How people think about buying new products
They first think about what they lose, not what they gain. Here is how to get them to switch to your new solution.
In marketing a new solution, you are asking a consumer to switch from their current solution. Research in behavioral science clearly shows that consumers will immediately consider what they lose by making a switch. Much more value is placed on the status quo….the product they have, rather than the benefits of a new solution.
You are cursed with an enormous amount of knowledge of your solution. So it’s hard for you to see how a fresh mind will evaluate a shiny new thing.
Using the research on the psychology of human decision making will help you understand how these fresh minds (your target consumers) think about switching solutions. Armed with this approach, you can architect for a much larger increase in solution switching.
To illustrate, let’s start with an example around how you manage passwords.
Access
Using the web and mobile apps, you often need personalized access to these apps to save your data and information. Think Facebook, Instagram, your banking app, Amazon, etc.
This personalized access is the job-to-be-done. It’s stable over time. The solutions you choose change.
To solve personalized access, you usually create accounts with usernames and passwords.
You probably have a mental security model for how you decide and manage what usernames and passwords you pick. You might have a variety of different ways to save and recall those passwords (even writing them down?).
You have undoubtedly heard about sites being hacked with usernames and passwords being compromised.
So it’s probably crossed your mind that maybe they got one of your secure passwords from your mental security model.
Let’s say you see or hear about a new password manager from a company called NewPassCo. They claim to make it much easier and safer for you to manage secure usernames and passwords. Based on hearing about this new app,
Would you sign up for the NewPassCo app?
Quick, make a mental note (and written if possible) of all the things that come to mind when you read that question. Things like: what features and benefits you think it has, how would you get your current passwords in there, is it more secure, and so on.
Archive this and come back to it after you read through the following discussion. This will help you see how you think about switching and therefore how it applies to the Switch Mountain Hypothesis.
Switching Solutions
First and foremost, people have a job-to-be-done. They hire a product or service to get that job done. The job-to-be-done causes a purchase.
But once they have made a purchase, what would cause them to switch to a new solution to solve the same job? How can you get the target consumer to fire the current solution and hire your solution?
This is where the behavioral science kicks in. To have a more structured way to think about switching solutions, I’m building on top of existing research and presenting the Switch Mountain Hypothesis:
Switching solutions is like getting consumers to hike up to a new mountain plateau. Initially, any movement brings up a fear of falling with little offsetting gains of getting to a higher plateau.
Over time, a series of events causes some consumers to determine that the gains of switching to a higher plateau are greater than the risk of losses (falling).
Conceptually, the Switch Mountain Hypothesis looks like this:
The general inertia is to do nothing and stay at the current solution plateau, the reference point from which switching is evaluated. Losses (falling) are more terrifying than gains (hiking) when initially thinking about moving off the current solution plateau. Switching to the new solution takes time and a series of events to help weight gains more than losses.
The Switch Mountain Hypothesis is built on top of a well researched, evidence-based theory in behavioral economics called Prospect Theory by Daniel Kahneman (2002 Nobel Prize winner in Economics) and Amos Tversky.
The Hypothesis also builds on the JTBD (job to be done) Timeline from the Re-Wired Group. They have done thousands of interviews trying to understand why people bought all sorts of different products. From that work, they have found that the decision to switch evolves over time as is usually mapped to a few distinct events. Additionally, the Re-Wired Group developed the JTBD Forces of Progress diagram. I translated the Forces into the relevant behavioral decision and social forces at play during the switch.
Switching solutions is more emotional than logical and rational. Consumers are not even aware of the emotional and mental processes at work influencing their decisions. Behavioral science has the research, theories and tools to really help make the mountain hike of a switch more of a science and less of a guess.
Generally, we need to bring more science to innovation. See here.
The Focus on Switching
A focus on switching solutions is much more telling and action oriented. Specifically:
A switch assumes a comparison; more specifically a reference point. A person has to stop doing something (fire) and start using (hire) the new solution. The person perceives they have to give up a whole range of things for the possible (risky) new solution benefits. Understanding this perspective is critical to design, development, marketing and sales.
Marketing a new solution with a focus on the features and benefits misses the behavioral dynamics of how consumers evaluate benefits. Specifically, the losses and gains from the current solution reference point. Maybe you need to market (and design) for the losses of what they have to give up. For example, the new smartphone wallets from Apple and Google. Do you immediately say: “Yea, all upside. I love it.” You have a comparison/reference point and you wonder about what you are giving up first before thinking about possible gains. Cash, drivers license, health card, special notes, odd keepsakes, kids pictures, etc. Why market it as a replacement? How about a tool that thins wallets rather than a wallet replacement? This approach sets a different reference point.
As the Re-Wired Group likes to say, “You have to see the energy from switching.” There is a story for the switch. Uncovering that story (e.g. screenplay) let’s you build a movie poster for the unswitched to follow. You can also identify segments by looking for common stories.
The emotion of a switch provides the indicators to uncovering the type and strength of behavioral decision forces (the irrational stuff) influencing a switch. “Man is not a rational animal, but a rationalizing animal”, says science fiction writer Robert Heinlein (from The Business of Choice). Standard economics assumes rational decision making. Actual decision making is “irrational” in the eyes of behavioral economics.
If you completed research before designing and developing your new solution, you will probably use that data to frame your marketing. Study after study shows that what people say they will do are poor predictors of what they actually did in a given situation. You need to analyze actual switches, through interviews, to understand what people actually did rather than what they said they were going to do. Your future self is more rational and aspirational than your present self: an intuition oriented, emotion filled action hero.
It is tempting to ask people which option they prefer among concepts for a new product. You can get actual answers that seem strong and articulate. The problem is that people don’t have reliable insights into their mental processes in future situations. As a result, they can’t predict what they will do in a given situation. A lot of intuition is at work (see System 1 vs. System 2 seminal work by Dr. Daniel Kahneman). Debriefing someone on why they switched allows you to uncover the mental processes (biases, heuristics and situational factors) that caused them to switch.
Back to NewPassCo
To continue to illustrate how the Switch Mountain Hypothesis works, let’s go back to NewPassCo.
Let’s now change and assume you work at NewPassCo. You’ve been hard at work for the last year designing a great password manager for desktop and mobile users. You love all the innovation and are proud of the ideas you and the team have dreamed up and implemented. You are close to launching.
You know that many consumers use only a few passwords and rarely change them. And, with so much press about hackers, you know that a growing segment (call them the Concerned Consumers — CCs) really needs a better solution, like NewPassCo.
So when you announce NewPassCo, will the CCs switch from a using a few common passwords to the new solution? What goes on in the mind of the CCs if they hear about NewPassCo? Will they jump right on it and download a free trial?
The Job to be Done and the Current Solution
The CCs are already performing a job of visiting websites and apps that require personalized, authenticated access. The job will be around for a long time. It’s stable. The solutions will change.
The solution is usually usernames and passwords. And on the Mac, the default, built-in choice is Safari Passwords; a feature of the Safari browser that saves passwords when you enter them in a form on a web app.
Here is a way a CC might think about how they are executing this job (see if you wrote many of these things down when I mentioned this case at the start of this article).
- I have 50+ sites/apps to access with username and passwords.
- I can’t keep unique ones in my head for all of these.
- I can easily keep 3 to 5 unique passwords in my head so that is safer since nothing is written down. I’ll use these at all the sites and apps.
- My 3 to 5 are unique. No one could guess them [a justification that goes against the data]
- From time to time I’ll change a password at a site say from password 3 to password 4. That should keep me safe for now.
- Most of these don’t have much of my personal data so if I’m hacked, there is a good chance they will not get something valuable.
- I’ll use my “hard” password at important money sites (Amazon, Apple, PayPal, Bank, credit card, etc).
- The odds of a hacker picking me out of the huge numbers of people are low.
Given all these points, the CC doesn’t appear to have much incentive to switch solutions. Would the CC want to lose all these things? In fact, the sum of all these points creates the reference point by which any new solution get compared. And, as you will see, the comparison is largely psychological with much of it not even conscious to the CC. [So how then would you extract this information in a focus group?]
The Prospect Theory Building Block
Prospect theory helps explain and predict behavior. Put another way, it is a evidence-based behavioral economic theory that describes the behavioral bias common to most decision making.
Prospect Theory was created and developed in 1979 by Dr. Daniel Kahneman and Dr. Amos Tversky in a paper: Prospect Theory: An Analysis of Decision under Risk.” Most importantly, it has stood the test of time. Meaning, many experts have deeply analyzed the theory and found it to still be predictive and explanatory.
Prospect theory’s fundamental finding is that people make decisions based on the value of gains and losses from a reference point. And further, people are loss averse. That is, they don’t like losses, a lot. The pain of loss is more than twice the pleasure of the resulting gains in most cases. It’s called loss aversion and it impacts your decision making every single day.
Diagrammatically and mathematically, Prospect theory looks like this:
Here are the key points:
The red loss line is steeper than the blue gain line. This means that $100 loss is 2x more painful than a $100 gain. Note the size of the boxes to visually compare. This explains why if someone offered you a bet, via a coin flip, of heads you win $100 and tails you lose $100, you likely won’t take it. The risk of losing the $100 is more painful than the pleasure of gaining $100.
Losses and gains are evaluated based on a reference point. In jobs to be done, the reference point is usually the current solution. So NewPassCo gets evaluated based on the losses and gains from Safari Passwords. This means that a CC is evaluating NewPassCo in reference to the gains and losses they trade from Safari Passwords. The CC does not consider the absolute value of NewsPassCo as a standalone solution. It’s relative to the reference point. At this point, one might ask: can we change the reference point to influence switching? Perhaps situational changes can impact the reference point and the resulting gain/loss analysis?
Conventional marketing says to emphasize the positive (e.g. gains) aspect of a solution. Prospect theory suggests that you should address the negative (e.g. losses) directly and how a consumer could minimize the losses. For NewPassCo, what about suggesting to keep using Safari Passwords while the CC starts using NewPassCo. Then they slowly transition in to adding a new solution, not just dropping Safari….and incurring all the losses.
People rate gains and losses differently. In mathematical terms, they put a decision weight on the probability of the gains and losses. In switching, that’s part of the discovery: what is the loss and what is the weight assigned to that loss. Uncovering this impacts your solution and its marketing. People overweight low probability events and underweight high probability events. (Lottery tickets play off people’s overweighting of low probability events….winning millions.).
Given the flattening slopes of the red and blue lines further out. This suggests that consumers assign less value further out.
Learn how customers weigh gains and loses. When a person has to weigh many gains and losses for a new solution, the objective is to find some way to get them to put less and less weight on the sum of the losses so that the sum of the gains exceed the losses. As you will see, the JTBD Timeline process does just this.
For NewPassCo, the reference point is Safari Passwords. Thus, when the CC hears of NewPassCo, they ask how is it “better” from Safari Passwords? More specifically, their mind is working to understand the losses and gains from switching.
Switching: The First Fire Thought
By using the building blocks, we can start to create a picture of how the CC thinks about solution switching over time.
The CC hears about NewPassCo. This is the First Fire Thought. It is on the JTBD Timeline. That is, it is the first time the CC has thought about firing Safari Passwords and hiring NewPassCo. Passive looking begins after this event.
But, from the Switch Mountain, can you quickly see that the losses from the First Fire Thought event are much greater than the perceived gains. Let’s describe these loss and gain decision forces.
The Switch Losses
Loss Aversion: This is the fundamental discovery from Prospect Theory. People hate losses much more than they enjoy pleasure from gains. As Kahneman puts it: Losses loom larger than gains. So on the First Fire Thought event, the losses of the current solution come to mind quickly. For a CC, things like: how do I transfer my passwords? How do I use NewPassCo compared to Safari? Will someone more easily steal my passwords if they are in one password manager, and more.
Status Quo Bias: Like in physics, things at rest stay at rest. There is a lot of inertia to just stay with what you are already doing, the current solution. Given that evaluating gains and losses takes some cognitive load, it’s no surprise that people tend to prefer to keep things as is…the status quo. A change from how things are is considered a loss on initial thought. No risk if you don’t change, or so says system 1 in your mind.
Endowment Effect: Discovered by Dr. Richard Thaler, it says that people assign more value to things they already own. You have already invested time in acquiring, thinking about it, using it, etc. so why give that up (the sunk cost effect is related here). There is a degree of pride in what you own, the fact that you understand it, you can explain it, you know the pros and cons, you made the decision to acquire it. The solution (or item) has more certainty than the marketing promises of a new solution…which often don’t live up to the perception.
The Switch Gains
Acquisition Utility: Also discovered by Thaler, this is the gain a person feels from acquiring a new solution. The Re-Wired Group calls it the “Pull of the Situation”. That is, when the consumer is in the situation, they still struggle with executing the job using the current solution. A new solution, that solves some of those struggles seems enticing. It’s pulling me. Acquiring a new solution has acquisition utility as the consumer feels it can reduce the struggles. Acquisition utility arises probably after Event 1 or Event 2 in the JTBD Timeline.
Availability Heuristic: If a person can immediately come up with examples, then it must be important and as a result weighs more heavily on a decision to switch. Multiple news stories of hackers accessing passwords of web and mobile apps increases the “availability” of information on hacking. Thus, when the CC hears of NewPassCo, the perceived the gains will be much greater. The CC can quickly think of these “available” news reports of hackers. Additionally, if a friend got hacked, availability increases. News stories create Push forces as shown in the JTBD Forces Diagram.
Optimism Bias: This is a cognitive bias that causes a person to think they are less at risk of achieving a negative event compared to others. Clearly this can work both ways in the case of a CC and NewPassCo. Initially a CC believes a hacker won’t get their passwords. Later, as more news stories arise and a friend gets hacked, the CC changes the optimism to thinking about how much safer they will be with a new solution.
Decision Weights for Gains and Losses
The CC may be on a path to switch, but it will take longer (perhaps another year) depending on when the next Event happens. For switching to occur, the minimum condition would be this formula in the mind of the CC.
Sum of Weighted Gains > Sum of Weighted Losses
The CC puts a decision weight (and corresponding probability) on all the gains and losses associated with a switch. Progressively, the CC switches the weighting (and/or the criteria) to move up Switch Mountain.
In many cases the weighted gains need to be greater, by some percentage, than the weighted losses, perhaps by 50%:
Switch Value >= 50%
where Switch Value is calculated as: (Sum of Gains/Sum of Losses)-1.
Let’s say you interviewed the CCs after they experienced First Fire Thought and obtained the following criteria and decision weighting:
In the case above, the CC is weighting the losses much higher than the gains so it’s not close to the 50% of gains over losses to achieve switching. Specifically, gains less losses are -75% (20%- 80%). [There are other weighting schemes that could be used.]
In some sense, the mind of the CC has put the summed gains and losses into the overall decision weights like this:
- Losses 80%: Loss aversion, status quo bias, endowment effect
- Gains 20%: Acquisition utility, availability heuristic, optimism bias
As Prospect Theory points out, people put disproportionally more weight on criteria that create losses from the reference point compared to the weight they put on gains. As further Events occur, the people would have to put less and less weight on the losses in order for switching to occur.
Switching: Events 1 and 2
Let’s say a friend of the CC got hacked. This is Event 1. It changes the weighting of gains and losses such that gains are a little stronger and losses are a little less. The CC is more actively considering hiking the Switch Mountain.
From this picture, you can see that the CC has now weighted the losses of switching higher up on the loss line (Event 1 is above First Fire Thought) and the gains further up the mountain towards the new solution. The friend that got hacked caused the CC to put less weight on things like keeping the 3 to 5 unique passwords, backing off on their belief that no one would guess a password and perhaps a few other things from the list above.
Let’s say some time after the friend got hacked, the CC get’s one of their accounts hacked as a result of a web app getting all their passwords exposed to some hacker group. This is Event 2. Now the CC is actively deciding what solution to pick. The have conceptually moved up switch mountain.
Switched
After the different events, the CC has switched to the new solution. They hiked the mountain by progressively weighting the losses less than the gains through a series of events.
The gains, influenced by the events and increased acquisition utility, caused available gains to be easily seen and heightened the optimism that things would be better with a new solution.
The gain decision forces beat out the loss forces, for some segments of CCs looking at NewPassCo. Switch value was greater than 50% (SV >.5).
Plenty of CCs won’t hike the Switch Mountain to NewPassCo. The loss weighting that drives loss aversion, status quo bias and the endowment effect causes those CCs to stay with what they have.
The Switch Starts Again
Once switched, the CC is on a new plateau. It’s another switch mountain with the next solution making an attempt to get the CC to hike. Recall that the job to be done, personalized access, is stable. The solution changes. But with each change, there is another switch mountain to hike.
By looking at solution switching this way, you can start to think differently about how do design, develop, market and sell your solution. It can affect the entire business design, including the business model.
Summary
Consumers have jobs to be done. They hire solutions. They fire their existing solution when they switch. The job to be done is still the same, even with the solution switch.
Behavioral science provides the framework for how you can improve your chances of getting a consumer to switch to your solution.
The Switch Mountain Hypothesis is an actionable framework for improving your switch conversions.
Consumer decision making for solution switching starts from a reference point (the current solution) and evaluates the gains and losses from that point. Losses are more painful than gains so they are weighted more than twice as heavily in the consumer’s mind. Design and market for this.
Losses map to: loss aversion, status quo bias and the endowment effect.
Gains map to: acquisition utility, availability heuristic and optimism bias.
The switch process proceeds over time through a series of events. The events cause the consumer to minimize the perceived losses and increase the perceived gains.
Once switched, the process can and will start all over again.
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