Don't Be A Creep
Be aware of invasiveness in Anticipatory Design
Imagine, however outlandish the scenario might be for you, that you're a high school girl. Like any other teenager, your hormonal imbalance and boundless curiosity is driving you to explore some intimate activities with another person. Nothing wrong with a little experimentation, right?
By some unfortunate chance however, you end up pregnant. And of course, because all kids like to shield their private lives from the all-seeing eyes of their parents as much as they can, you decide not to tell them anything about it. You're just not ready for that conversation yet.
This all goes relatively well — as well as can be in a scenario like this — until one day a nearby discount store starts sending you vouchers for baby clothes and cribs. Your father, obviously outraged that a company would send such ridiculous marketing material to his young daughter, marches over to the store to yell at one of their employees about "encouraging teenage pregnancy".
By now you realize you're running out of time and options. The only course of action left is to come clear and inform your parents of what's been happening behind closed doors in their home. All because of some flyers.
That sounds pretty far-fetched, right? What if I told you that actually happened? Andrew Pole, a statistician at Target, was able to develop an algorithm that assesses sudden changes in the purchase patterns of customers and accurately predict which ones might be pregnant.
Whilst these patterns mostly stay the same throughout the majority of our lives, a pregnancy is one of the few moments where purchase habits are in a state of flux. Exhausted and overwhelmed parents start buying products that are unusual for their normal spending habits, like maternity clothing or prenatal vitamins.
By leveraging their customer data and acting on these sudden changes in spending habits, Pole and thus Target were able to correctly predict pregnancies amongst their shoppers at a stunningly high success rate. And it worked out perfectly fine for Target, helping them establish long-term brand loyalty and influencing their customers shopping patterns.
Whilst Target was able to apply what is a perfect example of Anticipatory Design in action, their end user ended up suffering an uncomfortable and invasive experience.
In a previous article about Anticipatory Design we took a look at how companies can leverage their user data to take decisions on the user’s behalf. Whilst the article mostly talked about the applications in digital products, the example of Target provides a perfect example of anticipating customers needs in a physical product (a storefront, in this case).
From the moment that Target's algorithm picks up on early indicators of a pregnancy, it goes into full overdrive. This period marks what is possibly the single most important time period for binding customers to the company and ensuring a lifetime of brand loyalty.
Target’s misstep in handling their customer’s situation however, also outlines one of the pitfalls of Anticipatory Design. In offering coupons and vouchers for pregnancy-related products to a high-schooler, even though they correctly predicted that the customer was pregnant, they crossed the line from being beneficial to being uncomfortable and invasive.
In a research project on data privacy, Huge came across this same boundary between being helpful and beneficial versus creepy and invasive, and dubbed it the Creep Line. One of the things they found is that most users are not surprised by the amount of data that companies have on them. They’re not particularly concerned about this data either and simply acknowledge that this is the status quo.
Whilst the cost of supplying companies with our personal data is an unpleasant part of using digital products, we accept this as a necessary cost of business. Even though we’re aware of the fact that a company like Google has an enormous amount of information that it harvests from our use of their search engine, email client, online maps etc., we accept that this data buys us the convenience of using their products.
Because of this sheepish acceptance of the way our data privacy is handled, companies are seemingly growing more and more apathetic towards the privacy of their users. And thus, we start seeing scenarios where companies — like Target in the introductory scenario — knowingly cross this creep line. The prospect of binding a customer to their product for a lifetime was infinitely more attractive than the idea of respecting and protecting the precarious private situation of the customer.
The problem doesn't necessarily lie with the collection of data. As mentioned, we're mostly aware of the data collection and the fact that companies are leveraging this user data to analyze the ways we interact with their products. The problem occurs when companies seemingly forsake fostering an intimate relationship with their customers for the sake of driving up their sales.
As we make advancements in the field of Anticipatory Design and continue to get better at predicting user behaviour and anticipating their needs, the demand for an intimate relationship with the user grows.
The user has to believe and feel that they are getting some benefit out of handing over their personal data and allowing the product to predict what they might desire, based on this data. When the user feels secure enough to share their personal data with a product, it means they place a certain level of trust in this product; An understanding that their data will be handled sensitively and sensibly.
This trust is also what allows the product and the people building that product to be wrong at times. When you're in the business of anticipating or even predicting human behaviour, you're bound to make a mistake at times. Humans can exhibit erratic and unpredictable behaviour, making it difficult to always be right on the money when it comes to predicting what they might want or do.
The way these mistakes are handled though, is what makes the difference in whether the creep line is crossed or not. There's a fine line between being wrong but mitigating the damage, or just plain wrong. The way a product and its people handle being wrong and making a mistake can be the defining factor that ensures that users will continue to trust and use the product.
Uber provides two great example of how not to handle a situation where the anticipatory design went wrong, by quadrupling their pricing during a hostage operation in Sydney or alienating thousands of their customers by continuing to operate and raise their prices during a taxi strike.
When looking at it from a technical perspective, nothing actually went wrong in these two examples. Uber's prediction engine saw a rise in demand and its surge pricing kicked in to respond to this. The resulting uproar, however, could have been mitigated by a more human approach to the ongoing situations.
The key to maintaining a good relationship with the user, is not just to make them feel comfortable enough to share their information in the first place. It means building and actively maintaining an intimate relationship between the product and the user. It means establishing a level of mutual trust, providing the user with a sense of security, and above all, making sure that the product provides a beneficial experience.
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