Questionable A/B Tests Using Dark Patterns
Picture a normal triangle in your head. Now picture a triangle without area. Can you? In mathematics, a degenerate triangle is defined as follows:
A degenerate triangle is formed by three collinear points. It doesn’t look like a triangle, it looks like a line segment.
It’s as if the three angles of the triangle are flatten. In theory, the triangle exists but all that remain are overlapping linear lines.
While this post is not about mathematics, it will attempt to classify certain A/B testing methodology as degenerate using patterns that approach a line of user deception also known as dark patterns. Then, we will review questionable large scale tests that had happened in the tech industry. Finally, we will conclude by giving recommendations on how we can avoid this deception by making clear statements to our users.
Before we begin, let’s discuss what A/B testing is.
What Is A/B Testing?
A/B testing is loosely based in statistical mathematics and theoretical psychological experimentation. There is a control “A” and a variation “B”. An author of a test sends these two types to a controlled percentage of users for a variation of time in the product under test. The percentage and groups of users can be controlled by targeting.
Once the A/B test is completed, the data is collected and analyzed. The analysis allows the team to steer the product in a direction based on the results. These data points are inferred by the team; driving performance indicators such as customer retention and growth, call to action, revenue, conversion or to find new indicators.
Traditionally A/B testing acts on user experience, visual design, or other routes of modification of the targeted system to attempt at reducing user friction and to maximize engagement. Most tests are on the surface where the colors, layouts or navigation flow/behavior are altered.
A/B testing has gained a particular perception. Consultancies have formed businesses around the discipline, just how they formed around search engine optimization (SEO). SEO was popular in the 2000’s, A/B testing is now popular in the 2010’s. While SEO was beholden to secret algorithms that can immediately change from direct control of companies like Google, A/B test logic are owned wholly by the authors who think of and execute them to meet goals for their product and stakeholders.
Data collection and tooling has increased dramatically over the past few years. The authors of A/B tests may have increasing power to walk a fine line on a code of ethics. Let’s explore this next with defined high level dark patterns, with the first classification that leverages urgent platform tools.
Pattern 1: Act On Platform Tooling Urgency
Those that have used Android or iOS applications know well what a notification badge is. Notification badges are visual indicators on the app icon that cue the user to some type of important event inside the app. This is usually a brightly colored circle with a number associated with the notifications waiting.
Imagine if we push a badge with a deceptive count to represent subjective or vague notification activity. Even when the user is logged out of a service, the user may receive these badges without user activity. The badge is an engagement lore.
It happened to a group of users. The users repeatedly engaged with the application to understand what was happening in an app. They engaged many additional times since the notifications where loosely linked to the indicator.
Motto: “Urgent Notification”
On mobile platforms, that notification badge is one of many routes to execute an interesting A/B test. The platform tools will vary, but each platform have high level indicators for engagement that users are comfortable and knowledgeable with. The users are wired to respond and engage.
Next, we will fake things.
Pattern 2: Fake Platform Until We Build It
Of course, there are many parts of your digital product that haven’t been built intelligently yet. However, we want to increase engagement in some way.
Recently a group of users were navigating a site by actively looking to watch a particular genre of movie. As the users typed in the search result, they were steered to partial matches.
The users engaged further with similar content.
Motto: “Canned Search Results”
Developing the final polished product is something we can fake in many ways, and this includes search results. Instead of investing heavy in the act of the perfect feature, we assemble the results to simple categories based on previous research.
Now, let’s test with scarcity.
Pattern 3: Make Platform Things Scarce
Manipulating critical numbers such as item stock, content number, ratings, time, is well known to consumer marketing. While these tricks may work temporarily, they may backfire. However, we do have the power to adjust numbers to temporarily isolate and to understand the further dynamics of engagement.
Recently a group of users had been shopping on a website. The users seen quite a lot of different setups for commerce sites.
Let’s say that one of those items that the user absolutely needed was in stock, but only “one left”. Of course, the user acted quickly and purchased in a timely matter.
The next day the user returned to the site to research another similar purchase. Sixteen items where available.
Motto: “Everything Is The Last Item”
It does not have to stop at quantity of items and can be stretched to whatever the product desires. Use the platform limitations specifics to the product advantage. Low space, stock, rating, and a lot of things are adjustable. If your team owns a video platform, “expiring” content is subjective. Finally, anything as a number can be rounded.
Next, let’s test by giving away.
Pattern 4: Give Platform Expectedly or Unexpectedly
Sometimes if we give or take away major items of the platform it may manipulate the engagement.
A group of users were recently on an application that offered paid content behind a pay wall. This app was freemium in nature.
Access became “unlocked” and a group of users were able to view more content for free without the paid subscription. Then time passed and it was gone.
Some users wanted more, as they saw what they could have. They engaged.
Motto: “Curious Engagement Through Shock”
Opening the gates in an A/B test could be a potential opportunity to engage more users. The process here is to select known identities to engage them further.
Additionally, moving or closing things unexpectedly may cause more engagement and may increase return. This is because if services that are relied on go missing, those users will return more frequently until it re-established.
Finally, we can hide things.
Pattern 5: Platform Information Hiding
One final example is the creative use of information. Here, we conduct an A/B test that manipulates information that may be relevant to the users at the proper time and context. We either shift the information upstream or manipulate it so that it is diluted. We reduce the friction of information to obtain a result. Sometimes lack of information is that friction.
An example is if a service relies on the live location of the available liveries in the area — maybe those that are parked or on break, increasing that information to the user may deliver more engagement.
Group: “Shift, Adjust and Filter Information”
We will try to adjust information or direct the user into some desired action by either giving or taking information away at measured points. Taking it a step further in tests, information manipulation can be targeted to identified groups of users.
This wraps up the pattern examples. Urgency, faking, scarcity, giving, hiding, are subjectively divisive. Are they unethical?
Degenerate Tests May Be Unethical Tests
In mathematics, a degenerate triangle exists in the overarching term of degeneracy cases:
A degenerate case is a limiting case in which an element of a class of objects is qualitatively different from the rest of the class and hence belongs to another, usually simpler, class. Degeneracy is the condition of being a degenerate case.
The degenerate triangle is deceptive and so may be the patterns defined above. All tests are doable without the user understanding it had happened nor platforms catching authors in the act. These A/B tests may be in a degenerate class of the typical e-commerce standard tricks such as acting on urgency, scarcity, and human fallacy. These techniques are, in most cases, harmless but act on deception. The question posed here is when the human is unknowingly participating in a test, is it ethical to run these tests?
The question may have already been answered because large technical companies have published their results.
Some years ago, both Facebook and OkCupid deceived their users by running controversial A/B tests surrounding content engagement and companion matching. Then, they posted the results to the public. Many opponents suggested that manipulating emotions and incorrectly matching incompatible date companions was wrong. This started a very long chain of responses from the community.
One excellent research paper focused on this test fallout. You can read it here. Raquel Benbunan-Fitch dubbed these as A/B/C/D testing, where she states:
This is a deep form of testing, which I propose to call Code/Deception or C/D experimentation to distinguish it from the surface level testing associated with A/B testing.
Are the tests ethical? It’s inconclusive as the debate rages on. However, let me suggest some ways in-which we can state our testing code of conduct.
Ethics May Start By Saying We Are
While most of the industry is attempting to figure out how to roll out and scale A/B testing to their advantage, I’d like to define how we can avoid these types of C/D tests. Here are a few ideas that are centered around communicating the use of live user testing.
- Clearly specifying an A/B testing section in a Terms of Service.
- Publish an ethical statement and guiding principles of A/B testing.
- Visually indicating an A/B test is being performed with opt-out.
The messaging would be up to the team or company to decide, but it must be clear and simple to the user. The boundaries of testing must be clear.
For item three, many test evangelists would agree that this behavior would highly disrupt the results and the test would not be valid. The test author does not expect the user to feel that she is in a test “A” or “B” mode. The author wants the user to act as normal in order to measure correctly.
Now while that might be true, we would still want to give the option at a more appropriate time, perhaps in user preferences or at sign up. We want to satisfy the users concern and at the same time measure and sample to help improve the value of the product.
This is because our intention is to do just that.
In the future, state and government agencies may step in by defining limitations to C/D testing as public knowledge increases and more problematic tests are uncovered. Companies may be caught performing highly degenerate tests that none of us could fathom.
Many may ask what would be the consequence of being deceptive? At this time, there are no examples of crossing a line. We do have the power to ask whether to continue to professionally associate with the authoring team that knowingly deploys these increasingly questionable tests or opt-out of purchasing in, as a customer that is aware of the deceptive practices.
As always, time will tell how this shakes out. Data, tooling, and their alignment will continue to increase testing power. Public awareness will engage by asking hard questions. If there is one lasting advice, try not to deceive… too much.
If you are interested in more information on the subject of dark patterns, check out https://darkpatterns.org/.