Hacking Mobile Invites Using Census Data
Getting existing users to invite their friends is a basic, but effective growth strategy. Many of the social networks such as Facebook/LinkedIn drive tremendous growth by pushing new users to invite their friends by importing their email address book. Mobile apps have tried to take the same approach, but the problem is, most don’t do a very good job of maximizing the number of invites. In this post I’ll show a little hack on how you can use demographic data compiled by the US Government to maximize the number of invites.
Suggest Friends to Invite
One of the key mistakes many mobile invite flows make, is they just show an alphabetical list of all your contacts. The problem with this UI is it can take several seconds per friend to search for their contact and add them to the list of people being invited. The alphabetical UI makes inviting more than a couple friends a chore.
The secret to improving this is simply figuring out everyone the user would want to invite and then just putting those people at the top of the list. By showing a list of suggested friends you get two benefits over an alphabetical list (assuming of course that you can deliver quality suggestions)
- Adding friends from the suggested section is extremely fast
- You can remind the user to invite people that they may have otherwise forgotten
Pulling suggestions from thin air.
There are two components that go into suggestions. One is figuring out whom the user would be willing to invite and the other is to figure out who would be interested in your app. The second part can be especially challenging if all you have to go on is just the contact’s name and phone number. So how do you figure out if someone is interested in your app just based on their name? Well, it helps if you have a broad target demographic (ex: females between the ages of 20 to 45). This is because you can often infer not only someone’s gender, but also their approximate age from just from their name. As fans of Freakonomics already know, popularity of certain names tend to rise and fall over the years.
So, how do you actually go about figuring out someone’s age from their name? Well the Social Security Administration publicizes on their website the top 1000 baby names for each year from the past century. By crunching this data you can figure out for each name, the probability that someone with that name falls in your target demographic and use that information to help generate suggestions.