-to-check assumptions. They further divide policy change into three stages and estimate likelihoods of success for each: agenda-setting (conditions 1 to 3 above), adoption (conditions 4 to 8) and implementation (condition 9). Because successful policy will need to go through the three stages, the cumulative likelihood of success is the product of the likelihoods in each individual stage.
Conditions 3 to 8 are labeled “campaign conditions”, i.e., conditions that the campaign can influence in the near term. Conditions 1, 2 and 9 are called “context conditions” and “reflect forces that advocates have little ability to influence via the campaign, especially in the near term.” The campaign’s contribution is estimated by comparing the estimated likelihood of success with and without intervention. The philanthropic return on investment for a campaign is then the potential social benefit of a successful policy multiplied by the percentage-point increase in the likelihood of success, divided by the cost of the advocacy campaign.
The framework is valuable at each of the four stages of an advocacy campaign: (a) evaluating pathways (deciding on a strategy), (b) incorporating contribution (deciding on a tactical approach), (c) monitoring progress and (d) assessing results. For each of these four stages, the authors explain how their nine-condition Advocacy Assessment Framework can be used as a checklist, as a rubric and as a quantitative estimator.
The Hewlett Foundation in particular implemented the framework with great benefits when it had to identify five states to give support to for “broad-based advocacies of clean electricity policies.” The Western Energy Project is also using the framework to guide its advocacy efforts to restrict oil shale leasing. Finally, the framework can help analyze completed efforts such as those to protect a remote Wyoming range from further oil and gas exploration, which culminated in the 2009 Wyoming Range Legacy Act. The authors conclude: “The early applications described in this article suggest that it can help grantmakers determine whether they are choosing the right topics and investing in the right grantees to reach their goals.”