Posts Tagged internet-of-things

Amazon Dash – What does the one-click mindset mean for your brand?

Amazon is reinventing the shopping experience again with the Amazon Dash.

If you haven’t heard, Amazon Dashes are tangible $5 buttons (for Prime members) that you can stick anywhere, and when pressed, the buttons will automatically order and ship a single specific product to you.

Stick a Tide detergent button next to your washing machine. Or a Charmin button next to your toilet. When you’re running low, you no longer need drive out to the grocery store or even take out your phone to make a purchase, but just press the button without a second thought.

This is Amazon’s innovative approach to leveraging the internet of things to simplify the shopping experience, to bring the digital one click ordering to the physical world, and to create mindless habits that keep customers loyal to them.

It has become apparent that Amazon Dash, along with the slew of “do-it-now” apps from Uber to Instacart, is completely changing consumers’ expectations for their companies.

No matter what industry you are in or what service you offer, consumers will more than ever expect an instant and frictionless experience, much like the one-click apps they use on their phones.

Ease of use and convenience is no longer a ‘good thing to have’ but a ‘must have’. And you must deliver beyond that to keep your customer – for they are less patient and more ready than ever to go to another website or another app that is faster and more intuitive.

So how well do you know your consumers’ journey and the pain points they are experiencing?

Do you know when they use your product differently than intended? Do you know all the pain points that your consumers’ experience when they interact with your brand?

Where can you remove friction? Can you reduce 2 steps into just 1? Is there a point where the consumer would have to exert themselves to make a decision? How can you make the interaction more ‘mind-less’? How can your brand deliver something delightful that your customer did not expect?

By delving into your consumers’ journey, you can innovate your product or brand to meet the expectations of their “once-click” mindset.

Target’s Smart Home

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Smart devices are everywhere you look. You can get anything from the well-known Nest thermostat and Sonos speakers to the Philips Smart Light Bulb, Mr. Coffee WeMo Enabled Smart Coffeemaker, and Parrot plant waterer.

And although we see and hear about these smart products everywhere, it’s easy to be overwhelmed and intimidated to actually use them. Demand for smart products actually dropped by 15% this year, according to August Insights, showing the market has still not yet infiltrated the mainstream.

In an effort to make smart devices more accessible to consumers, Target opened up a futuristic concept home by the San Francisco Metreon to showcase how all their different smart products can talk to each other to make a better morning, evening and well, life, for you.

I recently stumbled into the experimental space they call Open House, with its beautiful transparent acrylic walls and furniture that really highlight the devices.

Each room has a tablet which you can choose from a variety of hypothetical scenarios that will play out, showing you how the devices work together. In one “morning” scenario, the baby starts waking up earlier than usual. The smart baby monitor Mimo then triggers the parent’s phone in another room to vibrate to wake her and get the baby as well as signals the coffee maker in the kitchen to start a cup of joe.  How much nicer of a morning would my parents have had if these devices could talk to each other like that? I’m not a parent yet myself, but I have high hopes for how connected devices will surely help me with the joys and tribulations of future child rearing.

Target will take their learning’s from the way consumers interact with the products in the smart house as insight for future sale strategies.  Hopefully Target’s efforts will be one of many that move our generation away from the seemingly excessive connected-hyped world, to one that can truly leverage the potential of the internet of things to create more seamless life-enhancing experiences.

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Insight from the Internet of Things


Inference is a key insight skill – looking for patterns in raw primary data to seek new meanings and contexts for understanding. Wearables, nearables and the Internet of Things bring us new universes of fantastic primary data. Compelling user benefits can be obtained by applying layers of inference on to this primary data.

Pattern Recognition

Smappee is a great example of this approach. It is able to detect your household’s electricity usage with all its minor fluctuations as different appliances and devices are switched on and perform their duties. The smarts come with what it does with this primary usage data. It is able to recognize the distinctive pattern, or signature, that each appliance has. For example, a dishwasher’s electricity usage will vary as it goes through its washing, rinsing and drying cycles.

Identifying Outliers

Unless you’re an energy nerd, telling users how much electricity each appliance is consuming is not a compelling benefit. More valuable benefits come as you take pattern recognition to the next level, namely learning the “usual” and identifying outliers and anomalies. For example, has the iron been left on for longer than is usual? If so, alert the user to the potential issue and ideally give them a way to resolve the problem. In Smappee’s case it provides a range of smart plugs that can be controlled from the mobile app to enable individual outlets to be switched on and off.

Iterative Learning: User Feedback

Services like Smappee can continuously learn and improve via two important mechanisms; user feedback and social data aggregation. Any decent learning system needs to give users an opportunity to help it improve and enhance what it learns. For example, what if the alert about the iron still being on was unwanted as the typical household pattern of behavior is to let a mountain of ironing accumulate and then do it all in one lengthy session. If the user can easily flag that the alert was inappropriate the system should be able to adjust and learn more about what represents “usual” for the household it is monitoring.

Learning from the network

Leveraging network effects is also critical for such services to maximize their value. For example, let’s say users are given the opportunity to enter the make and models of some of the appliances identified by the service. The intelligence provided by one user can be used to help the whole service improve its appliance signature detection capabilities across its whole network of users.

Joining Data Sets

Services like Smappee don’t need to rely just on the data provided by its device sensors or from user feedback. Combining other data sets can provide additional tiers of benefits. For example, based on learning the appliances that a household uses and their corresponding energy ratings the service can make specific recommendations as to which ones the household might want to consider replacing to make the most savings on their electricity bill.

Such intelligence would clearly be enormously valuable to marketers. This is where things get more complicated from a privacy perspective, consumers are not going to want to be bombarded with offers for a new washing machine because they’ve shared the fact that they have an aging energy hog whirring away in the laundry. As always the challenge is to ensure that consumers remain in control and are always getting genuine value from the data they share.

Open Data

In many cases startups like Smappee cannot research and develop all the inference and data joining capabilities that the data they are acquiring opens up. The best strategy is almost always to open up their data and allow others to develop applications and services that use it. A complete ecosystem that relies on their primary data is enabled. Users of the service must of course remain in complete control of their data and have full opt-in and opt-out capabilities from any additional services.

From a research and insight perspective using new data sources like this and applying these layers of inference, represents a huge opportunity and we’re only just starting to explore what’s possible.