Hacking the Endowment Effect
Loss Aversion, Scarcity, & Customer Acquisition
Understanding Loss Aversion
“Loss aversion” is a person’s tendency to value what they already have more than what they will gain. People are far less willing to part with objects that are already in their possession, than to gain new items — even if the items gained are more valuable. People are also far more willing to exhibit risk-seeking behaviors to avoid a loss. Loss aversion is easiest to understand with a few examples:
Ex: Stock Traders
Even stock traders fall susceptible to the endowment effect. Stock traders consistently exhibit loss aversion behavior by keeping their worst performing stocks in their portfolio and selling their better performing stocks. Traders would rather “let it ride” with their poor performing stocks and take the risk that the stocks will bounce back, instead of “cutting their losses” on the under-performing stocks (and accepting a realized loss).
Over the long term, this of course means that the trader’s portfolio is increasingly populated with under-performing stocks. The UC Berkley economist Terrance Odean discovered that the stocks traders sold outperformed stocks in their portfolio by 3.4%.
Ex: Outbreak Physicians
We typically assume physicians to be the most rational of professionals, yet they have proved to be just as likely to exhibit loss aversion as the rest of us muggles.
In a study by Nobel Prize economists Tversky and Kahneman, two samples of doctors were presented with two hypothetical scenarios. In the each scenario doctors were presented with a situation regarding a hypothetical outbreak:
The U.S. is preparing for the outbreak of an unusual Asian disease, which is expected to kill 600 people. Two alternative programs to combat the disease have been proposed. Assume that the exact scientific estimates of the consequences of the programs are as follows: If program A is adopted, 200 people will be saved. If program B is adopted, there is a one-third probability that 600 people will be saved and a two-thirds probability that no people will be saved. Which of the two programs would you favor?
Of the the large sample of doctors surveyed only 28% elected for the riskier program B. The large majority of doctors supported the risk-averse behavior of certainly saving 200 people.
When the exact same question was restructured and presented in the form of a loss:
The U.S. is preparing for the outbreak of an unusual Asian disease, which is expected to kill 600 people. Two alternative programs to combat the disease have been proposed. Assume that the exact scientific estimates of the consequences of the programs are as follows: If program C is adopted, 400 people will die. If program D is adopted, there is a one-third probability that nobody will die and a two-thirds probability that 600 people will die. Which of the two programs would you favor?
The percentages were flipped, with 78% of physicians opting for the riskier behavior of program D.
In reality both scenarios are exactly the same. Saving only 1/3 of the population is the same as losing 2/3rds, and yet — when framed in terms of a loss, doctors were significantly more willing to take a risk.
Scarcity and Value
What have nightclub lines, country clubs, and misprinting stamp manufacturers figured out long ago? That an object’s value is increased with its scarcity.
Scarcity & Facebook Gifting Applications
Ankur Napal, Facebook polluter extraordinaire, leveraged scarcity to improve engagement with his Facebook gifting applications. Ankur adjusted the CTA copy in one of his gifting applications from “Send all your Friends a Hug” to,
“You Can Only Send 20 More Hugs Today.”
The simple change had a small effect on the number of users that would send their friends virtual gifts, but had a dramatic affect on the engagement rate of users that were already sending gifts to their friends. Ankur says that many even started hitting the artificial limit.
By creating an artificial scarcity of gifts, the users valued each virtual hug more than when they had an endless amount.
Marketing Applications & Customer Acquisition
So what does all this behavioral economics have to do with marketing SaaS applications and customer acquisitions?
It tells us that customers are more willing to take action to prevent a loss than to gain something, even if ownership of the thing is implicitly granted.
It tells us that users value something more when there is less of it, and when they can’t get it back.
So how can you engineer your application to hack user behavior? I’m not entirely sure, but like any good marketer I intend on testing my assumptions to find out.
Think of Something to Give Your Users
I’m currently helping my fiancé re-engineer her marketing process for her photography business. It isn’t new for portrait photographers to include a credit with every session to use towards the purchasing physical prints, and we won’t be the exception.
As soon as we build the functionality that allows purchases to be made directly through the site, we will provide clients with a number of credits that they can use toward purchasing physical gifts for friends.
Make it Valuable
Think of what would be a good number of conversions that you would like to hit inside of your application and then reduce that number by about 1/3. For Heather’s site, we will likely create a “closed beta” version of her application that makes users wait a number of days after creating an account before they are granted their credit and the ability to purchase physical prints from her store.
This gives us the added benefit of working with a smaller, limited number of users as bugs come up, and allows us to non-automate specific portions of the application in a lean way, before finally baking them into the application.
Threaten to Take It Away
If we give each client a $40 credit towards the purchase of physical prints, and they don’t use the credit in a certain time period, then it’s safe to assume that the user doesn’t want the credit, right?
By creating our own currency inside the application we can create a system that incentivizes our desired user behavior. We can communicate to our users that their credits have a “use-by” date. If our users don’t redeem their credits by the date, we may “transfer” them to another user that has already exhausted his or her free credits. By framing the incentive in terms of a loss, the user will exhibit more willingness to engage with our application (risk-seeking), than if the incentive was framed in terms of a gain.
Have you leveraged the endowment effect to incentivize user behavior? I’d love to hear how you have framed incentives in terms of a loss at @jordanskole on Twitter, or over on Google+.
