Improve your writing by grounding Gemini in Google Docs in sources

What’s changing

We’re excited to announce source-grounded writing help in Google Docs. Gemini will automatically curate a list of sources linked in your document and you can choose to ask it to only pull details from those sources when providing writing assistance via the side panel. This will keep suggestions focused and grounded in trusted content. With source-grounded writing help you can:

  • Create contextual and reliable output from Gemini 
  • Query available knowledge on a topic, getting accurate answers that are tailored to your needs

Using this trusted and relevant content can help ensure you’re using exactly the context you need — no guesswork or tab switching required. 

Additionally, this launch can help save users time. All users have to do is link files in their document, which many users already do today, and then select "Document links" to ground on those files. 

Previously, users would have to remember and individually add files in the prompt field to ground Gemini in them. Adding files individually is still available for users that want to ground on files that are not linked in the document. However, this launch will make it quicker and easier for users that have already linked the relevant files in the doc to ground Gemini on that information, and can help ensure important sources are not left out.


Ground Gemini by selecting linked sources in the side panel
Ground Gemini by selecting linked sources in the side panel

Who’s impacted

End users

Getting started

  • Admins: There is no admin control for this feature.
  • End users: This feature will be available by default and is available in the side panel of Docs. Visit the Help Center to learn more about how to collaborate with Gemini in Google Docs. 

The dialog in the side panel where users can choose to ground Gemini in linked files


The dialog in the side panel where users can choose to ground Gemini in linked files

Rollout pace

Availability

Available for Google Workspace:

  • Business Standard, and Plus
  • Enterprise Standard, and Plus
  • Gemini Business, Enterprise
  • Google AI Pro for Education
  • Google One AI Premium 

Resources

ML Kit’s Prompt API: Unlock Custom On-Device Gemini Nano Experiences

Posted by Caren Chang, Developer Relations Engineer, Chengji Yan, Software Engineer, and Penny Li, Software Engineer

AI is making it easier to create personalized app experiences that transform content into the right format for users. We previously enabled developers to integrate with Gemini Nano through ML Kit GenAI APIs tailored for specific use cases like summarization and image description.


Today marks a major milestone for Android's on-device generative AI. We're announcing the Alpha release of the ML Kit GenAI Prompt API. This API allows you to send natural language and multimodal requests to Gemini Nano, addressing the demand for more control and flexibility when building with generative models.


Partners like Kakao are already building with Prompt API, creating unique experiences with real-world impact. You can experiment with Prompt API's powerful features today with minimal code.



Move beyond pre-built to custom on-device GenAI Prompt API moves beyond pre-built functionality to support custom, app-specific GenAI use cases, allowing you to create unique features with complex data transformation. Prompt API uses Gemini Nano on-device to process data locally, enabling offline capability and improved user privacy.


Key use cases for Prompt API:

Prompt API allows for highly customized GenAI use cases. Here are some recommended examples: 

  • Image understanding: Analyzing photos for classification (e.g., creating a draft social media post or identifying tags such as "pets," "food," or "travel").

  • Intelligent document scanning: Using a traditional ML model to extract text from a receipt, and then categorizing each item with Prompt API.

  • Transforming data for the UI: Analyzing long-form content to create a short, engaging notification title.

  • Content prompting: Suggesting topics for new journal entries based on a user’s preference for themes.

  • Content analysis: Classifying customer reviews into a positive, neutral, or negative category.

  • Information extraction: Extracting important details about an upcoming event from an email thread.


Implementation

Prompt API lets you create custom prompts and set optional generation parameters with just a few lines of code:


Generation.getClient().generateContent(
   generateContentRequest(
       ImagePart(bitmapImage),
       TextPart("Categorize this image as one of the following: car, motorcycle, bike, scooter, other. Return only the category as the response."),
   ) {
       // Optional parameters
       temperature = 0.2f
       topK = 10
       candidateCount = 1
       maxOutputTokens = 10
   },
)

For more detailed examples of implementing Prompt API, check out the official documentation and sample on Github.


Gemini Nano, performance, and prototyping


Prompt API currently performs best on the Pixel 10 device series, which runs the latest version of Gemini Nano (nano-v3). This version of Gemini Nano is built on the same architecture as Gemma 3n, the model we first shared with the open model community at I/O.


The shared foundation between Gemma 3n and nano-v3 enables developers to more easily prototype features. For those without a Pixel 10 device, you can start experimenting with prompts today by prototyping with Gemma 3n locally or accessing it online through Google AI Studio.


For the full list of devices that support GenAI APIs, refer to our device support documentation.


Learn more

Start implementing Prompt API in your Android apps today with guidance from our official documentation and the sample on Github.


Beyond Request-Response: Architecting Real-time Bidirectional Streaming Multi-agent System

The blog post argues the request-response model fails for advanced multi-agent AI. It advocates for a real-time bidirectional streaming architecture, implemented by the Agent Development Kit (ADK). This streaming model enables true concurrency, natural interruptibility, and unified multimodal processing. ADK's core features are real-time I/O management, stateful sessions for agent handoffs, and streaming-native tools.

Beyond Request-Response: Architecting Real-time Bidirectional Streaming Multi-agent System

The blog post argues the request-response model fails for advanced multi-agent AI. It advocates for a real-time bidirectional streaming architecture, implemented by the Agent Development Kit (ADK). This streaming model enables true concurrency, natural interruptibility, and unified multimodal processing. ADK's core features are real-time I/O management, stateful sessions for agent handoffs, and streaming-native tools.

Kakao Mobility uses Gemini Nano on-device to reduce costs and boost call conversion by 45%

Posted by Sa-ryong Kang and Caren Chang, Developer Relations Engineers


Kakao Mobility is South Korea's leading mobility business, offering a range of transportation and delivery services, including taxi-hailing, navigation, bike and scooter-sharing, parking, and parcel delivery, through its Kakao T app. The team at Kakao Mobility utilized Gemini Nano via ML Kit’s GenAI Prompt API to offer parking assistance for its bike-sharing service and an improved address entry experience for its navigation and delivery services.


The Kakao T app serves over 30 million total users, and its bike-sharing service is one of its most popular services. But unfortunately, many users were improperly parking the bikes or scooters when not in use. This behavior led to an influx of parking violations and safety concerns, resulting in public complaints, fines, and towing. These issues began to negatively affect public perception of both Kakao Mobility and its bike-sharing services.


“By leveraging the ML Kit’s GenAI Prompt API and Gemini Nano, we were able to quickly implement features that improve social value without compromising user experience. Kakao Mobility will continue to actively adopt on-device AI to provide safer and more convenient mobility services.” — Wisuk Ryu, Head of Client Development Div


To address these concerns, the team initially designed an image recognition model to notify users if their bike or scooter was parked correctly according to local laws and safety standards. Running this model through the cloud would have incurred significant server costs. In addition, the users’ uploaded photos contained information about their parking location, so the team wanted to avoid any privacy or security concerns. The team needed to find a more reliable and cost-effective solution.


The team also wanted to improve the entity extraction experience for the parcel delivery service within the Kakao T app. Previously, users were able to easily order parcel delivery on a chat interface, but drivers needed to enter the address into an order form manually to initiate the delivery order—a process which was cumbersome and prone to human error. The team sought to streamline this process, making order forms faster and less frustrating for delivery personnel.


Enhancing the user experience with ML Kit’s GenAI Prompt API


The team tested and compared cloud-based Gemini models against Gemini Nano, accessed via ML Kit’s GenAI Prompt API. “After reviewing privacy, cost, accuracy, and response speed, ML Kit’s GenAI Prompt API was clearly the optimal choice,” said Jinwoo Park, Android application developer at Kakao Mobility. 


To address the issue of improperly parked bikes or scooters, the team used Gemini Nano's multimodal capability via the ML Kit GenAI API SDK to detect when a bike or scooter violates local regulations by parking on yellow tactile paving. With a carefully crafted prompt, they were able to evaluate more than 200 labeled images of parking photos while continually refining the inputs. This evaluation, measured through well-known metrics like accuracy, precision, recall, and the F1 score, ensured the feature met production-level quality and reliability standards.


Now users can take a photo of their parked bike or scooter, and the app will inform them if it is parked properly, or provide guidance if it is not. The entire process happens in seconds on the device, protecting the user’s location and information. 



To create a streamlined entity extraction feature, the team again used ML Kit's GenAI Prompt API to process users' delivery orders written in natural language. If they had employed traditional machine learning, it would have required a large learning dataset and special expertise in machine learning. Instead, they could simply start with a prompt like, "Extract the recipient's name, address, and phone number from the message." The team prepared around 200 high-quality evaluation examples, and evaluated their prompt through many rounds of iteration to get the best result. The most effective method employed was a technique called few-shot prompting, and the results were carefully analyzed to ensure the output contained minimal hallucinations.


“ML Kit’s Prompt API reduces developer overhead while offering strong security and reliability on-device. It enables rapid prototyping, lowers infrastructure dependency, and incurs no additional cost. There is no reason not to recommend it.” — Jinwoo Park, Android application developer at Kakao Mobility


Delivering big results with ML Kit’s GenAI Prompt API


As a result, the entity extraction feature correctly identifies the necessary details of each order, even when multiple names and addresses are entered. To maximize the feature's reach and provide a robust fallback, the team also implemented a cloud-based path using Gemini Flash.


Implementing ML Kit’s GenAI Prompt API has yielded a significant amount of cost savings for the Kakao Mobility team by shifting to on-device AI. While the bike parking analysis feature has not yet launched, the address entry improvement has already delivered excellent results: 

  • Order completion time for delivery orders has been reduced by 24%. 

  • The conversion rate has increased by 45% for new users and 6% for existing users. 

  • During peak seasons, AI-powered orders increase by over 200%. 


“Small business owners in particular have shared very positive feedback, saying the feature has made their work much more efficient and significantly reduced stress,” Wisuk added.


After the image recognition feature for bike and scooter parking launches, the Kakao Mobility team is eager to improve it further. Urban parking environments can be challenging, and the team is exploring ways to filter out unnecessary regions from images. 


“ML Kit’s GenAI Prompt API offers high-quality features without additional overhead,” said Jinwoo. “This reduced developer effort, shortened overall development time, and allowed us to focus on prompt tuning for higher-quality results.”


Try ML Kit’s GenAI Prompt API for yourself


Build and deploy on-device AI in your app with ML Kit’s GenAI Prompt API to harness the capabilities of Gemini Nano.

redBus uses Gemini Flash via Firebase AI Logic to boost the length of customer reviews by 57%

Posted by Thomas Ezan, Developer Relations Engineer



As the world's largest online bus ticketing platform, redBus serves millions of travelers across India, Southeast Asia, and Latin America. The service is predominantly mobile-first, with over 90% of all bookings occurring through its app. However, this presents a significant challenge in gathering helpful feedback from a user base that speaks dozens of different languages. Typing reviews is inconvenient for many users, and a review written in Tamil, for instance, offers little value to a bus operator who only speaks Hindi.

To improve the quality and volume of user feedback, developers at redBus used Gemini Flash, a Google AI model providing low latency, to instantly transcribe and translate user voice recordings. To connect this powerful AI to their app without dealing with complex backend work, they used Firebase AI Logic. This new feature removed language barriers and simplified the review process, leading to a significant increase in user engagement and feedback quality.


Simplifying user feedback with a voice-first approach


The previous in-app review experience on redBus was text-based, which presented some key challenges. At our scale, reliable user reviews are critical: they build trust for travelers and give operators actionable insights. While our existing text-based system served us well, we found that customers often struggled to articulate their full experience, which resulted in our user feedback lacking the necessary detail and volume we needed to deliver maximum value to both travelers and operators. What's more, language barriers limited the usefulness of reviews, as reviews in one language were not helpful for users or bus operators who spoke another. "Our primary motivation was to leverage the expressive power of voice and overcome the language barrier to capture more authentic and detailed user feedback,” said Abhi Muktheeswarar, a senior tech lead in mobile engineering at redBus.

The developer team wanted to create a frictionless, voice-first experience, so they designed a new flow where users could simply speak their review in their native language. To encourage adoption, the team implemented a prominent, animated mic button paired with a text mentioning: “Your voice matters, share your review in your own language.” This mention appears in the user’s native language, consistent with their app language settings.


Using Gemini Flash, the application processes the user’s voice recording. It first transcribes the speech into text, then translates it into English, and finally analyzes the sentiment to automatically generate a star rating and predict relevant tags based on the review content. It then creates a concise summary and autofills the review form fields with the generated content.

Developers chose Firebase AI Logic because it allowed them to build and ship the feature without the help from the backend team, dramatically reducing development time and complexity. “The Firebase AI SDK was a key differentiator because it was the only solution that empowered our frontend team to build and ship the feature independently,” Abhi explained. This approach enabled the team to go from concept to launch in just 30 days.

During implementation, the engineers used structured output, enabling the Gemini Flash model to return well-formed JSON responses, including the transcription, translation, sentiment analysis, and star rating, making it easy to then populate the UI. This ensured a seamless user experience. Users are then shown both the original transcribed text in their own language and the translated, summarized version in English. Most importantly, the user is given full control to review and edit all AI-generated text and change the star rating before submitting the review. They can even speak again to add more content.


Driving engagement and capturing deeper user insights

The AI-powered voice review feature had a significant positive impact on user engagement. By enabling users to speak in their native language, redBus saw a 57% increase in review length and a notable increase in the overall volume of reviews.

The new feature successfully engaged a segment of the user base that was previously hesitant to type a review. Since implementation, user feedback has been overwhelmingly positive: customers appreciate the accuracy of the transcription and translation, and find the AI-generated summaries to be a concise overview of their longer, more detailed reviews.

Gemini Flash, although hosted in the cloud, delivered a highly responsive user experience. “A common observation from our partners and stakeholders has been that the level of responsiveness from our new AI feature is so fast and seamless that it feels like the AI is running directly on the device,” said Abhi. “This is a testament to the low latency of the Gemini Flash model, which has been a key factor in its success.”



An easier way to build with AI

For the redBus team, the project demonstrated how Firebase AI Logic and Gemini Flash empower mobile developers to build features that would otherwise require backend implementation. This reduces dependency on server-side changes and allows developers to iterate quickly and independently.

Following the success of the voice review feature, the team at redBus is exploring other use cases for on-device generative AI to further enhance their app. They also plan to use Google AI Studio to test and iterate on prompts moving forward. For Abhi, the lesson is clear: “It’s no longer about complex backend setups,” he said. “It’s about crafting the right prompt to build the next innovative feature that directly enhances the user experience.”



Get started

Learn more about how you can use Gemini and Firebase AI Logic to build generative AI features for your own app.


New agentic experiences for Android Studio, new AI APIs, the first Android XR device and more, in our Fall episode of The Android Show

Posted by Matthew McCullough, VP of Product Management, Android Developer

We’re in an important moment where AI changes everything, from how we work to the expectations that users have for your apps, and our goal on Android is to transform this AI evolution into opportunities for you and your users. Today in our Fall episode of The Android Show, we unpacked a bunch of new updates towards delivering the highest return on investment in building for the Android platform. From new agentic experiences for Gemini in Android Studio to a brand new on-device AI API to the first Android XR device, there’s so much to cover - let’s dive in! 


Build your own custom Gen AI features with the new Prompt API

On Android, we offer AI models on-device, or in the cloud.  Today, we’re excited to now give you full flexibility to shape the output of the Gemini Nano model by passing in any prompt you can imagine with the new Prompt API, now in Alpha. For flagship Android devices, Gemini Nano lets you build efficient on-device options where the users’ data never leaves their device. At I/O this May, we launched our on-device GenAI APIs using the Gemini Nano model, making common tasks easier with simple APIs for tasks like summarization, proofreading and image description. Kakao used the Prompt API to transform their parcel delivery service, replacing a slow, manual process where users had to copy and paste details into a form into just a simple message requesting a delivery, and the API automatically extracts all the necessary information. This single feature reduced order completion time by 24% and boosted new user conversion by an incredible 45%.


Tap into Nano Banana and Imagen using the Firebase SDK 

When you want to add cutting-edge capabilities across the entire fleet of Android devices, our  cloud-based AI solutions with Firebase AI Logic are a great fit. The excitement for models like Gemini 2.5 Flash Image (a.k.a. Nano Banana) and Imagen have been incredible; now your users can now generate and edit images using Nano Banana, and then for finer control, like selecting and transforming specific parts of an image, users can use the new mask-based editing feature that leverages the Imagen model. See our blog post to learn more. And beyond image generation, you can also use Gemini multimodal capabilities to process text, audio and image input. RedBus, for example, revolutionized their user reviews using Gemini Flash via Firebase AI Logic to make giving feedback easier, more inclusive, and reliable. The old problem? Short, low-quality text reviews. The new solution? Users can now leave reviews using voice input in their native languages. From the audio Gemini Flash is then generating a structured text response enabling longer, richer and more reliable user reviews. It's a win for everyone: travelers, operators, and developers!



Helping you be more productive, with agentic experiences in Android Studio

Helping you be more productive is our goal with Gemini in Android Studio, and why we’re infusing AI across our tooling. Developers like Pocket FM have seen an impressive development time savings of 50%. With the recent launch of Agent Mode, you can describe a complex goal in natural language and (with your permission), the agent plans and executes changes on multiple files across your project. The agent’s answers are now grounded in the most modern development practices, and can even cross-reference our latest documentation in real time. We demoed new agentic experiences such as updates to Agent Mode, the ability to upgrade APIs on your behalf, the new project assistant, and we announced you’ll be able to bring any LLM of your choice to power the AI functionality inside Android Studio, giving you more flexibility and choice on how you incorporate AI into your workflow. And for the newest stable features such as Back Up and Sync, make sure to download the latest stable version of Android Studio.



Elevating AI-assisted Android development, and improving LLMs with an Android benchmark

Our goal is to make it easier for Android developers to build great experiences. With more code being written by AI, developers have been asking for models that know more about Android development. We want to help developers be more productive, and that’s why we’re building a new task set for LLMs against a range of common Android development areas. The goal is to provide LLM makers with a benchmark, a north star of high quality Android development, so Android developers have a range of helpful models to choose for AI assistance. 


To reflect the challenges of Android development, the benchmark is composed of real-world problems sourced from public GitHub Android repositories. Each evaluation attempts to have an LLM recreate a pull request, which are then verified using human authored tests. This allows us to measure a model’s ability to navigate complex codebases, understand dependencies, and solve the kind of problems you encounter every day. 


We’re finalizing the task set we’ll be testing against LLMs, and will be sharing the results publicly in the coming months. We’re looking forward to seeing how this shapes AI assisted Android development, and the additional flexibility and choice it gives you to build on Android.


The first Android XR device: Samsung Galaxy XR

Last week was the launch of the first in a new wave of Android XR devices: the Galaxy XR, in partnership with Samsung. Android XR devices are built entirely in the Gemini era, creating a major new platform opportunity for your app. And because Android XR is built on top of familiar Android frameworks, when building adaptively, you’re already building for XR. To unlock the full potential of Android XR features, you can use the Jetpack XR SDK. The Calm team provides a perfect example of this in action. They successfully transformed their mobile app into an immersive spatial experience, building their first functional XR menus on the first day and a core XR experience in just two weeks by leveraging their existing Android codebase and the Jetpack XR SDK.  You can read more about Android XR from our Spotlight Week last week. 



Jetpack Navigation 3 is in Beta

The new Jetpack Navigation 3 library is now in beta! Instead of having behavior embedded into the library itself, we’re providing ‘how-to recipes’ with good defaults (nav3 recipes on github). Out of the box, it’s fully customizable, has animation support and is adaptive. Nav 3 was built from the ground up with Compose State as a fundamental building block. This means that it fully buys into the declarative programming model - you change the state you own and Nav3 reacts to that new state. On the Compose front, we’ve been working on making it faster and easier for you to build UI, covering the features you told us you needed from Views, while at the same time ensuring that Compose is performant.

Accelerate your business success on Google Play

With AI speeding up app development, Google Play is streamlining your workflow in Play Console so that your business growth can keep up with your code. The reimagined, goal-oriented app dashboard puts actionable metrics front and center. Plus, new capabilities are making your day-to-day operations faster, smarter, and more efficient: from pre-release testing with deep links validation to AI-powered analytics summaries and app strings localization. These updates are just the beginning. Check out the full list of announcements to get the latest from Play.  




Watch the Fall episode of The Android Show

Thank you for tuning into our Fall episode of The Android Show. We're excited to continue building great things together, and this show is an important part of our conversation with you. We'd love to hear your ideas for our next episode, so please reach out on X or LinkedIn. A special thanks to my co-hosts,  Rebecca Gutteridge and Adetunji Dahunsi, for helping us share the latest updates.




New tools and programs to accelerate your success on Google Play

Posted by Paul Feng, VP of Product Management, Google Play





Last month, we shared new updates showcasing our evolving vision for Google Play: a place where people can discover the content and experiences they love and where you can build and grow sustainable businesses. Our commitment to your success is at the heart of our continued investments.

Today, we're excited to introduce a new bundle of tools and programs designed to enhance your productivity and accelerate your growth. From simplifying technical integration and localization, to offering deeper insights and creating powerful new ways to engage your audience these features will help streamline your development lifecycle.


Watch our latest updates in The Android Show segment below or continue reading. You can also catch up on our latest Android developments by watching the full show.



Streamline your development and operations with new tools
We're launching new tools to remove friction from your tedious development tasks by helping you validate deep links and scale to new markets with Gemini-powered AI.


Simplify deep link validation with a built-in emulator
Troubleshooting deep links can be complex and time-consuming so we’re excited to launch a new, streamlined experience that allows you to instantly validate your deep links directly within Play Console. This means you can use a built-in emulator to test a deep link and immediately see the expected user experience on the spot, just as if someone clicked the URL on a real device.

Instantly validate your deep links using the new built-in emulator

Reach a global audience with Gemini-powered localization
We’re making it easier to bring your app or game to a global audience by simplifying localization. With our latest translation service, we've integrated the power of Gemini into Play Console to offer high-quality translations for your app strings, at no cost. This service automatically translates new app bundles into your selected languages, accelerating your title to new markets. Most importantly, you always remain in full control with the ability to preview the translated app with a built-in emulator and easily edit or disable translations.


Drive growth and engagement with AI-powered insights and You tab
We're launching new ways to help you reach and retain users, Including AI-powered insights and the new You tab for re-engagement.


Get faster insights with automated chart summaries
To help you spend less time interpreting data and more time acting on key insights, a new Gemini-powered feature on the Statistics page automatically generates descriptions of your charts. These summaries help you quickly understand key trends and events that might be affecting your metrics. For developers who use a screen reader, this feature also provides access to reporting in a way you haven't had before.

Get faster insights with new Gemini-powered chart summaries

Access objective-related metrics and actionable advice for audience growth
Earlier this year, we launched objective-based overview pages in Play Console to consolidate your key metrics, app performance, and actionable steps across essential workflows. With dedicated pages for Test & Release, Monitor & Improve, and Monetize with Play already live, we're excited to announce the full completion of this toolkit. The new Grow users overview page is now available, giving you a comprehensive, tailored view to help you acquire new users and expand your reach.

Track your key audience growth metrics on the new "Grow users" overview page


Boost re-engagement with the You tab
Last month, we launched You tab, a brand new, personalized destination on the Play Store. This is where users can discover and re-engage with content from their favorite apps and games with curated rewards, subscriptions, recommendations, and updates all in one place.

App developers can take advantage of this personalized destination by integrating with Engage SDK. This integration allows you to help people pick up right where they left off—like resuming a movie or playlist— or get personalized recommendations, all while seamlessly guiding them back into your app.

Game developers can use this surface to showcase timely in-game events, content updates, and special offers, making it easy for players to jump right back into the action. Promotional content, YouTube video listings, and Play Points coupons are now open to all game developers for creating a rich presence on the You tab. The availability of these powerful re-engagement tools is part of our broader commitment to game quality through the new Google Play Games Level Up program. Learn more about the program's guidelines here.


Showcase in-game events and offers on the new You tab

Optimize your monetization strategy and track performance
We're launching powerful new ways to configure your one-time products and track the full impact of your Play Points promotions with a new, consolidated reporting page.


Simplify catalog management for one-time products
Earlier this year, we introduced more flexible ways to configure one-time purchases. You can now offer your in-app products as limited-time rentals, and sign up for our early access program to get started with pre-orders. We've also launched a new taxonomy, building on our existing subscription model, to help you manage your catalog more efficiently. This new model unlocks significant flexibility to help you reach a wider audience and cater to different user preferences by letting you offer the same item in multiple ways. For example, you can sell an item in one country and rent it in another—helping Play better surface relevant offerings to users. Explore these new capabilities today in Play Console.

Manage your catalog more efficiently with new ways to configure one-time products

Understand the impact and performance of Play Points promotions
With Play Points recently opened to all eligible titles, you can now better understand the impact of your promotions. The new Play Points page in Play Console lets you see the total revenue, buyers and acquisitions that all Play Points promotions have generated. This reporting covers both your developer-created offers, as well as new reporting for Google-funded Play Points promotions, which includes direct and post-promotion performance metrics.

   

New reporting for Play Points promotions

The features announced today are more than just updates; they are the building blocks of a powerful growth engine for your business. We hope you start exploring these new capabilities today and continue sharing feedback so we can build the tools you need to build a thriving, sustainable business on Google Play.