Five tech trends brands should pay attention to in 2022
These are five tech trends brands should pay attention to in 2022. The list was created by observing consumer habits, paying close attention to technology adoption and understanding cultural signals. In order to make the list meaningful and useful, I challenged myself by asking the following questions:
Does this unlock any value for consumers?
Does this unlock any value for the business?
Are these areas where brands are playing today? If so is there room to really dial things up a notch?
Drumroll please, here’s the list:
Ambient Computing
Deeply integrated mobile commerce
Machine Learning
Computer Vision
XaaS - Everything as a service
Ambient Computing
As the name implies, Ambient Computing is computing that is all around us in our everyday lives. It describes an ecosystem of smart devices which are always on, and are extensions of ourselves in the digital world. In 2020, there were high expectations for Ambient Computing. However, COVID-19 pumped the brakes on the hype surrounding Ambient Computing, and instead, we were all forced to accept a new normal in every aspect of our lives.
In its simplest form, Ambient Computing refers to a unique set of technologies that, when used collectively allows us to have computers perform tasks for us in real life, in the background. The most notable of these unique technologies is The Internet of Things IoT, Machine Learning, Natural Language Processing (NLP), and Conditional rules.
A practical example of Ambient Computing is connecting your lights to your Alexa and thereby having your home automation plugged into a voice assistant. This allows you to not only have voice control but to set rules where Alexa can proactively turn the lights on and off at predetermined times. Additionally, Alexa can remind you to purchase items, follow up with someone or learn a new skill altogether
The looming challenge for not only Ambient Computing but AI, in general, is understanding human sentiment. This is called Sentiment Analysis and helps the computer to understand subjective human attributes such as mood and intent.
I believe there’s a huge opportunity for brands to take more advantage of Ambient Computing by looking for ways to integrate their core brand experience with voice assistants, smart appliances, smart biometric devices, and sensors. This is where first mover advantage for a brand is critical in 2022 as new variants of COVID-19 are being discovered and consumers will rely on their devices more than human interaction.
Deeply integrated mobile commerce
For years, I’ve been a huge fan of how seamlessly Starbucks integrated its payment system into the Starbucks app. It’s hands down one of my favorite experiences where digital meets the physical world. Brands such as Nike, Adidas, and Uniqlo all accept Apple Pay on their website which enables a deeply integrated and seamless checkout experience on the mobile web.
For Android users, Google offers Google Pay which is a similar solution. Brands such as Panera, Shell, and Target accept Google Pay. The interesting play Google made last year with revamping Google Pay, was to make it a holistic money management solution in addition to enabling quick, easy and secure checkout. While it may seem like table stakes in 2021 for every mobile commerce site to include Apple Pay and Google Pay integration, the reality is that adoption isn’t quite where you’d expect it to be. According to business.com, Apple Pay has 43.9 million users while Google Pay has 25 million. To put this in perspective, in 2020 there were 256 million people in the U.S. who purchased online. **source Statistica
For brands who haven’t yet integrated a mobile wallet solution, the time is now. Furthermore, there are many lessons to be learned from the existing integrations in terms of what’s working and what can be improved upon. I think this presents a huge opportunity for brands like Macy’s and Bloomingdales who have credit cards issued by Citibank but don’t quite have an integrated mobile payment experience. Consumers expect a seamless mobile e-commerce checkout experience that integrates with their digital wallet and 2022 is the year to do this.
Machine Learning
These days the term AI is thrown around so much that we oftentimes don’t really understand what specific application of Artificial Intelligence is being referenced. Quite often, one of the main technologies which get referred to as AI is actually Machine Learning. Machine Learning is a method of data analysis that automates the process of creating an analytical model. Machine learning emerged from pattern recognition and the theory that computers can learn without being programmed to perform specific tasks. Therefore, we began collecting massive amounts of data on everything from consumer trends, spending habits, eating habits, medical conditions, viewing and listening preferences; you name it, there’s intelligence being garnered from it.
In the past few years, we’ve seen impressive applications of Machine Learning which we’ve come to rely on in our daily lives. Brands like Uber, Lyft, Netflix, and Spotify are just a few which come to mind. Their use of Machine Learning has enabled them to create new business models which didn’t exist before. You don’t have to be one of these mega tech brands to be using Machine Learning in your business. I would encourage brands who are curious but haven’t yet invested in the technology to start with a creative partner who can create a prototype based on what’s possible with Machine Learning.
Once there’s some interest and traction in your Machine Learning approach, take a deeper dive and become familiar with the underlying technology. Here’s a handful of references to get you started.
Free Machine Learning Services on AWS
Tensor Flow, an end to end machine learning platform
Pytorch, a free open-source machine learning framework.
Machine learning has the potential to enhance the customer experience in so many ways. One of the most obvious means is by automatically personalizing a customer’s experience based on purchase history and inferred intent. This shows up in many different ways for the consumer at many different touch-points. Imagine if brands were able to send recommendations to consumers not only via an email blast but in real-time to their phones based on their proximity to the store as well as proximity to certain items within the physical space. For example, if I’m in Banana Republic it would be amazing to get real-time recommendations on my phone within their app, as I move throughout the store. This type of supplemental screen experience is actually already something consumers are doing today by looking up items online if they don’t see them in the actual store. Machine Learning makes this possible
Computer Vision
In the 10-13 years, there have been more advances in computer vision than at any other point in human history. This has been made possible by the advent of cameras on mobile phones, more powerful CPUs, dedicated GPUs, and the combination of Computer Vision and Machine Learning.
Computer vision allows computers to recognize objects in the real world based on training the computer to know what similar images look like. Similar to Machine Learning, Computer Vision is a distinct but related field within AI. This has made it possible for us to search using images on Google, unlock your phone with your face, detect a person on your Google Nest Doorbell camera and create autonomous driving vehicles.
The latest version of Apple Maps has a real-world identification feature whereby you can scan a building and it will direct you by overlaying your camera with directions. This is a reality because of Apple’s application of Computer Vision.
For brands within the beauty and cosmetics space, Computer Vision can be utilized to create virtual makeup applications and similar types of applications where the camera needs to recognize a human face and overlay it with different SKUs from your catalog. The same technology is used to create interactive kiosks whereby a user walks up and tries on an outfit. Imagine the other untapped potential use cases for Computer Vision outside of retail. By combining Computer Vision with Machine Learning and Predictive Analytics, there are opportunities where the technology could be used to detect certain types of dermatological issues, health issues and even monitor stress levels over time.
If you’re uncertain of how to take the next step with Computer Vision, start with what your brand already has and look for ways to enhance the consumer experience with vision. Because it’s relatively easy to implement; in the past 5 years, we’ve seen many brands experiment with voice recognition. Voice recognition is a natural complementary experience to Computer Vision. Imagine not only being able to control the lights and music with your voice but also being able to authenticate using your face on your Google Home Hub which is much more secure than just using your voice.
XaaS - Everything as a Service
During the past two decades, we’ve witnessed explosive growth in cloud computing. This has allowed organizations to move away from on-premises solutions and instead to have cloud-based offerings. The cloud allows businesses to move more quickly, iterate faster, and scale at unprecedented rates. Some of the first movers in this area were Salesforce, VMWare, Amazon, Google, and others.
An explanation of XaaS - source BMC
As a result of all these new cloud capabilities, it didn’t take long for vertical cloud offerings to start appearing, this includes (Software as a Service, Platform as a Service, Infrastructure as a Service). Today, by leveraging different components of XaaS (Everything as a Service), organizations can significantly improve their customer experience while simultaneously lowering operating costs. In a SaaS model, the goal is to convert one-time consumers into subscribers to receive continuous benefits of having direct access to the products and services in the cloud. XaaS makes it possible for brands to combine products and services into a single offering. This is referred to as Servitization. Companies such as Google and Adobe have been successful at adopting this model by offering their products as cloud-based services. Others such as Rolls Royce have also realized significant gains by switching to a XaaS model. Of all the trends mentioned within this article, this particular tech trend has the potential to affect the most significant change to a company’s bottom line. Moving to a XaaS based operating model takes time, planning, and extreme coordination.
