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The trigger is the actuator of a behavior—the spark plug in the engine. Triggers come in two types: external and internal. Habit-forming technologies start by alerting users with external triggers like an email, a link on a web site, or the app icon on a phone. By cycling continuously through successive desire engines, users begin to form internal triggers, which become attached to existing behaviors and emotions. Soon users are internally triggered every time they feel a certain way.  The internal trigger becomes part of their routine behavior and the habit is formed.

After the trigger comes the intended action. Here, companies leverage two pulleys of human behavior – motivation and ability. To increase the odds of a user taking the intended action, the behavior designer makes the action as easy as possible, while simultaneously boosting the user’s motivation. This phase of the desire engine draws upon the art and science of usability design to ensure that the user acts the way the designer intends.

What separates the desire engine from a plain vanilla feedback loop is the engine’s ability to create wanting in the user. Feedback loops are all around us, but predictable ones don’t create desire. The predictable response of your fridge light turning on when you open the door doesn’t drive you to keep opening it again and again. However, add some variability to the mix—say a different treat magically appears in your fridge every time you open it—and voila, desire is created. You’ll be opening that door like a lab rat in aSkinner box.

Variable schedules of reward are one of the most powerful tools that companies use to hook users. Research shows that levels of dopamine surge when the brain is expecting a reward. Introducing variability multiplies the effect, creating a frenzied hunting state, which suppresses the areas of the brain associated with judgment and reason while activating the parts associated with wanting and desire. Although classic examples include slot machines and lotteries, variable rewards are prevalent in habit-forming technologies as well.

The exciting juxtaposition of relevant and irrelevant, tantalizing and plain, beautiful and common sets her brain’s dopamine system aflutter with the promise of reward. Now she’s spending more time on the site, hunting for the next wonderful thing to find. Before she knows it, she’s spent 45 minutes scrolling in search of her next hit.

unlike a sales funnel, which has a set endpoint, the commitment phase isn’t about consumers opening up their wallets and moving on with their day. The commitment implies an action that improves the service for the next go-around.  Inviting friends, stating preferences, building virtual assets, and learning to use new features are all commitments that improve the service for the user. These commitments can be leveraged to make the trigger more engaging, the action easier, and the reward more exciting with every pass through the desire engine.

As Barbra enjoys endlessly scrolling the Pinterest cornucopia, she builds a desire to keep the things that delight her. By collecting items, she’ll be giving the site data about her preferences. Soon she will follow, pin, re-pin, and make other commitments, which serve to increase her ties to the site and prime her for future loops through the desire engine.

Humans, like the mice in Skinner’s box, crave predictability and struggle to find patterns, even when none exist. Variability is the brain’s cognitive nemesis and our minds make deduction of cause and effect a priority over other functions like self-control and moderation.

Recent neuroscience has revealed that our dopamine system works not to provide us with rewards for our efforts, but to keep us searching by inducing a semi-stressful response we call desire.

Email, for example, is addictive because it provides all three reward types at random intervals. First, we have a social obligation to answer our emails (the tribe). We are also conditioned to know that an email may tell us information about a potential business opportunity (the hunt). And finally, our email seems to call for us to complete the task of removing the unopened item notification in a sort of challenge to gain control over it (the self). Interestingly, these motivations go away as soon as we’ve actually opened all our emails and the mystery disappears. We’re addicted to checking email while there is still variability of reward and once that’s gone, emails languish in our inboxes.

We’re meant to be part of a tribe so our brains seek out rewards that make us feel accepted, important, attractive, and included.

But as sociable as we are, our individual need for sustenance is even more crucial. The need to acquire physical things, such as food and supplies, is part of the brain’s operating system and we clearly wouldn’t have survived the millennia without this impulse. But where we once hunted for food, today we hunt for deals and information. The same compulsion that kept us searching for food coerces us to open emails from Groupon and Appsumo. New shopping startups make the hunt for products entertaining by introducing variability to what the user may find next. Pinterest and Wanelo keep users searching with an endless supply of eye candy, a trove of dopamine flooding desirables. To see an example of how the hunt for information engages users, look no further then the right side of this page. There, you will find a listing of popular posts. Using intriguing images and short, attention-grabbing text, the list is a variable reward mechanism designed to keep you hunting for your next discovery.

We also seek mastery of the world around us. Game mechanics, found everywhere from Zynga games to business productivity apps like to-do lists, provide a variable rewards system built around our need to control, dominate, and complete challenges. Slaying new messages in your inbox stimulates neurons similar to those stimulated by playing StarCraft.

Variable rewards come in three types and involve the persistent pursuit of: rewards of the tribe, rewards of the hunt, and rewards of the self.

As B.F. Skinner discovered over 50 years ago, variable rewards are a powerful inducement to creating compulsions.

When people say"page load time" for a website, they usually mean one of two measurements: "document complete" time or "fully rendered" time. Think of document complete time as the time it takes a page to load before you can start clicking or entering data. All the content might not be there yet, but you can interact with the page. Think of fully rendered time as the time it takes to download and display all images, advertisements, and analytic trackers. This is all the "background stuff" you see fill in as you're scrolling through a page.

Since Google was not clear on what page load time means, we examined both the effects of both document complete and fully rendered on search rankings. However our biggest surprise came from the lack of correlation of two key metrics! We expected, if anything, these 2 metrics would clearly have an impact on search ranking. However, our data shows no clear correlation between document complete or fully rendered times with search engine rank, as you can see in the graph below:

With no correlation between search ranking and what is traditionally thought of a "page load time" we expanded our search to the Time to First Byte (TTFB). This metric captures how long it takes your browser to receive the first byte of a response from a web server when you request a particular URL. In other words, this metric encompasses the network latency of sending your request to the web server, the amount of time the web server spent processing and generating a response, and amount of time it took to send the first byte of that response back from the server to your browser.

The TTFB result was surprising in a clear correlation was identified between decreasing search rank and increasing time to first byte. Sites that have a lower TTFB respond faster and have higher search result rankings than slower sites with a higher TTFB. Of all the data we captured, the TTFB metric had the strongest correlation effect, implying a high likelihood of some level of influence on search ranking.

The surprising result here was with the the median size of each web page, in bytes, relative to the search ranking position. By "page size," we mean all of the bytes that were downloaded to fully render the page, including all the images, ads, third party widgets, and fonts. When we graphed the median page size for each search rank position, we found a counterintuitive correlation of decreasing page size to decreasing page rank, with an anomalous dip in the top 3 ranks.

We suspect over time, though, that page rendering time will also factor into rankings due to the high indication of the importance of user experience.

our data shows there is a correlation between lower time-to-first-byte (TTFB) metrics and higher search engine rankings. Websites with servers and back-end infrastructure that could quickly deliver web content had a higher search ranking than those that were slower. This means that, despite conventional wisdom, it is back-end website performance and not front-end website performance that directly impacts a website's search engine ranking.

Our data shows there is no correlation between "page load time" (either document complete or fully rendered) and ranking on Google's search results page. This is true not only for generic searches (one or two keywords) but also for "long tail" searches (4 or 5 keywords) as well. We did not see websites with faster page load times ranking higher than websites with slower page load times in any consistent fashion. If Page Load Time is a factor in search engine rankings, it is being lost in the noise of other factors. We had hoped to see some correlation especially for generic one- or two-word queries. Our belief was that the high competition for generic searches would make smaller factors like page speed stand out more.

TTFB is affected by 3 factors: The network latency between a visitor and the server. How heavily loaded the web server is. How quickly the website's back end can generate the content.

Websites can lower network latency by utilizing Content Distribution Networks (CDNs). CDNs can quickly deliver content to all visitors, often regardless of geographic location, in a greatly accelerated manner.

Do these websites rank highly because they have better back-end infrastructure than other sites? Or do they need better back-end infrastructure to handle the load of ALREADY being ranked higher? While both are possible, our conclusion is that sites with faster back ends receive a higher rank, and not the other way around.

The back-end performance of a website directly impacts search engine ranking. The back end includes the web servers, their network connections, the use of CDNs, and the back-end application and database servers. Website owners should explore ways to improve their TTFB. This includes using CDNs, optimizing your application code, optimizing database queries, and ensuring you have fast and responsive web servers.

Fast websites have more visitors, who visit more pages, for longer period of times, who come back more often, and are more likely to purchase products or click ads. In short, faster websites make users happy, and happy users promote your website through linking and sharing. All of these things contribute to improving search engine rankings.