Busy week (1min read):
- Google IO happened:
- Android O: Instant Apps ⚡️🎉 (finally!), Google Lens camera to describe and interact with objects, Assistant adds a Chatbot/iOS/Actions like Alexa, automatic photo sharing, picture-in-picture (⬆️ engagement, see Stats), autofill web & apps (like iOS keychain/autofill), app badge dots, better security, pulls a Swift and introduces Kotlin (potentially lowering crash rates), better Play Store analytics, more, Google Play adverts
- Android Pay available as an Assistant Action and adds easier Loyalty cards
- A Magical Camera thanks to AI
- Positional tracking on a phone (move in real life = move in VR) and a standalone VR headset, Chrome will do AR and have a VR version, Seurat lets mobile VR use high-quality movie 3D environments (probably a basic AI authoring tool), you'll be able to watch Youtube with a friend in VR
- Firebase Analytics renamed to Google Analytics for Firebase and integrated with Fabric (e.g. Intercom-like send a message when user does X, add easy sms authentication via Digits)
- Chatbase (Chatbot Analytics)
- Reportedly replacing Roboto with Product Sans for UI
- Adds Fundraising and Food Ordering (time to reignite the 2014 “when should you split your app” debate?);
- Goes ballistic against failing Snapchat by expanding live streaming with eSports (a market with +$500m revenue in 2016), upgraded chat, co-streaming, face filters, location stories, cross-app notifications. (How long until Facebook copes Snap's Magic Eraser?)
- Voice Assistants everywhere (gimmick?): Google Home on GE and LG appliances, Alexa on set-top boxes (and in case you missed it, Alexa now does calls)
AI and ML
Applications Of Machine Learning For Designers – Smashing Magazine
Fantastic 30min read that explains what Machine Learning is, when to use it, current and future applications, once more, summarized here for you:
Why Understanding ML Matters
- Design better products: understanding the possibilities and limitations of ML allows you to determine and explain when and why it brings value to a product.
- Understanding (i.e. Machine Learning) enables Action (i.e. Artificial Intelligence);
- An AI action (e.g. driving) relies on one or multiple learnings (e.g. what are traffic lights + how to control speed + when to increase/decrease speed, what are lanes + how to switch lane + when to switch lane, etc);
When to Use ML
- Circumvent Human limitations: a machine doesn’t mind sorting tomatoes 24/7 at very high speed
- When there is enough data containing patterns to learn from e.g. to find what’s common across your favorite songs you’d need: list of favorite songs and a method to extract common characteristics, to detect red lights you’d need: photos of red lights and a method to ignore everything in the photo but the red light (aside: you can also pollute data to block machine learning!);
Applications of ML
- Detect, Predict, Create:
- Detect: detect cancer, detect criminals, detect and avoid whales;
- Prediction: personalized recommendations for thousands (one, two, three), predict product sales, predicting actions based on past behavior (the user always sets copies of Object A to a certain relative position – i.e. below – and opacity – i.e. 54%).
- Creation: alternative slide layouts, photos from sketches, say anything with someone else’s voice, transform photos into paintings with the desired artistic style, create illustrations from scratch, turn selfies into Photos.
Rethinking Design Tools in the Age of Machine Learning
5 second summary (20 minute read):
- Designers will spend less time doing and more time deciding (just like digital cameras didn’t replace photographers)
- Design will be more accessible (think iMovie vs Final Cut Pro)
- Easier to explore dozens or hundreds of variations
- You still have to know what to change and which constrains to manipulate (again, the camera doesn’t make the photographer)
- Tools could adapt to individual designers (observe and propose actions)
- Historical decisions can be shared between designers (What Would MacGyver Do?)
- Tools can understand the work (e.g. Is there a problem with navigation?; Show me alternatives for adding to cart)
Good to understand how AI and ML will impact the world. The author seems to believe AI doesn’t threaten the Designer role, I believe that’s an underestimation. In time, AI will design, build, release and iterate on its own, with minimal Human support. That’s fine, as long as we don’t turn into its batteries.
“What you actually need to learn to become a great Product Designer."
Product Design isn’t about Design Production, it’s about Production as a whole.
"What makes a good Product Manager?"
- Understand the users well enough to explain them to stakeholders and developers;
- Listen to makers enough to explain practical realities in business terms to stakeholders;
- Understand business realities and help makers accept those constraints;
- Have a passion for users and the product and be contagiously excited about both;
- Keep the high-level vision and work with others to define the details and make it a practical reality.
Product Managers and the Iceberg of Ideas
Recommended read if you’re a Product Designer who fluctuates between roles, or if you’re a Product Manager looking for some validation on what your role should be (look somewhere else if you’re looking for detail on process/tools).
It all boils down to capturing and prioritizing ideas based on stakeholders expectations and customers, resource/biz constraints, then you might delegate or run the learning cycle yourself (e.g. analyze/diverge/converge/test).
📹 Before digital typesetting: Linotron 505 - 1969
The digital future pulled the rug under the future-proof Linotron 505.
The manufacturer, involved in the creation of fonts such as Helvetica, was eventually acquired and reacquired as this solution became obsolete, ending up living off of digital font royalties.
Unlock honest feedback with this one word
TL:DR; Ask for “advice” instead of “feedback”.
Metrics Versus Experience – The Year of the Looking Glass
- Gut instinct is no longer enough to create successful products
- Measurement and Analytics allowed us to perform better than Gut Instinct
- Metrics can be manipulated and don’t always correlate with a good experience
- But Metrics and Experience aren’t mutually exclusive
- Metrics (GOOD): a) Give you insight into behavior; b) Help distinguish good from bad; and c) Create tangible output that can be used to solve otherwise subjective opinions.
- Metrics (BAD): a) Only as good as the measuring plan; b) Tell you what not why; c) Easy to manipulate
- Metrics (UGLY): a) Do not provide qualitative insights – Do they trust me? Do they like it? What about competitors? What to change/add/fix? What will happen 1 month ahead?;
- Product-market fit: look at retention, new users are meaningless if they don’t stick
- Retain by analyzing the AAARRR funnel for churn
- Plan and measure only the right metrics
- Find the right metrics by starting with the customer outcome and work your way back to measurable data (e.g. Are recommendations valuable? IF they were, then they’d watch/share them more than other content)
- Nullius in verba (Take nobody's word for it): question goals and metrics, what could make them fail? Is there an alternative?
- Question yourself and use countermetrics: If I were wrong about this, how would that look like in metrics?
- Complement with qualitative research get the full picture
Facebook and Snapchat: metrics versus creation
- You cannot control customers but you can measure them quantitatively at scale
- Customers would choose a linear timeline, Facebook metrics prove personalization works better
- Creation-heavy Snapchat does not rely on metrics to decide where to go next (you can only use quant data to measure success)
A bit too binary for my liking and somewhat confusing post. Quantitative data can absolutely be used to determine where to go next, even if the next is gathering qualitative data to identify unsolved customer jobs, and then move into ideation.
Building something no one else can measure
Healthy skepticism on metrics:
- People anchor a lot on metrics for everything from people's incentives to how quickly to optimize the business.
- Metrics can be the organizational equivalent of Schrödinger’s cat: picking the metric itself can cause weird cultural distortion
- Campbell's Law: "The more any quantitative social indicator is used for social decision-making, the more subject it will be to corruption pressures and the more apt it will be to distort and corrupt the social processes it is intended to monitor."
- We cannot measure behavior directly, instead we use metrics as proxies for measuring underlying behavior:
- e.g. Clicks -> "Did I read this?”
- e.g. Time spent -> "Do I like this?”
- Metrics turn “this makes people feel warm and fuzzy” into “this improves metric X by 5% week-over-week” which everyone can agree and act upon.
- Metrics always create secondary behavior on some other metric (good science practice suggests creating counter metrics to ensure you can see the whole whack-a-mole system)
- e.g. “Quality Journalism”: measure by clicks, leads to click-bait content
- e.g. “Quality Ad”: measure time spent, leads to preventing people from skipping the ad
- Good Product Managers are aware of systems and how metrics correlate
- This is a general problem that goes beyond tech, related article on how metrics can be short-sighted, short-term: Metrics Incrementalism and Local Maxima
- Although people are aware of metrics shortfalls, there are no clear better options, so you settle for metrics.
- The problem is that long-term metrics are hard (but possible) to monetize:
- e.g. Google optimized for sending people away from Search, capitalizing on retention instead of time spend on Search.
- e.g. Zappos optimized for making the experience great for returning-customers, instead of increasing checkout value.
Security & Privacy
WannaCry Global Ransomware Attack of 12-19 May 2017 targeted Windows computers (mostly Win 7 contrary to easier to swallow Windows XP story) in Telecoms/Health/Commodities and hit China especially hard, someone found a kill switch, which was later targeted by botnets and removed from newer WannaCry variants, someone also found a way to decrypt infected Windows XP computers (provided they had not been turned off since infected).
It made $116.000 in revenue so far (they could’ve done better infecting Android, see below).
Security is boring:
- Google reveals its servers all contain custom security silicon
- BrickerBot malware will brick unsecure Internet of Things devices
- The Internet of Things will host devastating, unstoppable botnets (classic Cory Doctorow sensationalism)
- These Popular Headphones Spy on Users, Lawsuit Says
- Samsung’s Android Replacement Is a Hacker’s Dream
- Found: Quite possibly the most sophisticated Android espionage app ever
- Mobile phone security’s been busted for years, and now 2-factor auth is busted too and real-world exploit: Thieves drain 2fa-protected bank accounts by abusing SS7 routing protocol
- 235 apps attempt to secretly track users with ultrasonic audio
- More Android phones than ever are covertly listening for inaudible sounds in ads
- Android apps share data between them without your permission
- An Analysis of the Privacy and Security Risks of Android VPN Permission-enabled Apps
- Another Reason 99% of Mobile Malware Targets Androids
- Russian criminals silently steal $892,000 from infected Android phones via SMS banking
Newly-found API will make SMS authentication for apps a lot smarter in Android O | 9to5Google
There are three options for SMS tokens:1) Manual input;2) Read the token from a link that opens the app (i.e. App/Universal Link)3) Read the token by observing the customer’s SMSWhile the third option doesn’t force customers out of the app, it requires full SMS access. Android O offers an interesting balance between security and ease of use by removing the need for full SMS access, and instead notifying the app when the token is received.
- Picture-in-picture increased live video viewing by 86% in the MLB app (allow your customers to multitask!)
- Android has 2BN MAUs (iOS had 1BN in Jan 16, for scale, there are 700m PCs worldwide)
- Xbox Live reaches 55M MAUs (+15% YoY)
- 400M MAUs for Facebook Messenger audio/video (sorry Xbox)
- Snapchat growth is slow with only 12M since Q4 16, blames data caps, clashes with engineers, (space station turn to Instagram/Facebook) (sustaining growth is an even harder challenge)
💯 Picking a pricing strategy for your product
Great article on product pricing strategy, it builds upon Joel York’s “SaaS Startup Strategy” and adds practical examples:
- Applicable to any commercial venture (e.g. software, hardware, food, agencies, …);
- 2 Axis: Customer Revenue and Acquisition Cost (i.e. generate more revenue than you spend);
- 3 Strategies: Transactional, Enterprise and Self-service (i.e. Stripe – low-cost high-value customers, SalesForce – high-cost high-value, Basecamp – low-cost low-value);
- All strategies are valid as long as they’re a conscious decision (e.g. low-cost low-value will impact customer support for free users, be aware and compensate to sustain free-to-paid conversion)
- Products may have more than one pricing strategy (e.g. GitHub has both free and enterprise customers).
It almost feels like this should belong to the History section, Joel Spolski wrote about this 13 years ago in “Camels and Rubber Duckies”, he adds customer segmentation to capture consumer surplus (the extra money someone would pay for your product), and many other example-rich points (e.g. a list of bad ideas, enterprise pricing self-harm, irregular demand curves, and more).
All very “obvious” but when was the last time you actively thought about your product or agency sales in this way? I found it very comprehensive, no-bullshit and eye-opening.
Alexa learns to talk like a human with whispers, pauses & emotion | TechCrunch
And so does Google to an even greater extent. Valuable as it allows for a more natural-sounding voice, and personalizing the voice to your service.
Good quote for those who cannot stand “opinions” 😉.
Scan in 1 min, read it in 5 min 💨
Busy week(s) with Google I/O bringing us magic for future apps, Global Ransomware (highlighting the importance of security), Facebook trying to bury Snapchat (and why you don’t compete on features), tips on Product Design/Management and how how tools might evolve with AI (or how YOU might use AI to evolve your app for that matter).