Learn how to use Fogg’s behavioral model to make better product decisions.
I’m not a fan of most UX models and diagrams because they’re either too complex to be useful, or I never seem to be in a situation where I can apply them. But Fogg’s Behaviour Model is one of the models I return to most in my work as a UX consultant.
If you’ve never used it or heard of it, then let me explain it to you. I’ll also give you some examples of its application in UX and innovation.
We’re often working on a behavior
UX professionals are often trying to help more people do a thing. That thing might be converting sales, newsletter sign-ups, online passport applications, booking a restaurant table, or whatever. That thing is a behavior. We’re also working at an interaction level at times. Those interactions are also behaviors.
Fogg’s Behavior Model can be applied to almost any situation where you’re thinking about whether that behavior will or won’t happen and what you can do about it
Fogg’s model explained
Fogg’s model tells us that for any behavior to occur, motivation and ability must be high enough at the moment of experiencing a prompt. When motivation and/or behavior are too low at that moment, the behavior will not occur.
Behavior = Motivation x Ability x Prompt
A simple model to help understand user behavior.
In the diagram above, the Action Line is the threshold between the behavior occurring and not occurring. Some behaviors happen despite low levels of ability because the user’s motivation is high.
Likewise, users with low levels of motivation might still perform a behavior that’s very easy to do. But your users (or potential customers) are usually scattered across this graph in different positions for any given behavior. Some are near the action line at the moment of a prompt, others are well beyond it, while others still are far away from ever performing the behavior.
Ability has two sides
Some applications of the model
Here are some examples of how I’ve applied Fogg’s model and where it’s helped me provide explanations to my clients
1. Reducing friction to increase conversions
If you reduce the friction in an online process, you increase the user’s ability. The behavior becomes a bit easier for users to perform. If there are a number of people hovering near the Action Line, then your interventions to reduce friction in an experience can move them across the Action Line. A lower level of ability is needed to perform the behavior, and therefore, more people will perform it.
But sometimes, the reduction of friction has no impact, so why is that? You’re not fixing the issues that impact people close to converting. If you make something a little easier for a customer having a nightmare with every aspect of your online process, then the slight reduction of friction won’t help them.
When you understand the things that block people who are nearly converting, fixing those things is more likely to impact your conversion rate.
2. Why startups often talk to the wrong users
Early-stage startups and innovative products are first adopted by people with high motivation and/or ability to adopt them. These people either feel the problem very strongly and/or are motivated to try new ways of doing things in this space.
The Tech Adoption Lifecycle calls these people “Innovators.” It makes the mistake (in my view) of assuming they are all tech enthusiasts. But in my experience, some are just people with a painful problem. This pain motivates them to try a product that many other people wouldn’t have the motivation to try.
If you work on an innovative product and are trying to work out how to make it more accessible to the masses, you need to consider that your existing users are higher on the motivation and/or ability axis than the people you hope will adopt the product.
You need to understand more about the people close to adopting the product. Making changes to the product that accounts for their needs is likely to have a better impact. When startups continue to build for their first users, they often build a complex product that’s hard for others to adopt.
3. Why conversion rates often drop when traffic goes up
When companies are new to A/B testing and experimentation, they often make the mistake of thinking that their conversion rate is constant and will only fluctuate with changes to the design. They believe that doubling the traffic will double the overall conversions, for example. But that’s not how it works, and Fogg’s model helps us understand why.
Your conversion rate is determined by the motivation and ability of the traffic that your website/product receives. When that is stable, then so is your conversion rate. UX improvements enhance the ability of the existing users (traffic) by making converting easier and sometimes by motivating them a bit more. But when the mix of your traffic changes, then so will your conversion rate.
Spending a heap of money on Facebook ads might increase traffic but also decrease the conversion rate. This is because your extra traffic will likely have lower levels of motivation for the task at hand. These aren’t the same type of people who found their way to your product of their own accord. Instead, you pulled them toward it, and therefore, their levels of motivation and ability will generally be lower.
Sometimes, traffic does go up, as well as the conversion rate. This might happen seasonally. For example, a tax-efficient financial product (like a pension) might experience higher conversion rates and higher traffic near tax year end because a lot of very motivated people with a time deadline hit the site at the same time.
4. Understanding different types of innovation
I sometimes group innovations into one of two categories when thinking about them. Are they like contactless payment cards, or are they like Google/Apple Pay? The difference I’m talking about is how seamlessly users can adopt them. One type of innovation requires more motivation to adopt than the other.
The contactless card type of innovation requires low levels of motivation to adopt. This is because adoption slips seamlessly into the customer’s existing workflow. When you got your first contactless payment card, it just came as a replacement for your current card. You just had to pay with it to activate it using the same machines you always did. Instead of entering the card into the machine, you simply tapped the card on the top of the machine. Ability is very high because it’s the same as what you always do. Therefore, even people with low motivation might adopt it.
The Google/Apple pay type of innovation requires the user to deviate from their existing workflow to set up. They need to add a card to an app on their phone and start paying in a different way than they used to. Changing an approach requires more effort; therefore, innovations like this require more motivation for users to adopt. I finally set up Google Pay the day I was on a bike ride paying for coffee, and the card I’d brought declined. This was an embarrassing situation I didn’t want to repeat. Several years after using contactless payment cards, I eventually set up Google Pay. My motivation had never been high enough before then. Motivation is also hampered by concerns about the new way we’ve never tried before.
Both of these examples are successful innovations. But, failed innovation ideas sometimes do the job better than the existing approach. They just demanded too much of a change of the user’s existing workflow to adopt. Potential customers weren’t motivated enough to change their way of working, even if the innovation idea did the job a bit better.
Prompts don’t have an axis
Note that prompts don’t have an axis on the graph. I don’t find Fogg’s Model actually helps too much in shaping the design of prompts. Your prompts can affect motivation differently, but the model doesn’t help here. However, It shows us that the more prompts there are for different people, the more of the behavior there is likely to be. So long as the extra prompts fall within the action line that is.
Instead of thinking about prompts, think about how motivation and ability can be at different levels for different people.
Zooming in and out
Hopefully, these examples illustrate that Fogg’s Model can be zoomed into the interaction level but also zoomed out to the very existence of the product.
As the statistician George Box said on the year I was born (you’ll need to Google that to see how old I am)...
“All models are wrong, but some are useful.”
It’s often the simplicity of a model that makes it both useful and wrong. By cutting away at the unhelpful complexity, it becomes something we can actually use.