From How Airbnb Built One Of The Most Successful Referral Programs Ever:
Key insights:
After speaking with other well-known companies that had implemented successful referral programs, Airbnb discovered that potentially 25-55% of new user growth could be produced through referral growth channels.
What did Airbnb measure?
In order to measure the effectiveness of various parts of the program, Airbnb looked at several different levers that would impact how effective their referral program could be.
- Monthly Active Users Sending Invites
- Invitees per Inviter
- Conversion Rate to New User
- Conversion Rate to New Guest
- Conversion Rate to New Host
- Revenue Impact Potential
The detailed mechanics of use user flow is important:
Making it easy for users to send invites to their friends was also essential. In addition to providing an easily shareable link, Airbnb provided a method to import contacts and friends from Gmail and send a customized invitation email to each contact. When users engaged the referral program, Airbnb also experimented with how to present the contacts for the best user experience.
Neo has an option to share link and two-way rewards to incentivize both users, but there is no way to import contacts to send customized invitations. Neo has an import contacts feature in the e-transfer menu, but it does not allow bulk import - every contact has to be added manually and the user can’t see which friends already have Neo and which ones don’t. There is must be a way to incorporate an invitation to join Neo in e-transfers so that people are invited every time anyone sends money.
“We tried to figure out contacts that were close to you,” said Jimmy Tang, Engineering Manager at Airbnb. “The goal here was to figure out who are the people you talk the most in order to get a higher conversion rate on the send.
AI could be used to make invite text much more personalized and engaging, although for A/B testing it might make sense to have a mix of different templates as well as custom AI-generated messages.
The importance of including photo:
One of the most important design features Airbnb tested was including a photo of the sender. “We take the picture of the referrer and put it right in the center to show some social proof and build some trust.” said Tang, Engineering Manager at Airbnb. This augmented the gift-like feel of the email and helped new users who were new to Airbnb understand that his was a personal referral.
Gift-like experience is important:
We got the perception that people were really treating this as a gift and this was exactly what we wanted.”
Self-interested vs altruistic messaging:
In one email, we emphasized that you can earn $25 for inviting a friend (self-interested). In the other email, we emphasized that you are sharing $25 with your friend (altruistic). The result was the the altruistic email preformed better globally and provides a valuable lesson for anyone designing a referral program and trying to balance which incentives might elicit user action.”
“You’re not only getting a $25 credit off your vacation, but your friend also receives that same credit. So it doesn’t feel like someone is taking advantage of you, it’s more of a natural conversation which helps drive user participation. Many consumers just aren’t interested in trying to make money off of their friends, it seems greedy and could sour relationships.”
This awareness of how a referral offer plays into everyday human to human interactions is an important consideration when constructing the components of a referral offer.
Airbnb measured everything that matters at every step:
“The way we did tracking for this specific product is that we built a taxonomy of all the events that we wanted to track ahead of time and instrumented every single one of these events right on the day we launched…that made it easy to see where we would iterate on this product and where we would go next”
Looking at Airbnb’s careful instrumentation, it became clear which methods were working and which ones were not. This made A/B testing the effectiveness of emails, subject lines, calls to action, and landing pages much easier to evaluate.
The result was a clear indication of hundreds of thousands of additional nights booked. One of the most interesting takeaways, was that in new markets, the referral program had increased impact. “The less mature the market, the more the impact of the referral program,” said Gustaf Alströmer.
Resources required to build Airbnb referrals:
“Building a referral system is very hard work. It took a 5 person team (with lots of borrowed help!) 3 full months and 30,000 lines of code to do Airbnb’s Referrals 2.0 system.”
What do you think?