Posted by the Firebase team
The COVID-19 pandemic of 2020 brought changes and challenges for many businesses. During this time, we saw developers use resilience and ingenuity to adapt their apps and business models to these new circumstances. For GameNexa Studios, an app developer and consultancy based in India, one of the biggest challenges they faced this year was to figure out how to evolve their monetization strategy in the face of declining ad revenue. The GameNexa team needed a data-driven approach to diversify their revenue stream across their portfolio so they turned to Firebase.
With 40 apps and games under their belt serving 5 million monthly users, GameNexa Studios had a well-established monetization strategy, but like many of their peers, it was disrupted by the COVID-19 pandemic. Previously, the company earned most of its income from ads in their free-to-download titles. However, when many of their advertisers slashed their budgets, GameNexa’s ad revenue dropped too.
To offset their losses, GameNexa needed to pivot from a one-size-fits-all strategy to a diversified revenue model. But diversifying revenue doesn’t mean bombarding users with more offers and in-app promotions - that could drive people away. The most effective monetization strategies are tailored to user preferences and behavior. So, GameNexa first used Google Analytics and Firebase Predictions to better understand their users and then grouped them into segments based on common characteristics like language, and predicted future behavior, like their propensity to make an in-app purchase.
After gaining insight into their users, GameNexa used Firebase Remote Config and Firebase A/B Testing to test new ad placement, formats, and different in-app promotions on each segment to find which offer resonated with each group. They also worked on improving their user experience with Firebase Crashlytics and Firebase Performance Monitoring.
As a result of these efforts, GameNexa saw a 2.5x increase in revenue from in-app purchases and they were able to bring their ad revenue back up to pre-COVID levels by doubling ad impressions. In addition, by creating customized in-app purchase packs for different audiences, GameNexa increased conversions by 6x. Inspired by their own success, GameNexa now plans on sharing what they’ve learned about the power of data-driven monetization and personalization with other developers through their app consultancy. Read their full story and get more details on how they used Firebase to grow and diversify their revenue in our new case study.
One of the biggest challenges game developers face is figuring out how to improve monetization without compromising their game experience. Many game developers embed ads into their titles, which enables them to offer their games for free and remove the cost barrier of adoption for players - while still generating revenue. In-app advertising can be lucrative, when done effectively and in moderation.
But how do you know what types of ads are best-suited for your game? How do you ensure ads won’t drive away players? These are the exact questions Pomelo Games had. For answers, they turned to Firebase.
Pomelo Games is one of the top game studios in Uruguay. They pride themselves on developing unique and polished games that capture players’ imaginations. Their recent release, Once Upon a Tower, “is an easy-to-pick-up, hard-to-put-down, free-to-play game,” says co-founder Jonás Mora. A Play Store Editors’ Choice, the game is beloved for its high-fidelity graphics, as well as the “fairness of its free-to-play mechanics,” says Jonás.
So when the team needed to improve the game’s monetization, they were unsure how to proceed. They were looking for a way to increase revenue without sacrificing the affordability and game quality their players loved.
Pomelo Games used Firebase Remote Config and Firebase A/B Testing to test a new ad format: interstitials. They also used Google Analytics to monitor revenue and Firebase Crashlytics to keep an eye on stability.
Although initially opposed to the idea, Jonás and his team discovered that showing interstitial ads to their entire player base led to an average 25% increase in AdMob revenue, and surprisingly, a 35% increase in in-app purchases as well. In both cases, there was almost no negative impact on retention or game stability. Firebase gave Pomelo Games the confidence to try new approaches to grow revenue without driving players away. Read Pomelo Games’ full story and get details on their success with Firebase in our new case study. And learn more about how Firebase can help you build and grow your game, and see what other game studios are using Firebase.
Last year at Firebase Summit, we introduced you to Predictions, a machine learning product that helps you smartly segment your users based on their predicted future behavior. Without requiring anyone on your app team to have ML expertise, Predictions gives you insight into which segments of users are likely to churn or spend (or complete another conversion event) so you can make informed product decisions and grow your app.
As of today, Predictions makes more than 6 billion predictions per day for our developers and allows them to take meaningful actions by making predictive segments available for targeting in Remote Config, Cloud Messaging, In-App Messaging, and A/B Testing.
This year at Firebase Summit, we announced that Predictions has graduated out of beta and into general availability with a host of new features that we added based on your feedback.
Since Predictions continuously update based on actual user behavior inside your app, we heard from many of you that you wanted to know how stable a prediction was before you integrate it into your app.
To help answer this question, we created a health indicator at the bottom of each predictive segment card that gives you a snapshot of how a certain prediction is performing:
Image 1: Green means it has been performing consistently well over the last two weeks
Image 2: Yellow means it is performing well today but did not meet the quality threshold some time in the past two weeks
Image 3: Red means it is not performing well today and had other performance issues over the last two weeks
It is worth mentioning that actions targeted with Predictions have a fail-safe mechanism, so if a predictive segment is performing poorly, it simply turns inactive. That means, if you are using Remote Config to deliver a set of values to users in that predicted group, Remote Config will gracefully fall back to your default values if the predictive segment decreases in reliability. Any notifications or in-app messages directed at that predictive segment will also not trigger until the predictive segment increases in accuracy.
To help you understand how we assess the quality of a prediction, we are now exposing our evaluation criteria. For every predictive segment, we use a portion of your historical data from the last 28 days that we hold out during the model training phase.
We then compare the results of the prediction to what actually happened. This gives us two ways to score the prediction: how many of the users in the predictive segment actually behaved in the predicted way (we call that true positive rate) and how many users in the predictive segment were incorrectly classified (or in more technical terms, the false positive rate).
You can access this data from the bottom of the prediction card
Tapping on the health indicator exposes these values.
By exposing these two scores to you, you can now make a better determination about which risk profile to choose for your action.
Another common question we received during our beta phase is what went into creating a predictive segment. We now offer a details page that gives you the ingredient list! You can click through and see what data our model makes use of. This includes event frequency, volume, and parameters as well as other data like device language, freshness of app install and more.
The last thing we are excited to announce is that now, you can export your raw predictions data into BigQuery. This will give you access to the raw prediction score, the thresholds we used for each risk profile, as well as the final result. You can use this data to create your own risk profiles or if you supply your own user_id property in analytics, to do sophisticated analysis with your analytics data. For example, you can find out which countries exhibit the highest potential to churn or spend!
We are humbled to have gained your trust over the past year and hope these improvements make it easier for you to make the most out of Predictions in your mobile apps and games. As always, if you have any questions, you can find us on Twitter (@firebase) and on Stack Overflow.
For more information on these updates, check out our docs below!
Predictions risk tolerance and performance
Predictions model inputs and details page
Predictions data export to BigQuery
Here at Firebase, we believe that apps improve the way people live, work, learn, and socialize. That's why our mission is to make app development as easy as possible by giving you a platform that solves key challenges across the app lifecycle. Whether you're an up-and-coming startup or a well-established enterprise, Firebase can help you build your app, improve its quality, and grow your business.
It's exciting to evolve Firebase alongside our passionate community. Right now, over 1.5 million apps are actively using Firebase every month. We love hearing your stories; they inspire us to keep making Firebase even better so you can continue to succeed. One story we heard recently is from Hotstar, India's largest entertainment app with over 150 million monthly active users around the world.
A few months ago, the Hotstar team safely rolled out new features to their video watch screen during a major live-streaming sports event. These changes, along with updates to their onboarding flow, increased user engagement by 38%! Impressively, by using a combination of Firebase products, Hotstar was able to do this without disrupting users, sacrificing stability or releasing a new build.
Learn more about their story here:
Today, we're hosting the third annual Firebase Summit in Prague to meet many more members of our developer community and learn about the great things they're building. All sessions will be posted to our YouTube channel or you can read on to learn about all the exciting new updates we're announcing today!
We've been working hard to make it easier for sophisticated app development teams to use Firebase. Today, we're excited to share that we'll be adding support for Firebase to our Google Cloud Platform (GCP) support packages, available in beta by the end of this year.
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.
In addition to Cloud support, we've made improvements to Firebase across the board. More below!
Manage projects with ease via Firebase Management API
We've worked hard to open access to our server-side APIs, so that you can easily integrate Firebase services with your existing systems. Today we're releasing the Firebase Management API, a REST API that allows you to create and manage projects and apps programmatically. Now, you can create and destroy Firebase environments as part of your existing developer workflow.
The Management API is also enabling partners to build awesome new experiences. We're thrilled to share that you can now deploy to Firebase Hosting directly from within StackBlitz and Glitch, two web-based IDEs. Their platforms will now automatically detect when you are creating a Firebase app and allow you to deploy to Firebase Hosting with the click of a button, without ever leaving their platforms.
What's especially cool is that this isn't just for partners. This is a new, extensible API and we're very excited to see what you build with it. You can learn more and get started here.
Enhanced face detection with ML Kit
Launched at Google I/O in May, ML Kit makes machine learning easy and accessible for all app developers, regardless of your experience with ML. If you're new to the space you can use ML Kit's out-of-the-box APIs, like text recognition or face detection, or if you're more experienced you can bring your own custom TensorFlow Lite models and serve them through Firebase.
Today, we're expanding on the face detection API with the beta launch of face contours, allowing you to detect 100+ detailed points in and around a user's face. The face contours functionality empowers apps to easily overlay masks or accessories on facial features with high fidelity and accurate positioning, or add beautification elements, like skin smoothing or coloration. See our docs to learn more!
Increased confidence in deployment with Cloud Firestore
In the past, we've heard feedback that testing can be tricky on Firebase. For example, it's sometimes hard to set the right rules to ensure your apps are secure. To help with this problem, we're releasing local emulators for Cloud Firestore and the Realtime Database. These emulators let you develop and test locally, and can be built into your continuous integration workflow so you can deploy with more confidence and peace of mind. Learn more about the emulator here.
Propagate Remote Config updates in near real time + integration with Cloud Functions
Developers love using Remote Config because it gives them the ability to modify their app, customize the UI, or release a new feature without deploying a new version that could disrupt users. But, there was no easy way of knowing when an app's Remote Config was updated! You had to fetch updates from Remote Config every few hours to ensure your users always saw the latest changes in their app.
Today, we're happy to announce that Remote Config now integrates with Cloud Functions & Firebase Cloud Messaging so you can notify your apps in near real time when you publish (or rollback) a new config. This reduces the set-up complexity of Remote Config and uses less bandwidth on devices because apps only need to fetch when a new config is available.
Additionally, Remote Config can now trigger developer-defined functions when you publish or rollback your config. This way, you can keep different Remote Config projects in sync (for development/staging/production environment workflows), as well as send Slack messages to your team when a new config is published. To learn more, visit our docs!
From our early access partner eBay:
"The combination of the Cloud Functions with the Firebase Remote Config REST API has allowed my distributed team at eBay to be instantly notified of any changes to our application's configuration. Using these tools to create a function which pushes changes to Slack ensures everybody who needs to know about a configuration change has that information immediately."
- Jake Hall, eBay Classifieds Group Mobile Architect
Test Lab for iOS graduates into general availability
At Google I/O, we also launched the beta availability of Firebase Test Lab for iOS. Over the last several months, we've expanded the iOS device farm, added support for iOS 12 as well as for older iOS versions, and integrated the UI for iOS into the Firebase console. With these updates, we are graduating Test Lab for iOS out of beta and into general availability. Learn more and get started with Test Lab today!
Performance Monitoring: Sessions insights and issues management
Even if you run thorough tests throughout your development lifecycle, bugs and performance issues will pop up in your production app from time to time. Performance Monitoring gives you insight into these issues and automatically surfaces the most critical issues in a given trace instance (i.e. a particular app start or checkout flow). Now, you can dive into an individual trace session to see exactly what was going on when a performance issue occurred.
For example, in the following dashboard, you could see that CPU usage spiked after the app fetches and renders a product image, which tells you the specific part of code to investigate.
With all the data and issues that Performance Monitoring surfaces, it can be hard to prioritize your efforts. That's why we're also launching the ability to "mute", "close", and "reopen" issues in your console. Muting temporarily silences the issue, so you can concentrate on other work, until you're ready to tackle it. Marking an issue as closed indicates that it's been solved, but Firebase will notify you if it occurs again.
Learn more about session insights and issues management here.
Crashlytics is now integrated with PagerDuty
App performance and stability issues can occur anytime. To help you stay on top of stability, even when you're away from your desk, we're introducing a Firebase Crashlytics stability digest email and a new integration with PagerDuty. The stability digest highlights emerging issues that could become problematic in the future, while the PagerDuty integration allows you to alert your team about a high impact crash, any time of day. To connect Crashlytics with PagerDuty, follow the steps here.
Do more with your data with BigQuery + Data Studio
Earlier this year, we integrated Crashlytics with BigQuery so you can run deeper analysis on your crash data. To help you get started in BigQuery, we've put together a Data Studio template, so you can quickly produce a shareable report. You can preview the template with mock data, and then customize the report to suit your needs. Learn more here.
Predictions graduates out of beta into general availability
Last year at the Firebase Summit, we introduced you to Firebase Predictions. Predictions applies Google's machine learning to your app analytics data to create user segments based on predicted behavior. Without requiring anyone on your app team to have ML expertise, Predictions give you insight into which segments of users are likely to churn or spend (or complete another conversion event) so you can make informed product decisions. This year, we're excited to announce that Predictions is graduating out of beta and into general availability with a host of new features designed to make Predictions more useful.
Wondering what goes go into any given prediction? We added a new details page that shows you what factors the ML model considered (like events, device, user data, etc.) to make that prediction. We also now expose performance metrics for each prediction, letting you see how the prediction has performed historically against actual user behavior, so you can better calibrate your risk tolerance level. And, if you want to do a deeper analysis of prediction data or use it in third party services, you can export your complete prediction dataset to BigQuery.
Check out our docs to learn more!
Reach users more effectively with dynamic audiences in Google Analytics
Google Analytics for Firebase has always given you the ability to segment your users into audiences based on events, device type, and other dimensions. Now, we're enhancing the audience builder with a few major updates: dynamic audience evaluation, audience exclusion, and membership duration.
First, audiences are now dynamic by default, meaning that Firebase will pull in new users that meet your criteria and automatically remove users that no longer meet your criteria. If, for example, you set up an audience of users who are on level 5 in your game, as users beat the level and move on to level 6, they will automatically be removed from the audience. Conversely, once users advance to level 5 they will automatically be added to the level 5 audience you've established.
Secondly, you can refine your audience by adding exclusion criteria using and/or statements which enable you to create audiences like "users that have added to their shopping cart but not made a purchase".
Finally, audiences can now include a membership duration, allowing you to ensure that your audiences stay fresh. This enables you to target users who have completed an action within a specific time period, e.g. "made a purchase within the past two weeks".
Dynamic audiences allow you to reach your users more effectively with relevant messaging and a more personalized app experience. Learn more and get started with dynamic audiences here.
Run automatically recurring campaigns with Cloud Messaging
Once you've defined your user segments using Analytics or Predictions, you can use Firebase Cloud Messaging (FCM) to send notifications to latent users to bring them back into your app. We've redesigned the notifications console to support more sophisticated campaigns. This new UI gives you the power to set up recurring notification campaigns that automatically send messages to new users as they meet the targeting criteria. Previously, you could only schedule one-time sends.
Additionally, the new notifications UI allows you to easily target users based on the date of their first session or the number of days since they last opened an app. And last but not least, we've updated the campaign results view so you can track the effectiveness of recurring notification campaigns day-by-day.
Check out the new UI on your console!
We're excited about all the updates to Firebase that we've announced today. As we continue to grow and enhance the platform, we'd love to have your feedback. Join our Alpha program to get a sneak peek of what we're building next, share your thoughts with us, and shape the future of Firebase.
If you weren't able to join us in person in Prague, all of our sessions are recorded and posted to our YouTube channel. Thanks for being a part of our community and happy building!
We're delighted to announce the beta launch of Firebase Predictions. With this we are bringing the power of Google's machine learning systems to every developer that uses Firebase. Predictions is a product that can build dynamic user groups based on predicted behavior, determined using a machine learned model, and these user groups can then be targeted using Firebase Cloud Messaging, Remote Config and other technologies. User groups are updated daily to keep your predictions fresh.
Out of the box, there are four predicted groups:
You'll see these three groups right away when you select Predictions in the left nav bar of Firebase Console.
You'll notice that each of these cards has actions that you can take upon them.
Tolerance: The tolerance slider gives you the ability to tolerate low, medium or high risk of false positives. So, with a low tolerance, your population of users will be smaller, but so also will your risk of false positives. Similarly, with a high tolerance, you'll have a larger population of users, but at a risk of some of them being false positives. In the case of 'churn', a false positive would be a user who is predicted to churn, but in fact continues to use your app.
Target Users: This gives you a drop-down on which you can select Remote Config or Notifications for that user group. It also links to some handy guidance for offering in-app incentives.
Selecting Remote Config will take you to a new screen where you can specify the remote config parameter that you want to set up, and then the value for it for that population. So, for example if you've been building a game, and a lot of people have churned and you see from feedback that it's too difficult to play, you could set a remote config variable for the difficulty, so that likely churners could get a lower default value set for them, and thus would have an easier experience playing the game.
Selecting Notifications will take you to the familiar composer for messages to be sent using Firebase Cloud Messaging, but in addition to the usual options for picking target audience, you'll also get the predicted user group pre-populated as a user segment.
This allows you to target notifications at that user group. So, for example, for users at a risk of churning, you could send a notification with an enticement to continue using the app.
Creating your own predictions. You aren't limited to the built-in predictions cards, of course, and can create your own based on custom events that you set up in your app. In this case, you'll see a card that allows you to create a prediction.
And when you select it, you can then create a prediction for when your event will, or will not happen. This helps you identify users who are likely to engage in that conversion event:
So, for example, in the above case, whenever a user levels up in the game, the level_up conversion event is logged. Thus, you could create a prediction for players who may level up, and incentivize them to continue playing.
Then, once you've saved your prediction, over time a card will populate on the Firebase Console in the same way as the built-in ones.
And this card can be used in the same way as the others -- including targeting users with notifications and Remote Config.
Firebase Predictions is a Beta product, and we're continuing to work on it and improve it. If you have any questions or feedback, please reach out -- and for bugs and product suggestions, you can reach us at firebase.google.com/support.
Learn more about Firebase Predictions at firebase.google.com/products/predictions/ or dive straight into our docs right here.
Our mission for Firebase is to help you build better apps and grow your business, by providing tools that solve common problems throughout your app development lifecycle. We manage your backend infrastructure, provide you with the tools to improve the quality and stability of your app, and help you acquire and engage users, so you can focus on building a fantastic user experience.
To date, over one million developers have used Firebase to build their apps across iOS, Android and the web. It's both inspiring and humbling to hear the many stories that all of you share with us. Take Doodle, for instance, a company that helps you find the best date and time to meet with people. Doodle recently used Firebase to redesign their app and increase retention and engagement.
We're excited to be hosting the second annual Firebase Dev Summit here in Amsterdam, where we get to meet many members of our developer community! We've been working hard to improve Firebase, so that our products work seamlessly together, and we have several exciting new updates to share today. We've integrated Crashlytics into Firebase, enabled first-class A/B support and taken our first step in bringing the power of Google's machine learning into Firebase with a new product called Predictions. We've also made a few other improvements, so let's dive in!
Since Fabric joined Google, we've been working to bring the best of our platforms together. Today we're announcing a big step in that journey: we're adding Crashlytics to the Firebase Console for new and existing Firebase users. Crashlytics is the best-in-class crash reporter that helps you track, prioritize, and fix stability issues that erode your app quality, in realtime. We'll be rolling out this update over the next several weeks, but if you're eager to try it out sooner, you can visit g.co/firebase/opt-in and get access today.
We're also integrating Crashlytics with other parts of Firebase. You can now use Crashlytics events to trigger Cloud Functions and power custom workflow integrations. For example, you can automate a workflow to route issues in a critical app flow - like your purchase path - to a particular developer or Slack room, ensuring the proper escalations, reducing the time to resolution, and increasing stability.
In addition to bringing Crashlytics to Firebase, collaborating with the Fabric team has allowed us to make some exciting updates to the Firebase console that will help you find key information about your app more easily and efficiently.
First of all, you're going to notice a new structure in the left-hand navigation bar. We've clustered Firebase products into four main areas, based on the app development lifecycle: Develop, Stability, Analytics, and Grow. All of the products that you're used to seeing in the Firebase console are still there; we've simply reorganized things to more accurately reflect the way your team works.
We've also redesigned the first screen you see when you open a Firebase project — what we call your Project Overview screen. We've heard from you that the majority of the time, when you come to the console, you're looking for four main statistics: daily active users, monthly active users, crash-free user rate, and total crashes. We've taken those four key metrics and made them front-and-center for any apps in the project. We've also added sparklines, so you can understand how your app is trending over time.
Finally, we've overhauled the Analytics section of the console. You'll find a new dashboard that is organized around the questions and tasks that you tackle on a day-to-day basis. We've also added a Latest Release section that gives you all the information you need about the stability and adoption of your latest app release, so you can make quick decisions after a launch. Lastly, we've added realtime cards to both of these sections, so you can have up-to-the-second insight into your app data. Like Crashlytics, these changes are rolling out over the next few weeks, but you can get access today by visiting g.co/firebase/opt-in.
Firebase Cloud Messaging (FCM) gives you an easy way to send notifications to your users, either programmatically or through the Firebase Console. However, sending cross-platform notifications with more complex functionality has been difficult, sometimes requiring you to create multiple, separate messages.
Today, we're announcing a new RESTful, FCM HTTP v1 API that makes it safer and easier to send messages to your cross-platform applications. The new FCM API allows you to use platform-specific fields in a single notification. For example, you might send a simple text notification to iOS, but a request with a click_action to Android, all in one API call. To read more about the new FCM API, visit our documentation.
click_action
In addition to FCM, another powerful tool for driving user engagement and retention is Remote Config. Up until now, running variant tests with either Remote Config or FCM has been manual and quite some work. We've heard from many of you that you want an easier way to test how different app variants or push notification messages impact your key business metrics.
Today, we're launching the beta version of A/B testing, a new Firebase feature that's integrated with Analytics, FCM and Remote Config. It's built on the statistical engine and years of learning from Google Optimize, our free website testing and personalization product, and makes it easy to design experiments right from the Firebase console.
Setting up an A/B test is quick and simple. You can create an experiment with Remote Config or FCM, define different variant values and population sizes to test on, then set the experiment goal. From there, Firebase will take care of the rest, automatically running the experiment then letting you know when a winner towards your goal is determined with statistical significance. Learn more and get started with A/B testing here.
Whether you're driving engagement, revenue, or a different business metric, determining the right targeting can be difficult. Being proactive, instead of reactive, is always better, but up until now, there's been no easy way to anticipate what actions your users are likely to take. To help with this, we're taking our first step in bringing the power of Google's machine learning to Firebase with a new product called Firebase Predictions.
We've already started using machine learning in other parts of Google, to enhance consumer products like Photos, Inbox, or the Assistant. Now, you can harness Google's machine learning, using Firebase, to help you build great products. Predictions automatically creates dynamic user groups based on predicted behavior from your Analytics data and, out of the box, it will generate four user groups:
You can use these predictions for targeting with Remote Config and notifications composer, giving you the ability to only show ads to users who are predicted to not spend money in your app or send a notification to users who are predicted to churn in the next 7 days.
You can also create predictions for any Analytics conversion event in your app. For example, if completing level 3 is an important milestone in your app, you can create a prediction for users who are likely to not hit that milestone and then send them an in-app promotion using Remote Config.
We're already hearing from partners that Predictions helps them drive growth in their key business metrics. Halfbrick, a games developer known for popular titles such as Fruit Ninja and Dan the Man, used Predictions and Remote Config and boosted their 7-day retention rate by 20%! To learn more about Predictions, as well as read the full Halfbrick story, visit our product page here.
While we're excited about the updates to Firebase that we've announced today, we also know that there's a lot more work to be done. We are working hard to prepare for the General Data Protection Regulation (GDPR) across Firebase and we're committed to helping you succeed under it. Offering a data processing agreement where appropriate is one important step we're taking to make sure that Firebase works for you, no matter how large your business or where your users are. We'll also be publishing tools and documentation to help developers ensure they are compliant. You can check out our privacy FAQs at g.co/firebase/gdpr.
As we continue to grow and improve the platform, we'd love to have your input. Join our Alpha program to help shape the future of the platform and stay on the cutting edge of Firebase.
If you weren't able to join us in person in Amsterdam, all of our sessions are recorded and posted to our YouTube channel. Thanks for being a part of our community and happy building!
