Word-of-mouth marketing is normally viewed as something that should arise out of person’s intrinsic motivation to spread the message rather than explicit rewards for spreading it. An exception to this is refer-a-friend programs. The internet made the process of referring extremely easy, making refer-a-friend programs an attractive tool for marketers.
In the tradition form, a refer-a-friend program would offer a reward to the referrer for each successful referral. In the last few years, many refer-a-friend programs started offering equal compensation to the referrer and the invitee. In fact, googling “recommend a friend” one may struggle to find a program that does not compensate the invitee. Successful examples of symmetric refer-a-friend programs include online storage service Dropbox offering 500 extra megabytes of free space and a taxi company Uber offering £10 of credit. There is an element of gamification that intensifies the incentives: people brag about how many free taxi rides they got.
Compensating the invitee equally with the referrer ameliorates the concern that either party will feel the referrer is taking advantage of his friends in the hopes of getting referral rewards. Another criticism of refer-a-friend programs is that the referrer might feel he is selling information of his friends. However, social networks help avoid this problem. Instead of entering emails of friends, one might post a message with the referral link on Facebook, Twitter, blog, etc. This is also much easier to do (just click “share on facebook”) and is likely to have a wider reach.
Symmetric refer-a-friend programs have become popular enough for a business to appear that matches a prospective new customer with a referrer. Not exactly what designers of refer-a-friend programs had in mind, but they have little reason to complain.
Why stop at compensating the referrer and the invitee equally? Pushing it all the way towards altruism, means only the invitee gets compensated. Will this work? We are undertaking a natural field experiment to find out which refer-a-friend version works best in which context. And we are looking to partner up with a company that wants to learn more about its customer referral behavior. If you are one of them, get in touch!
How can word-of-mouth marketing be encouraged with direct incentives? We undertook a natural field experiment to compare three mechanism that acknowledged successful referrals with different number of points.
Meritocracy sounds like a pretty good idea. Contributions of users in crowdsourcing systems are observable and, in cases where the relative worth of various types of contribution can be measured, the users can be evaluated based on the total contribution they made. In practice, a user can be assigned a certain number of points for every contribution. These virtual points are meaningless per se. However a variety of meanings to can be attributed to make them useful.
User’s trustworthiness or reputation can be measured by points. The reputation in turn corresponds to the power a user welds within the crowdsourcing system. For instance, Stack Overflow lets users earn points for useful contributions such as answering questions or asking good questions. The more points one earns the more actions one is allowed to perform.
Gamification and points go hand in hand. A status bar showing how far you profile is from being completed could be a motivation to enter more information. This can be combined with the idea of releasing more features to people who made a larger contribution. For example, a dating site may not let you view pictures of others until you upload your own.
Another clear application for points is encouraging contributions through competition. Highest contributors can be listed on the leader board. Further incentives can be provided by telling the user how many more points she needs to go up in the rankings. Comparisons with ones Facebook friends may be most interesting. It keep me playing the Traveller IQ game for a while.
In a similar way, users can be motivated by social recognition which is provide for users with most points. The names or twitter handles of top contributors can be listed on the front page. In some cases such recognition has monetizable value. Top Stack Overflow users are viewed as professional developers by the companies even before they are interviewed.
A very direct way of using points is to convert the into something of immediate value such as free space on Dropbox or a cash equivalent.
The point of this post is to say that points provide a unified way of keeping track of contributions, which can be manifested to the user in a variety of ways.
How do you get me to take out my phone and take a measurement to you? The answer is “nobody knows”. The field is still very young with most literature describing pilot studies with only preliminary findings.
Examining Micro-Payments for Participatory Sensing Data Collections. Pilot study with 55 participants at UCLA. The task was to take photos of outdoor waste bins. The incentives compared included low, medium, and high levels of payment per photo, single large payment for participation, as well as competition-based payments. The competition-based scheme was most effective in the experiment.
Many tasks are too big for a single person or firm and can only be performed with the help of the crowd. Such tasks include data collection projects (e.g., Ushahidi maps), data processing projects (e.g., GalaxyZoo), and wide-area search tasks (e.g., the Red Balloon Challenge). The success of these tasks relies on having a large number people working on them. However, recruiting a large number of people is a difficult task itself, which often cannot be performed by a single person/firm… In order to succeed in crowdsourcing such tasks, recruitment of people should also be crowdsourced.
Consider an old example of crowdsourcing — search for a criminal. A bounty encourages to capture/report the wanted person. However, a bounty provides no inventives for referrals. A modern-day version of such search tasks do not have to rely on posting the photo of the target on every street pole. Instead, information about the task can spread through referrals on social networks, ideally leading fast to the people who have the right information. The Red Balloon Challenge demonstrated that such search can be very successful.
The tools are there: social networks make it easy to share information; keeping track of referrals is also not hard. The question is how to encourage people to share information given these tools. There will not be a golden bullet solution but rather a better understanding of what mechanisms can be used and which are more appropriate for a given type of project (e.g., fundraising, citizen science, natural disaster relief).
In the context of selling goods, referral programs have been studied in marketing for decades. Multi-level marketing deals with referral chains. There are companies whose business focusing specifically on sales referral management referralcandy and curebit.
Several crowdsourcing competitions were held in recent years.
Red Balloons challenged participants to find 10 red weather balloons moored at secret locations throughout the US.The winning team found all balloons within 9 hours using an incentive scheme that encouraged not only reporting the balloons, but also recruiting new participants.
The Tag Challenge was similar to the Red Balloons challenge, but the search was for 5 individuals moving around 5 cities based on their picture and t-shirt. A similar incentive scheme worked.
The MyHeartMap Challenge crowdsourced creating a map of AEDs throughout Philadelphia. Crowdsourcing did not produce the expected result here. It did work in the Netherlands, however. Cultural differences? Monetary incentives “crowding out” altruistic spirit? Not enough media support?
Stack Overflow has an elaborate point-based system to keep track of reputation and assign privileges. They also assign badges to acknowledge various types of contributions.
Open Street Map uses a seemingly much more limited points and badges system. Here is the info from https://help.openstreetmap.org/faq/ (retrieved 2012-09-24).
When a question or answer is upvoted, the user who posted it will gain some points, called “karma points”. These points serve as a rough measure of the community trust in the user. Various moderation tasks are gradually assigned to users based on those points.
For example, if you ask an interesting question or give a helpful answer, your input will be upvoted. On the other hand if the answer is misleading it will be downvoted. Each vote in favour will add 15 points, each vote against will subtract 1 points. There is a limit of 200 points that can be accumulated per question or answer. The table below explains karma requirements for each type of moderation task.