In the digital world, new products are getting released all the time. Last week it was Facebook and the new Timeline. This week it’s Amazon and several new Kindle models. No matter what company is behind a new product release and no matter what the details are of a particular new product, there is a fairly consistent pattern of user adoption that unfolds. This sociologic model, known as the Innovation Adoption Lifecycle, dates as far back as the 1950’s when it was first documented by corn seed researchers at the University of Iowa. A small group of fearless people inadvertently start things off as early adopters; they are followed some time later by the masses, who are more cautious of change; and finally the change-averse eventually get on board long after it’s popular to do so.



If you look at any technology from the past or present like color television, GPS, or digital video streaming, user behavior closely adheres to this model. The same is true if you look at any specific web behaviors, like online communication, online shopping, or even online dating (remember when that was something people would be embarrassed about?). This model explains why a significant number of people are furious about the new changes to Facebook (they’ve been forced to adopt a change long before they’re comfortable doing so), and also why these same people will be happy to use the new Facebook in another few weeks (they will have had some time to get accustomed to it).

When companies attempt to score themselves on the success of a product launch, they often look at how long it took to reach a particular milestone. For example, it took Facebook 852 days to reach 10M users and Twitter 780 days to reach 10M users, while it only took Google+ 16 days to reach the same amount of users (source). Does that mean that Google+ is going to be more successful? Not necessarily. It just means that by the time Google+ was released, joining a social network was no longer so innovative, and therefor early adopters and mass users alike were joining all at once. Another interesting example is to look at the adoption of smart phones. While Blackberries were the first dominant force in the smartphone market, they were later surpassed by iPhones, who have since been surpassed by Androids. While some adoption curves move more slowly, they end up reaching a higher total rate of adoption, like in the case of Android. That might look something like this in a comparative plot:



The reality is that it is nearly impossible to anticipate how a product launch will go. No matter what the data looks like at any given moment, you never know if you’re in the middle of a huge exponential growth curve, or whether your growth is just about to peak and fall off. Rather than trying to predict the future, it makes more sense to focus on the various types of users and create a plan that meets the needs of as many people as possible. Modeling a multi-staged advertising campaign that first targets early adopters and later targets the mainstream is one effective way to use this knowledge of human behavior to increase your odds of success. There are no guarantees in life, but smart thinking can start to stack the odds in your favor.