The Case For and Against "Peak App"

Peak app is the idea that the mobile app-driven smartphone economy has peaked and is now in relative decline.  Should you be concerned?

Whether it eventually proves to be true or not, examining the case for and against peak app does surface a number of relevant insights to consider as you chart your company's mobile apps' path forward.

The Case for Peak App
As background, I'm unsure where the term "peak app" originated; the first reference I've found online is from late 2014, apparently triggered by a Deloitte/UK study on the mobile consumer.  

Since then the concept of peak app has appeared sporadically in the tech media and sometimes elsewhere.  The concept picked up steam in 2016 when comScore published the 2016 U.S. Mobile App Report that included the following infographic:

As you might expect, much of the published thinking around peak app is why it's true.  And there are clearly some compelling arguments to be made in favor of the peak app theory:

• While smartphones' share of digital media time continues to slowly increase, it's concentrated in fewer apps, dominated by Facebook, Google and their associated properties, which own the top six (and eight of the top 10) most-used apps
• The mobile app ecosystem is saturated; most people don't download a single new app in any given month even though 40k+ new apps are added monthly to the app stores
• Consumers spend almost half of their phone time on a single app (usually their primary social network) and 90% of their time on the five apps that they use the most
• Consumer consciousness of potentially detrimental impact of obsessive smartphone use

As quoted in a Fast Company article, Brian Roemmele, an independent tech consultant who specializes in voice and commerce, says that "Peak app has already happened...  The new iPhone is going to do great, and it’s going to change a whole lot, but it’s not going to induce more and more app downloads. So that economy has shifted."

On another front, tech giants like Amazon, whose Fire Phone was a major flop, have a significant interest in pushing the peak app narrative as they've watched from the sidelines as Apple and Google revolutionized personal computing.  

Also quoted in Fast Company: "Mobile behavior is already set with consumers typically using about five apps a day on a mobile device, making it harder for brands to get users to discover and download new apps on their devices," says Ambika Nigam, Bloomberg Media’s global head of mobile app products. "However on Alexa, if you’re having a conversation and can organically weave your brand and content into the mix, it can potentially become easier for users to discover your skill."

In any case, these facts and opinions indicate that new consumer-facing mobile apps have a truly uphill battle in the stiff competition for consumer eyeballs and digital time share--and not just from other apps.

The Case Against Peak App

Given the peak app theory's arguments why would an enterprise or brand still make mobile initiatives a focus?  Here a few counter points from comScore's 2017 report:

• Mobile users spend 16x as much time with apps vs. the mobile web 
• Mobile apps consume over 50% of consumers' digital media time; desktop apps are a distant second at 34%--and the mobile web only sees seven percent
• App usage--and a willingness to pay for apps--is much more common for younger users than older users

In a blog post titled "Why the Concept of Peak App is Just an Urban Legend" David Bolton points to several reasons why peak app is just not so:

• Sensor Tower's latest report says that there were 13.2 billion installs in the second quarter—an annual growth rate of 12% or around 1.44 billion installs
• Uber surpassed Facebook in terms of installs for Q2 2017--installed 6.5 million times compared to the six million downloads for Facebook’s core app
• The Apple App Store had a year-on-year revenue growth of 52%, with Google Play showed an annual growth rate of 72%

Peter Dolukhanov earlier this year examined peak app theory and adds:

• Mobile minutes have already increased 11% [in the UK] since March of this year [2017] with apps dominating that time
• On a per-user basis, average app time up 14% in the last 6 months, and with users now spending on average almost 63 hours a month on mobile apps, versus 14 on mobile web

In summary, the case against peak app is that overall mobile app usage continues to grow in just about every measurable way and that consumers will increasingly pay for great apps (and not just games).

Magenic's Perspective

In Magenic's Mobile Practice experience we see two additional reasons to remain bullish on mobile apps.  

First, mobile apps are the "connective tissue" that tie together digital experiences of all types from IoT to digital reality to artificial intelligence and machine learning.  (Check out our webinar on emerging technologies, digital transformation and mobile.)

Second, we believe that emerging technology is enabling completely new experiences for which mobile apps will be the window to the world. 

In short, no other technology platform is so well positioned in terms of market penetration, context, immediacy and technical capability to enable brands to communicate effectively with their customers.

Conclusion

So, should peak app concern you?  Maybe.  Whether or not peak app theory proves true, mobile product owners clearly must consider that while increasing audiences and share of digital time suggest there is still room for app growth, there are significant challenges to overcome to succeed in the consumer-facing app market.

Mobile Apps Mature--Now What?

Now that over 10 years have passed since the first iPhone was introduced--and more than nine since the first Android smartphone (remember the HTC Dream/T-Mobile G1?)--most of us have created multiple mobile apps for our customers, employees and partners.

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.