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Google already knows what you’re going to do
(before you even think about it).

Google already knows what you're going to do (before you even think about it).
From Uber to Amazon, more and more brands are looking to anticipate our next move before we have even thought about it. It’s a strategy that can really pay off, but it’s not without its risks.

It’s Saturday night and you’re going out to dinner. As you walk out the door, your phone tells you there’s a chance of showers at 22pm and that it would be wise to grab your umbrella.

You leave the building and launch the Uber app. Surprise: the address of the restaurant you’re going to has already been entered. And, as you start writing a text message saying "here’s the restaurant’s address" your phone automatically adds "... 36 rue de la Paix". It’s certainly impressive – but also quite worrying.

You have just carried out three proactive design experiments: the ability of certain applications to suggest information or services to you without any action on your part soliciting it.

Using your location, appointments in your calendar or usage habits, Google Now, Uber and Android (which now includes predictive SMS) are trying to predict your next action, before you’ve even thought of it.

Significant technological advances.

It’s tempting not to see anything new in this trend. After all, as SAP’s Sajid Sayed notes, “It can be argued that any form of user centered design is anticipatory design, because the designer creates something in anticipation of a user reaction.”

But so far, the history of interface design has focused largely on the mechanics of “user action – machine reaction.” Yet, several technologies are now transforming this model.

First of all, the interconnection of APIs and the ubiquity of geolocation have considerably extended the potential sources of data: the Uber application knows where you are leaving from, what appointments you have in your agenda (as of the beginning of January) and what your usual travel habits are at this time of day.

In addition, the rise of artificial intelligence and machine learning is increasing the effectiveness of these suggestions, by constantly checking if they actually work and by adjusting the aim if the user regularly refuses suggestions made.

What benefits for brands?

If the solution is well-implemented, the positive effects for brands will be exponential.

The first objective is to simplify the user’s choice, moving to an almost subliminal level, and to reduce what has been described as “decision fatigue”: we make 35 decisions every day, 000 of which are related to the sole question of what to eat.

By suggesting an immediate solution before the problem even arises, the brand has the advantage of no longer competing with other brands. This is Amazon’s goal in offering more and more automatic restocking options, from printer cartridges to grocery shopping or toilet paper: to rid the user of the need to think…and take advantage of it.

For a brand like Android, the goal is different: to move from being a “convenience” brand to a service and consulting brand. On a different note, many brands are facing the same challenge as they realize that their applications are rarely or never used (recent statistics show that applications lose an average of 90% of their active users within 30 days of installation). By anticipating needs, the brand becomes proactive, hoping to recreate usage value and to bond with the user.

Risks that shouldn’t be underestimated.

But successful proactive design is not easy to implement – far from it, in fact. Four risks come to mind:

Risk of intrusiveness: anticipatory design is based on the ability to collect data on the user in order to offer relevant choices. While many users now fully understand that their phones or applications collect information about them without their knowledge, it is risky to reveal too much knowledge of the user's habits, privacy and location. The danger lies in both generating a rejection by the customer and seriously damaging the brand experience.

Risk of irrelevance: this is a consequence of the previous risk. If too much knowledge is intrusive, anticipatory design must be relevant, and this cannot be achieved without data collection. If Uber recommended whimsical destinations every time you opened it, you’d probably have already uninstalled the app.

This is a particularly compelling example of the paradox of privacy and personalization, which reveals the schizophrenic nature of the modern consumer: on the one hand, we are opposed, as a matter of principle, to the collection of our personal data. On the other hand, we opt for brands who can fine tune their offers and remember our tastes, preferences, and history.

Risk of trust: Aaron Shapiro, father of the concept of anticipatory design, summed up its philosophy as follows: “the next breakthrough innovation in design and technology will be the creation of products and services that eliminate unnecessary choices in our lives by making them for us.”

But are we really ready to let Google, Amazon and Apple make decisions for us about which products and services we want to use?

The last, and certainly not the least of these risks: for many purposes, anticipatory design requires applications that run continuously, constantly consuming bandwidth and battery life. That is the greatest risk of predictive design: having a phone that dies at 16:30 in the afternoon. Is anyone really ready to take that risk?

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