And over time we've learned and gotten much smarter about how to approach mobile initiatives, increase our odds of success and mature mobile app portfolio management.
Today, it feels like mobile app development is a known process, we have our arms around it, and we know the recipe for success. In many ways this is true; for example, the gap between iOS and Android has never been smaller. Almost all of the mobile market is owned by two mature operating systems with little chance any viable third competitor will emerge on the near horizon. Mobile is quickly becoming the dominant channel for eCommerce. There are many effective cross-platform approaches to mobile development that save time, money and deliver engaging experiences.
However, the mobile story from the beginning has been rapidly evolving technology, innovation and disruption, and continuous transformation of customer experiences and expectations. This remains the case today!
As you think about how your mobile apps need to evolve in 2018 there are three themes you should focus on:
- Artificial Intelligence (AI)
- New Experiences
- Data-Driven Interactions; e.g., through Machine Learning (ML)
First, there's good news and bad news here. The bad news is that incorporating AI/ML and creating new experiences is hard--there's a lot of data science and advanced, cutting edge technologies involved. The great news is that much of the technology to drive these new capabilities is readily accessible as services. In other words, you don't need to build it in your mobile app so much as figure out how to leverage it.
Let's look at some real-world examples.
Artificial Intelligence
An amazing AI mobile app that always brings a smile to my face is Seeing AI, an app that interprets the physical world for the sight-impaired. I first learned about this app in early 2017 while it was still a proof of concept and highlighted it in a series of events about how emerging technologies are changing mobile.
The Seeing AI app leverages Microsoft's Cognitive Services, primarily the Vision and Speech API sets, and can even run some AI features when offline using CoreML (for Android you'd try TensorFlow). (Note that Amazon, Alphabet's DeepMind and IBM's Watson have similar services).
The key takeaway here is that AI is a service you call (from your code), not something you build from scratch in your mobile app. Incorporating AI into your mobile app has never be easier--the hardest part is figuring out what to do, not how to do it.
New Experiences
One of the things that mobile early changed about how we build apps is that no one creates user manuals or--in most cases--even basic tutorials explaining how to use an app. Either you get it or you don't. And if you don't, you uninstall. For mobile apps, there can be no "you aren't using it right" scenario.
Two ascendant mobile experiences--chatbots and custom speech interactions (i.e., not generic Siri/Cortana/Assistant)--should be on your 2018 roadmap for experimentation. There are an increasing number of very capable chatbot apps. It's likely such an interaction model would both work for your business and be welcomed by your customers.
Led by Alexa, the AI-driven smart speaker market is red hot. Brands are increasingly extending their reach via channels like Alexa Skills--even companies like Spotify, which were originally mobile only.
Back to the first thought regarding new experiences, can you get a chatbot or speech interaction right? In some well publicized events (Tay, Alexa) even the big guys had embarrassing failures.
While natural language interactions through chatbot or speech continue to improve, the analysts say that capabilities are now on par with human interaction. And the key challenges you'd expect to encounter in a business environment, such as understanding intents, other languages and business domain-specific language, are being rapidly addressed through, for example, API services such as Translator Speech and Custom Speech.
Exciting new technology is ready--you just need to figure out how to leverage it.
Data-Driven Interactions through Machine Learning
In short, "data-driven interactions though machine learning" means that we can create mobile experiences that are backed by predictive ML technology, learning and reacting in real time.
Who's using ML in mobile? Here's a few examples:
- Netflix uses linear regression, logistic regression, and other machine learning algorithms to perfect its personalized recommendations by means of ML
- Tinder's "Smart Photos" feature shows a random order of your profile photos to people and analyzes how often they’re swiped right or left. This knowledge allows Tinder to reorder your photos by putting most popular ones first. This system is honing itself constantly
- Google Maps' "Find Parking" feature uses anonymous aggregated information from users who decided to share their location data as input to a standard logistic regression model. Then the app--based on the dispersion of parking locations--predicts when, where, and how difficult finding an empty spot will be
Or, if you want to experience something different, try "The JFK Files" a website that uses a variety of AI/ML technologies to explore recently classified JFK files released by the government.
Again, the technologies required to make practical use of machine learning in your mobile apps exist and are ready. One way to explore getting started is Microsoft's Azure Machine Learning Studio, which will expose your algorithms via web services your mobile app can consume. Another is to explore Amazon's Machine Learning capabilities for Android or Apple's CoreML.
Time to experiment!
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At Magenic Technologies our Mobile Practice experts guide clients' mobile initiatives from ideation to strategy to execution to lifecycle support and governance.
Our emerging technologies experience in the mobile space makes us an ideal partner for companies who desire to take the next step in mobile engagement that will differentiate their users' brand experience and deliver objective value.
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