What is conditional giving?
In simple terms - it is "all or nothing".
Either you meet your fundraising goal and claim success, or If the target is not reached by a certain date, all individuals are given options for re-routing their donation or getting their money back. Counter intuitive?
If you dare to go conditional - great things will happen.
In fact, we proved time and time again that conditional campaigns are 2x more successful than plain campaigns.
Reduce uncertainty. Make giving meaningful.
Our technology is based on 10+ years of game-theory research and live lab experiments, conducted by two of the company's founders, economics professors from NYU and UC Berkeley.
Your supporters can observe project momentum as it builds towards an established and clear goal. This shows donors what their "share" of the project is, and encourages higher donor participation.
Conditional giving makes the fulfillment of commitments for a project pending until the campaign reaches a specified threshold.
In the case the target goal was not met, donors are provided notice and are given several options of re-routing their donation to a new cause or of receiving their money back.
Counter intuitive? perhaps. Unorthodox approach? Well, this was our response at the get go 5 years ago. Lab trials, conducted at the Experimental Lab at UC Berkeley provided eminent proof that donor participation and giving levels go up 3X once their risk is reduced.
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Wanna know more?
The Science Behind
To illustrate the problem that we solve, consider the following example:
Eleanor’s three year old daughter, Beth, has a heart defect. Without costly surgery, she’s not expected to live 6 months. Eleanor’s neighbor Jean wants to help raise money for the surgery. She is hesitant to make a large contribution herself, because she is unsure that enough money will be raised to make a difference and that her neighbors won’t help her reach her goal.
This illustrates the two problems plaguing social fundraising for charities: fragmentation and free-riding.
Fragmentation is the inability to coordinate the charitable activities of numerous individuals in pursuit of a common goal. This can occur due to lack of information, inefficiency, fraud and conflicting expectations. It can lead to an outcome that leaves everyone worse off.
Free-riding takes place when individuals take on less than their fair share of the costs of aiding the common good or public interest. Such self-seeking behavior results in inefficient results. While these problems are well known and partially addressed in traditional giving, the shift from offline to online giving makes them significant inhibitors to the exponential growth of Social-Fundraising
The example above illustrates the conditional features of give2gether that sustain efficient outcomes.
Jean joins a give2gether campaign set by “save a child’s heart” with a Fundraising Target of $10,000 goal. She makes a $500 contribution, based on others funding the remaining $9,500 with their contributions. Within two weeks the goal is reached and Beth’s life is saved.
The key to our solution is to have the option to make pledges conditional. Individuals can make pledges conditional on reaching a target. If the target is not reached by a certain date, all individuals are given options for re-routing their donation or getting their money back. This reduces the fragmentation risk of falling short of the target. Furthermore each pledge is observed by other individuals and builds toward the target, thus encouraging more contributions, which reduces the free-rider problem.
the “Secret Sauce”
Our theoretical and experimental research shows that both fragmentation and free-riding are solved by allowing individuals to make their contributions conditional on matching contributions and/or the achievement of a specific goal.
The campaign in the above example can be optimized using 5 parameters:
The success or failure of a fundraising campaign depends on the specific set of parameters chosen. Our technology innovation is an interactive game-theory algorithm, which optimizes the choice of these parameters to maximize the probability of success.