This article originally appeared on the Google Marketing Platform blog.
For businesses to make the best decisions about where to invest their marketing budget, it's critical that they understand user behavior on both their web and app properties. And while a website is often the first customer touchpoint, for many businesses, apps are where customers are spending more of their time. As a result, marketers need to capture audience insights from their app analytics that they can then take action on, both within and outside of their apps.
Google Analytics for Firebase, our app analytics solution, has historically given you the ability to organize your audiences around events, device type, and other dimensions. These criteria were not exhaustive, however, or dynamic as user behavior changed over time.
That's why we've made enhancements to the audience builder experience, with a few major updates to help you identify relevant app audiences more easily and with greater precision:
These new tools make audiences more powerful, flexible and actionable than before, so you can be confident that your insights reflect relevant users and activity on your apps. In 2019, we will continue to enhance the Google Analytics for Firebase audience builder, offering even more ways to precisely create audiences.
Take action once you've identified relevant audiences
Once you've improved your understanding of users, you can also deliver personalized experiences based on varying user needs. For example, through push notifications or Remote Config in Firebase, or customized ads in Google Ads.
Let's say you have an e-commerce app. Using these advanced audience capabilities, you can build an audience of users that visit your app for the first time and add an item to their cart, but don't make a purchase—and only include those who do so in a 30 day window.
Build a dynamic audience for first time users that have abandoned their cart.
You can now reach that audience with tailored messaging relevant to their experience with the app, and encourage them to make the purchase through an in-app promotion, email notification, or personalized ad. Once these users have returned to the app, made a purchase, and/or exceeded the 30 day window however, they will no longer meet the criteria for that audience, and you will not adversely affect their experience with marketing that is no longer relevant to them.
With the ability to create dynamic audiences, you are able to understand your users with better precision. A better view into your audiences means more insight into the customer journey, so you can invest in your marketing activities with confidence and see better results—keeping users happy, and your app growing.
Hey there, Firebase developers! Did you hear the news? Cloud Firestore — our NoSQL database in the cloud for mobile and web apps — is officially out of beta and in General Availability!
Great! So… why's that a big deal?
The move from beta to GA is significant for a number of reasons. For starters, it's Google's way of saying that we're confident in Cloud Firestore's ability to handle your toughest database needs. And you can rely on Cloud Firestore to power all kinds of apps — from experimental prototypes to mission critical enterprise-scale applications.
Moving into GA also has some practical benefits. One of the most important is that Cloud Firestore is now included in GCP's official Service Level Agreements (or SLAs, as they're commonly known). This means you now have guaranteed uptime — 99.999% for multi-region instances of Cloud Firestore, and 99.99% for regional instances.
In addition, now that Cloud Firestore is out of beta, important programs like GCP's deprecation policy officially apply to Cloud Firestore, and Cloud Firestore is now included among our list of services that support HIPAA compliance.
New lower pricing tier — coming soon!
We're also excited to announce a new lower pricing tier (up to 50% off) for most regional instances of Cloud Firestore. And if you're wondering what exactly I mean by "regional" and "multi-region", let's explain:
To over-simplify things a bit, when you have a Cloud Firestore database installed in a multi-region location, exact copies of your database are created in multiple data centers at least a few hundred miles apart from each other. This is great from a reliability standpoint; even if one region were to completely go offline due to a natural disaster or giant radioactive lizard attack, your database would continue to be hosted from the other two.
Even better, this whole setup is completely transparent to you. When you write new values to the database, you're able to read them right away, and not have to worry about whether or not they've fanned out to all of the other regions properly. This is known as being strongly consistent, and honestly, it's a pretty amazing piece of tech that will impress your database aficionado friends.
That said, creating a strongly consistent multi-region database is pretty heavy-duty tech that requires a lot of infrastructure. And while all of that is important for mission critical applications, it might be overkill for some of your other apps, and you might be perfectly happy with the guaranteed 99.99% uptime that comes from having Cloud Firestore hosted in a single region. (Usually just referred to as "regional" locations.)
So to help reflect these differences, we're going to start lowering prices on most of our regional instances of Cloud Firestore in the next month or two. The price will vary by location, but the discount will be as much as 50%. Once this new pricing goes into effect on March 3rd, 2019, it will be applied automatically to those of you who have Cloud Firestore databases hosted in those regional instances.
New locations, too!
As long as we're talking about data center locations, we've added several new locations around the world to host your Cloud Firestore data. There will be a new multi-region location in Europe, and then 9 additional regional locations, including a few in Asia, Australia, North and South America, and Europe. Be sure to check out the Google Cloud blog post for the complete list.
We believe these new locations will give you the tools to help address your local regulations around data storage, and better serve the needs of your customers who might be concentrated in certain areas around the globe. You can select your Cloud Firestore location when you first create your Firebase project, so make sure to pick the location that will provide the best experience for your customers.
Better usage tracking, powered by Stackdriver
One of the biggest concerns we've heard from developers when they're using a tool like Cloud Firestore is that they're not very confident in their app's database usage, and they're worried about the day when a bill arrives in the mail for more than they expected because they had underestimated their app's database usage.
So to help address this, we're going to be adding a new "Usage" tab in the Firebase console to show you exactly how many reads, writes, and deletes your database has received over time. These are the operations that drive the majority of your Cloud Firestore pricing, so making sure you know what your traffic is like in these three areas is critical to keeping a handle on your costs.
These graphs are certainly helpful, but what's really exciting about these usage reports is that they're being powered by Stackdriver, Google Cloud's incredibly flexible monitoring and reporting toolkit. And one really nice feature about Stackdriver is that you can set up your own custom alerts if any of the metrics its measuring go outside certain ranges.
So if you want to receive an email (or PagerDuty notification, or a Slack message) when your database receives more reads than you were expecting in a one-hour period, you can do that. You can also receive alerts if your traffic spikes an unusual amount compared to normal, or if your overall traffic drops below a certain level.
Please note that this usage tab is currently in beta, and while there's a lot more you already can do with Stackdriver (including building your own custom dashboards), we'll be looking to add more Cloud Firestore metrics to Stackdriver in the future. Look for this new Usage tab to appear in the Firebase console in the next few days!
Give it a try today!
Even in beta, Cloud Firestore was driving some fantastic app experiences — with partners like The New York Times, Skip Scooters, QuintoAndar, Nerdery, and more building some really great features powered by Cloud Firestore.
And now that we're in General Availability, this a great time to get started using Cloud Firestore to power your apps. We've got a lot of documentation and samples get started with, and a fun little video series that I am legally obligated to plug. So give it a look, and happy databasing!
What's the deal with audiences?
So if you've never used audiences before, they can be a really powerful way to segment your user base so that you can deliver tailored and relevant experiences to specific groups of users. Maybe that's sending them a customized notification in Firebase Cloud Messaging, maybe that's creating a unique experience for them in Remote Config, or maybe that's targeting them with a remarketing campaign in Google Ads.
We at the Firebase office all enjoyed playing with Hanley Weng's "CoreML-in-ARKit" project. It displays 3D labels on top of images it detects in the scene. While the on-device detection provides a fast response, we wanted to build a solution that gave you the speed of the on-device model with the accuracy you can get from a cloud-based solution. Well, that's exactly what we built with our MLKit-ARKit project. Read on to find out more about how we did it!
This image takes a while to load, but it’s worth it.
ML Kit for Firebase is a mobile SDK that enables developers to bring Google's machine learning (ML) expertise to their Android and iOS apps. It includes easy-to-use on-device and cloud-based Base APIs and also offers the ability to bring your own custom TFLite models.
ARKit is Apple's framework that combines device motion tracking, camera scene capture, advanced scene processing, and display conveniences to simplify the task of building an AR experience. You can use these technologies to create many kinds of AR experiences using either the back camera or front camera of an iOS device.
In this project we are pushing ARKit frames from the back camera into a queue. ML Kit processes these to find out the objects in that frame.
When the user taps the screen, ML Kit returns the detected label with the highest confidence. We then create a 3D bubble text and add it into the user's scene.
ML Kit makes ML easy for all mobile developers, whether you have experience in ML or are new to the space. For those with more advanced use cases, ML Kit allows you to bring your own TFLite models, but for more common use cases, you can implement one of the easy-to-use Base APIs. These APIs cover use cases such as text recognition, image labeling, face detection and more. We'll be using image labeling in our example.
Base APIs are available in two flavors: On-device and cloud-based. The on-device APIs are free to use and run locally, while the cloud-based ones provide higher accuracy and more precise responses. Cloud-based Vision APIs are free for the first 1000/API calls and paid after that. They provide the power of full-sized models from Google's Cloud Vision APIs.
We are using the ML Kit on-device image labeling API to get a live feed of results while keeping our frame rate steady at 60fps. When the user taps the screen we fire up an async call to the Cloud image labeling API with the current image. When we get a response from this higher accuracy model, we update the 3D label on the fly. So while we are continuously running the on-device API and using its result as the initial source of information, the higher accuracy Cloud API is called on-demand and its results replaces on-device label eventually.
Which result to show?
While the on-device API is real-time with all the processing happening locally, the Cloud Vision API makes a network request to the Google Cloud backend, leveraging a larger, higher accuracy model. Once the response arrives, we replace the label provided by the on-device API with the result from Cloud Vision API.
1. Clone the project
$ git clone https://github.com/FirebaseExtended/MLKit-ARKit.git
2. Install the pods and open the .xcworkspace file to see the project in Xcode.
$ cd MLKit-ARKit
$ pod install --repo-update
$ open MLKit-ARKit.xcworkspace
GoogleService-Info.plist
Info.plist
At this point, the app should work using the on-device recognition.
★ The cloud label detection feature is still free for first 1000 uses per month. Click here to see additional pricing details.
At this point, the app should update labels with more precise results from the Cloud Vision API.
Firebase launched over six and a half years ago as a database, but since then we've grown into a platform of eighteen (18!!) products. And over the last year we've announced a number of new features to help you build better apps and grow your business. We also infused Firebase with more machine learning super-power, so you can make your apps smarter, and matured the platform, so Firebase works better for developers at large, sophisticated enterprises.
Since the end of the year is a great time for top-ten lists, we were going to cap off the year with our own "Top Ten List of Firebase launches." But, then, we realized we had more than ten launches we wanted to talk about, and we really don't like playing favorites. So instead, here's our "Thirteen Firebase Launches In No Particular Order Because They're All Great In Their Own Way" list for 2018. Enjoy!
At Google I/O, we launched one of our most exciting features of 2018: ML Kit for Firebase, a machine learning SDK for Android and iOS. ML Kit lets you add the power of machine learning to your app, without needing an advanced degree in neutral networks. It provides a number of out-of-the-box solutions for performing tasks like recognizing text in images, labeling objects in photos, or detecting faces. And it will also let you use custom models, for those of you who are into building your own. (Bespoke artisanal neural networks are big among hipster data scientists these days.)
Notifications are a great way to get latent users back into your app, but how do you communicate with active users who are actively using your app? In 2018, we launched Firebase In-App Messaging to help you send targeted and contextual message to users who are actively using your app. In-app messages are a great way to encourage app exploration and discovery, and guide users towards discovering new features in your product, or working their way towards that important conversion event.
At Firebase, we're big fans of building scripts to make our lives easier; whether that's to automate common tasks, or to perform custom logic. To help with that goal, we launched three new REST APIs that you can use to automate your life (at least from a Firebase perspective). The Firebase Management API is great for automating tasks like creating new projects, the Remote Config REST API can be useful for customizing the way you update Remote Config values, and the Firebase Hosting API can be used to automatically upload certain files to your site.
Recently, StackBlitz and Glitch used the Management API to build integrations that allow you to deploy projects directly to Firebase Hosting. Start a project, write some code, click a few buttons, and voila! You've deployed your Firebase project to the web!
Good performance is one of the key factors for creating a great user experience. Firebase Performance Monitoring automatically collects performance metrics where it matters the most: the real world.
This year, Performance Monitoring graduated from beta into general availability. Along the way, we added helpful new features like an issue feed in the dashboard to highlight important performance problems your users are encountering. We've also added session view support for network class and traces, which lets you dig deeper into an individual session of a trace, so you see attributes and events that happened leading up to a performance issue.
We also released Firebase Predictions into GA. Predictions uses machine learning to intelligently segment users based on their predicted future behavior. Along the way, we added health indicators and evaluation criteria to every prediction, so you can better understand how reliable a prediction is, as well as the data being used to make it. We also integrated Predictions with BigQuery, so you have more control over your data.
Getting started with Predictions is as easy as flipping a switch in the console. We predict you're going to love it! (Sorry.)
The general availability party keeps on going! Cloud Functions hit GA and we also released a new version of the SDK. The new SDK adds "callable" functions that make it much easier to call server functions from the client, especially if your function requires authentication.
Cloud Functions also released a brand new library, firebase-functions-test, to simplify unit testing functions. This library takes care of the necessary setup and teardown, allowing easy mocking of test data. So in addition to simple standalone tests, you can now write tests that interact with a development Firebase project and observe the success of actions like database writes.
firebase-functions-test
Firebase Test Lab went cross-platform in 2018 by adding support for iOS. Now you can write and run tests on real iOS devices running in our data centers. Test Lab supports ten models of iPhones and iPads running seven different versions of iOS, including iOS 12.
Test Lab also launched a number of improvements to Robo, a tool which runs fully automated tests on Android devices. Testing games is now easier, thanks to 'monkey actions' (which can randomly click on your screen), and game loops (which perform pre-scripted actions). You can also customize Robo better now, in case you need to sign-in at the start of your app or add intelligent text to a search field.
Continuing the theme of testing, in 2018, we launched emulators for Firestore and the Realtime Database, so you can more easily unit test your security rules and incorporate them into a continuous integration environment. These emulators run locally and allow you to test your security rules offline so you can be confident before deploying to production. We also created a testing library that simplifies your test code.
From the beginning, Cloud Functions has tightly integrated important usage metrics with Stackdriver, Google Cloud's powerful monitoring service. To deepen our integration further, we linked the Realtime Database with Stackdriver. You can now see even more metrics than the Firebase console provides, such as load broken down by operation type and information about your downloaded bytes.
The real power of this integration is to set up alerts on metrics or errors so you can detect and respond to issues before your customers notice them.
Sometimes the reporting dashboards in the Firebase console don't give you the level of granularity or specific data slice that you need. That's where BigQuery - Google Cloud's data warehouse - and Data Studio - Google Cloud's data visualization tool - come into play.
We've given you the ability to export your Analytics data to BigQuery for a while now. This year, we added integrations with Predictions and Crashlytics, so you can export even more of your Firebase data into one central warehouse. Learn more about using Firebase and BigQuery together here.
Cloud Firestore is our next generation database with many of the features you've come to love from the Realtime Database, combined with the scale and sophistication of the Google Cloud Platform. Over the course of 2018, we've launched a number of improvements to Firestore, to make it better suited for complex enterprises.
We also added some nice features along the way -- we expanded offline support for the web SDK from one browser tab to multiple. We've added better support for searching documents by the contents of their arrays. And we added multiple new locations where you can store your Firestore data: Frankfurt, Germany and South Carolina, USA. (We'll be adding even more locations in 2019.)
The Firebase console is a crucial part of the Firebase workflow for just about any team. We spent a lot of time in 2018 making the console better than ever. Here's a few things we added:
These features make you more productive and confident in your app's security and performance. We can't wait to add more to the console in 2019!
For a while now, we've been hearing from some of you that you'd like an option to get enterprise-grade support for Firebase. To address that request, we added support for Firebase to our Google Cloud Platform (GCP) support packages, available in beta right now.
If you already have a paid GCP support package, our beta will let you get your Firebase questions answered through the GCP support channel - at no additional charge. When this new support graduates to general availability, it will include target response times, technical account management (for enterprise tier), and more. You can learn more about GCP support here.
If you're planning to stick with Firebase's free support, don't worry - we don't plan to change anything about our existing support model. Please continue to reach out to our friendly support team for help as needed!
It's been a great year, so we're going to take a little time with friends and family before we hit the ground running in January. However you celebrate the end of your year, we hope your December is full of happiness and relaxation. And if it happens to be full of building mobile or web apps, we hope you use Firebase! Happy building!
