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Predict User Churn on Gaming Apps with Google Analytics Data using BigQuery ML

User retention can be a major challenge for mobile game developers. According to the Mobile Gaming Industry Analysis in 2019, most mobile games only see a 25% retention rate for users after the first day. To retain a larger percentage of users after their first use of an app, developers can take steps to motivate and incentivize certain users to return. But to do so, developers need to identify the propensity of any specific user returning after the first 24 hours. 

In this blog post, we will discuss how you can use BigQuery ML to run propensity models on Google Analytics 4 data from your gaming app to determine the likelihood of specific users returning to your app.

You can also use the same end-to-end solution approach in other types of apps using Google Analytics for Firebase as well as apps and websites using Google Analytics 4. To try out the steps in this blogpost or to implement the solution for your own data, you can use this Jupyter Notebook. 

Using this blog post and the accompanying Jupyter Notebook, you’ll learn how to:

  • Explore the BigQuery export dataset for Google Analytics 4
  • Prepare the training data using demographic and behavioural attributes
  • Train propensity models using BigQuery ML
  • Evaluate BigQuery ML models
  • Make predictions using the BigQuery ML models
  • Implement model insights in practical implementations

Google Analytics 4 (GA4) properties unify app and website measurement on a single platform and are now default in Google Analytics. Any business that wants to measure their website, app, or both, can use GA4 for a more complete view of how customers engage with their business. With the launch of Google Analytics 4, BigQuery export of Google Analytics data is now available to all users. If you are already using a Google Analytics 4 property, you can follow this guide to set up exporting your GA data to BigQuery.

Once you have set up the BigQuery export, you can explore the data in BigQuery. Google Analytics 4 uses an event-based measurement model. Each row in the data is an event with additional parameters and properties. The Schema for BigQuery Export can help you to understand the structure of the data.  

In this blogpost, we use the public sample export data from an actual mobile game app called “Flood It!” (Android, iOS) to build a churn prediction model. But you can use data from your own app or website. 

Here’s what the data looks like. Each row in the dataset is a unique event, which can contain nested fields for event parameters.

  SELECT *
FROM `firebase-public-project.analytics_153293282.events_*`
TABLESAMPLE SYSTEM (1 PERCENT)
table

This dataset contains 5.7M events from over 15k users.

  SELECT 
    COUNT(DISTINCT user_pseudo_id) as count_distinct_users,
    COUNT(event_timestamp) as count_events
FROM
  `firebase-public-project.analytics_153293282.events_*
count

Our goal is to use BigQuery ML on the sample app dataset to predict propensity to user churn or not churn based on users’ demographics and activities within the first 24 hours of app installation.

data

In the following sections, we’ll cover how to:

  1. Pre-process the raw event data from GA4
    1. Identify users & the label feature
    2. Process demographic features
    3. Process behavioral features
  2. Train classification model using BigQuery ML
  3. Evaluate the model using BigQueryML
  4. Make predictions using BigQuery ML
  5. Utilize predictions for activation

Pre-process the raw event data

You cannot simply use raw event data to train a machine learning model as it would not be in the right shape and format to use as training data. So in this section, we’ll go through how to pre-process the raw data into an appropriate format to use as training data for classification models.

This is what the training data should look like for our use case at the end of this section:

user id

Notice that in this training data, each row represents a unique user with a distinct user ID (user_pseudo_id). 

Identify users & the label feature

We first filtered the dataset to remove users who were unlikely to return the app anyway. We defined these ‘bounced’ users as ones who spent less than 10 mins with the app. Then we labeled all remaining users:

  • churned: No event data for the user after 24 hours of first engaging with the app.
  • returned: The user has at least one event record after 24 hours of first engaging with the app.

For your use case, you can have a different definition of bounce and churning. Also you can even try to predict something else other than churning, e.g.:

  • whether a user is likely to spend money on in-game currency 
  • likelihood of completing n-number of game levels
  • likelihood of spending n amount of time in-game etc.

In such cases, label each record accordingly so that whatever you are trying to predict can be identified from the label column.

From our dataset, we found that ~41% users (5,557) bounced. However, from the remaining users (8,031),  ~23% (1,883) churned after 24 hours:

  SELECT
    bounced,
    churned, 
    COUNT(churned) as count_users
FROM
    bqmlga4.returningusers
GROUP BY 1,2
ORDER BY bounced
boucned

To create these bounced and churned columns, we used the following snippet of SQL code. 

  ...
#churned = 1 if last_touch within 24 hr of app installation, else 0
IF (user_last_engagement < TIMESTAMP_ADD(user_first_engagement, 
      INTERVAL 24 HOUR),
    1,
    0 ) AS churned,
#bounced = 1 if last_touch within 10 min, else 0
IF (user_last_engagement <= TIMESTAMP_ADD(user_first_engagement, 
      INTERVAL 10 MINUTE),
    1,
    0 ) AS bounced,
...

You can view the Jupyter Notebook for the full query used for materializing the bounced and churned labels. 

Process demographic features

Next, we added features both for demographic data and for behavioral data spanning across multiple columns. Having a combination of both demographic data and behavioral data helps to create a more predictive model. 

We used the following fields for each user as demographic features:

  • geo.country
  • device.operating_system
  • device.language

A user might have multiple unique values in these fields — for example if a user uses the app from two different devices. To simplify, we used the values from the very first user engagement event.

  CREATE OR REPLACE VIEW bqmlga4.user_demographics AS (
  WITH first_values AS (
      SELECT
          user_pseudo_id,
          geo.country as country,
          device.operating_system as operating_system,
          device.language as language,
          ROW_NUMBER() OVER (PARTITION BY user_pseudo_id ORDER BY event_timestamp DESC) AS row_num
      FROM `firebase-public-project.analytics_153293282.events_*`
      WHERE event_name="user_engagement"
      )
  SELECT * EXCEPT (row_num)
  FROM first_values
  WHERE row_num = 1 #first engagement
);

Process behavioral features

There is additional demographic information present in the GA4 export dataset, e.g. app_info, device, event_params, geo etc. You may also send demographic information to Google Analytics through each hit via user_properties. Furthermore, if you have first-party data on your own system, you can join that with the GA4 export data based on user_ids. 

To extract user behavior from the data, we looked into the user’s activities within the first 24 hours of first user engagement. In addition to the events automatically collected by Google Analytics, there are also the recommended events for games that can be explored to analyze user behavior. For our use case, to predict user churn, we counted the number of times the follow events were collected for a user within 24 hours of first user engagement: 

  • user_engagement
  • level_start_quickplay
  • level_end_quickplay
  • level_complete_quickplay
  • level_reset_quickplay
  • post_score
  • spend_virtual_currency
  • ad_reward
  • challenge_a_friend
  • completed_5_levels
  • use_extra_steps

The following query shows how these features were calculated:

  WITH
  events_first24hr AS (
    SELECT
      e.*
    FROM
      `firebase-public-project.analytics_153293282.events_*` e
    JOIN
      bqmlga4.returningusers r
      ON
        e.user_pseudo_id = r.user_pseudo_id
    WHERE
      TIMESTAMP_MICROS(e.event_timestamp) <= r.ts_24hr_after_first_engagement
  )
SELECT
  user_pseudo_id,
  SUM(IF(event_name = 'user_engagement', 1, 0)) AS cnt_user_engagement,
  # ... repeated for all behavior data ... 
  SUM(IF(event_name = 'use_extra_steps', 1, 0)) AS cnt_use_extra_steps,
FROM
  events_first24hr
GROUP BY
  1

View the notebook for the query used to aggregate and extract the behavioral data. You can use different sets of events for your use case. To view the complete list of events, use the following query:

  SELECT
    event_name,
    COUNT(event_name) as event_count
FROM
    `firebase-public-project.analytics_153293282.events_*`
GROUP BY 1
ORDER BY
   event_count DESC

After this we combined the features to ensure our training dataset reflects the intended structure. We had the following columns in our table:

  • User ID:
    • user_pseudo_id
  • Label:
    • churned
  • Demographic features
    • country
    • device_os
    • device_language
  • Behavioral features
    • cnt_user_engagement
    • cnt_level_start_quickplay
    • cnt_level_end_quickplay
    • cnt_level_complete_quickplay
    • cnt_level_reset_quickplay
    • cnt_post_score
    • cnt_spend_virtual_currency
    • cnt_ad_reward
    • cnt_challenge_a_friend
    • cnt_completed_5_levels
    • cnt_use_extra_steps
    • user_first_engagement

At this point, the dataset was ready to train the classification machine learning model in BigQuery ML. Once trained, the model will output a propensity score between churn (churned=1) or return (churned=0) indicating the probability of a user churning based on the training data.

Train classification model 

When using the CREATE MODEL statement, BigQuery ML automatically splits the data between training and test. Thus the model can be evaluated immediately after training (see the documentation for more information).

For the ML model, we can choose among the following classification algorithms where each type has its own pros and cons:

model

Often logistic regression is used as a starting point because it is the fastest to train. The query below shows how we trained the logistic regression classification models in BigQuery ML.

  CREATE OR REPLACE MODEL bqmlga4.churn_logreg
TRANSFORM(
  EXTRACT(MONTH from user_first_engagement) as month,
  EXTRACT(DAYOFYEAR from user_first_engagement) as julianday,
  EXTRACT(DAYOFWEEK from user_first_engagement) as dayofweek,
  EXTRACT(HOUR from user_first_engagement) as hour,
  * EXCEPT(user_first_engagement, user_pseudo_id)
)
OPTIONS(
  MODEL_TYPE="LOGISTIC_REG",
  INPUT_LABEL_COLS=["churned"]
) AS
SELECT
  *
FROM
  bqmlga4.train

We extracted month, julianday, and dayofweek  from datetimes/timestamps as one simple example of additional feature preprocessing before training. Using TRANSFORM() in your CREATE MODEL query allows the model to remember the extracted values. Thus, when making predictions using the model later on, these values won’t have to be extracted again. View the notebook for the example queries to train other types of models (XGBoost, deep neural network, AutoML Tables).

Evaluate model

Once the model finished training, we ran ML.EVALUATE to generate precision, recall, accuracy and f1_score for the model:

  SELECT
  *
FROM
  ML.EVALUATE(MODEL bqmlga4.churn_logreg)
row

The optional THRESHOLD parameter can be used to modify the default classification threshold of 0.5. For more information on these metrics, you can read through the definitions on precision and recall, accuracy, f1-score, log_loss and roc_auc. Comparing the resulting evaluation metrics can help to decide among multiple models.Furthermore, we used a confusion matrix to inspect how well the model predicted the labels, compared to the actual labels. The confusion matrix is created using the default threshold of 0.5, which you may want to adjust to optimize for recall, precision, or a balance (more information here).

  SELECT
  expected_label,
  _0 AS predicted_0,
  _1 AS predicted_1
FROM
  ML.CONFUSION_MATRIX(MODEL bqmlga4.churn_logreg)
expected

This table can be interpreted in the following way:

actual

Make predictions using BigQuery ML

Once the ideal model was available, we ran ML.PREDICT to make predictions. For propensity modeling, the most important output is the probability of a behavior occurring. The following query returns the probability that the user will return after 24 hrs. The higher the probability and closer it is to 1, the more likely the user is predicted to return, and the closer it is to 0, the more likely the user is predicted to churn.

  SELECT
  user_pseudo_id,
  returned,
  predicted_returned,
  predicted_returned_probs[OFFSET(0)].prob as probability_returned
FROM
  ML.PREDICT(MODEL bqmlga4.churn_logreg,
  (SELECT * FROM bqmlga4.train)) #can be replaced with a proper test dataset

Utilize predictions for activation

Once the model predictions are available for your users, you can activate this insight in different ways. In our analysis, we used user_pseudo_id as the user identifier. However, ideally, your app should send back the user_id from your app to Google Analytics. In addition to using first-party data for model predictions, this will also let you join back the predictions from the model into your own data.

  • You can import the model predictions back into Google Analytics as a user attribute. This can be done using the Data Import feature for Google Analytics 4. Based on the prediction values you can Create and edit audiences and also do Audience targeting. For example, an audience can be users with prediction probability between 0.4 and 0.7, to represent users who are predicted to be “on the fence” between churning and returning.
  • For Firebase Apps, you can use the Import segments feature. You can tailor user experience by targeting your identified users through Firebase services such as Remote Config, Cloud Messaging, and In-App Messaging. This will involve importing the segment data from BigQuery into Firebase. After that you can send notifications to the users, configure the app for them, or follow the user journeys across devices.
  • Run targeted marketing campaigns via CRMs like Salesforce, e.g. send out reminder emails.

You can find all of the code used in this blogpost in the Github repository:

https://github.com/GoogleCloudPlatform/analytics-componentized-patterns/tree/master/gaming/propensity-model/bqml

What’s next? 

Continuous model evaluation and re-training

As you collect more data from your users, you may want to regularly evaluate your model on fresh data and re-train the model if you notice that the model quality is decaying.

Continuous evaluation—the process of ensuring a production machine learning model is still performing well on new data—is an essential part in any ML workflow. Performing continuous evaluation can help you catch model drift, a phenomenon that occurs when the data used to train your model no longer reflects the current environment. 

To learn more about how to do continuous model evaluation and re-train models, you can read the blogpost: Continuous model evaluation with BigQuery ML, Stored Procedures, and Cloud Scheduler

More resources

If you’d like to learn more about any of the topics covered in this post, check out these resources:

Or learn more about how you can use BigQuery ML to easily build other machine learning solutions:

Let us know what you thought of this post, and if you have topics you’d like to see covered in the future! You can find us on Twitter at @polonglin and @_mkazi_.Thanks to reviewers: Abhishek Kashyap, Breen Baker, David Sabater Dinter.

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Hybrid Work with Google Workspace: What Customers can Expect

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After the announcement of Google's vision of hybrid workplace in June, Google Workspace brings forth ways it can help customers' organizations transform experiences in the emerging hybrid world with tools and best-practices!

In June, we shared our vision for navigating the future of hybrid work with a single connected experience in Google Workspace. Now, as many of our customers begin to embark on their own hybrid journeys, I wanted to share how we’re helping them bridge the gaps in this new way of working.

A dedicated place for team collaboration: Spaces are now live

Spaces are the central place for team collaboration in Workspace and starting today, Spaces are live for all users. Spaces are unique in that they are tightly integrated with Google Workspace tools like Calendar, Drive, Docs, Sheets, Slides, Meet, and Tasks, providing a better way for people to engage in topic-based discussions, share knowledge and ideas, move projects forward and build communities and team culture.

We hear from our customers that they’re continuing to work across a broad range of locations and working hours, and that Spaces can be a central hub for collaboration, both in real time and asynchronously. Instead of starting an email chain or scheduling a video meeting, teams can come together directly in a Space to move projects and topics along, whether it’s a team of 10 for “2022 Roadmap Planning” or a team of 1,000 for the “Company-wide All-Hands.”

With Spaces, teams can share ideas, collaborate on documents, and manage tasks from a single place. Because all their work is preserved for future reference, team members can easily jump in and contribute at a time that works best for them, seeing a full history of the conversations, context, and content along the way. Using Spaces has already helped my own team, which is spread across time zones and varied in its working styles. It’s been helpful to rely on Spaces to retain and structure foundational knowledge, whether it’s for onboarding a new teammate or preserving context when someone moves to a new role.

We’re just getting started with Spaces and I’m excited to share a little more about where we’ll take it next. 

In the coming months, our users will see: 

  • Streamlined navigation: A flexible user interface helps users easily access their inbox, chats, Spaces and meetings—all from a single location—so they can stay on top of everything that’s important.
  • Discoverable Spaces: Spaces and their content can be made discoverable to all members of an organization, so other people can find and join the conversation. Administrators can also set discoverability as the default for their organization.
  • Enhanced search: Allows users to easily find content from within and across Spaces, or even discover new Spaces to join. “Search everything” in Spaces opens up powerful new collaboration possibilities and makes it easier to access the team’s collective knowledge base.
  • In-line topic threading: The ability to reply to any message within a Space fuels deeper discussions and collaboration across teams and organizations.
  • Robust security & admin features: Tools for content moderation, managing Spaces, and establishing the right rules for healthy communication across domains and companies.
1 In-line topic threading in spaces fuels deeper discussions.jpg
In-line topic threading in Spaces fuels deeper discussions

Making meetings more hybrid friendly

Spaces will play an important role in laying the groundwork for meetings and, in some cases, reduce the number of meetings a team needs to gain alignment. Having a dedicated space for asynchronous collaboration, with access to all the right content and context, will help teams be more deliberate about scheduling meetings. This is top of mind for us and many of our customers given the rise in meeting fatigue over the last 18 months. But when having a meeting genuinely feels worthwhile, the experience of transitioning into (and out of) it from different collaboration touchpoints should be seamless. 

Over the past few months, in collaboration with many customers and across teams within Google, we developed a handbook for navigating hybrid work that includes best-practice blueprints for the five most common hybrid meeting types. Because meetings are a foundational piece of hybrid work, our goal is to make them as productive, immersive and inclusive as possible. 

Scheduling meetings that work across the entire team

With hybrid work, many teams—my own included—have locations and working hours that can change daily. When many of us were together in the office, we might have tended to schedule meetings at a time that favored the “in-office majority,” but now it’s especially important to schedule meetings that scale across the entire distributed team. 

Now, beyond indicating their virtual or physical presence when accepting meeting invites, team members can set their location for each work day in Calendar. When combined, these capabilities allow meeting organizers and on-site support teams to plan for the right mix of in-person and virtual attendance. They can also provide greater visibility and help set expectations across hybrid teams.

2 Working location in Google Calendar helps set expectations with co-workers.gif
Working location in Google Calendar helps set expectations with co-workers

Making meetings more spontaneous

Remember when you’d casually bump into a colleague in the office hallway or a break room and start a conversation that sparked new ideas? That’s perhaps the thing I’m looking forward to most as more of my team plan their part-time return to the office. These casual encounters were always a great way to build relationships and learn about topics that might not come up in structured meetings. To help enable these spontaneous connections when teammates aren’t in the office together, we’re bringing Google Meet calling to Workspace.


Google Meet calling is a seamless experience of initiating a video or audio call between one or more participants, complementing more structured, scheduled video meetings. Our intention is to bring Meet calling to all the natural endpoints in Workspace where you’d initiate an ad-hoc call including chats, people cards, and Spaces, but this will come first to one-to-one chats within the Gmail mobile app. Soon I’ll be able to call members of my team directly from a one-to-one chat. This will ring their device running the Gmail mobile app and send a call chip to our chat on their laptop, so they can easily answer from any device. It’s not quite the same as a spontaneous hallway conversation, but it might be the next best thing in a hybrid setting.

3 Google Meet calling.gif
Google Meet calling from 1:1 chats in the Gmail mobile app easily connects co-workers

Ensuring collaboration equity in hybrid meetings

How do we ensure that hybrid meetings aren’t two meetings in one, divided between those in-person and those who are remote? Effective hybrid meetings need a unified experience so that the people sitting together in the same room can interact with their remote colleagues seamlessly, without it ever feeling like there are two parallel conversations.

We designed Companion mode in Google Meet to specifically meet this challenge and we’ll start rolling it out to customers in November. With Companion mode I can host or join a meeting from within a conference room using my laptop while leveraging the in-room audio and video—and it all happens without any awkward audio feedback. This functionality lets me share content or see presentations up close on my own device, access the meeting chat and whiteboard, initiate and vote on polls, or post a question in Q&A, just as I would from home. And to ensure that users have greater choice over how they participate in meetings, live-translated captions will be available in Meet and through Companion mode by the end of the year. We’re currently working on translating meetings in English to French, German, Spanish and Portuguese, with many more languages coming in the future.

4 Companion mode.gif
Companion mode let’s in-room attendees host, present and fully participate in meetings

Expanding choice and flexibility for meeting hardware

While Companion mode keeps people in the room seamlessly connected to their remote colleagues, meeting hardware plays a crucial role in making hybrid meetings feel more immersive and human, with the ability for everyone to be clearly seen and heard. To provide more flexibility and choice on this foundation of how we come together in a hybrid work world, we’re announcing an expanded Google Meet hardware portfolio and interoperability with other conference room solutions.

First, we’re announcing two new all-in-one video conferencing devices to complement our Series One Room Kits. The Series One Desk 27 is an all-in-one 27” device that’s perfect for small shared spaces or your desktop, either in the office or at home. Series One Board 65 is an all-in-one 65” 4K device that can be paired with an optional stand for ultimate configuration flexibility—turning any room or space into a video collaboration hub in minutes. 

Both devices feature collaboration capabilities with the Jamboard app built right in, and each can be used as an external display. While optimized for Google Meet, USB-C connection from your laptop gives you the flexibility to use the meeting app of your choice while leveraging the high-fidelity audio and high-definition video on Series One Desk 27 and Board 65. You can learn more about these devices developed in partnership with Avocor through our on-demand webinar starting at 9 AM PT today.
https://www.youtube.com/embed/BR81EAce5BQ?enablejsapi=1&

We’re also proud to announce new third-party devices coming to the Google Meet hardware ecosystem. Google has certified the Logitech Rally Bar Mini and Rally Bar for Google Meet, which provide complete room solutions for small and mid-sized rooms. You can learn more about these Google Meet-certified products in Logitech’s September 21 webinar. Additionally, the Rayz Rally Pro is a new mobile device speaker dock by Appcessori that will automatically launch Google Meet for video meetings and provide an improved audio experience from your mobile device.

5 New third-party devices certified for Google Meet.jpg
New third-party devices certified for Google Meet

Certified Google Meet hardware ensures high-quality video and audio in meetings and is easy to deploy and manage. You can view our full Google Meet device portfolio and purchase directly from approved resellers by visiting the Google Meet Hardware website. 

While Google Meet customers enjoy a full-featured experience on Meet hardware, we realize they sometimes need to connect with people outside of their video calling network. To enable this, organizations can use Pexip for Google Meet to seamlessly join Meet meetings from the widest range of third-party video conferencing solutions. Today, we’re also announcing support for bidirectional interoperability with devices from Webex by Cisco and Google Meet hardware. Soon you’ll be able to launch a Google Meet meeting on Webex hardware and a Webex meeting on Google Meet hardware. Calling interoperability between Meet and Webex is supported on Series One Board 65, Series One Desk 27, and the rest of the Google Meet hardware portfolio, as well as Webex Room Series, Room Kit Series, Desk Series and Board Series. We expect general availability later this year. You can find more details here. We plan to give Meet users even broader calling interoperability with support for other conferencing services in the near future. 

Navigating hybrid work is a journey

We’re thrilled to bring these new Google Workspace experiences and devices to our customers, but we also realize that navigating hybrid work will be a journey of learning and adaptation for every organization. Two resources we recently developed can help along the way:

Bridging the gaps in the emerging hybrid work world will necessarily be a combination of technology, workplace culture, and reimagining the use of physical spaces. But developing a “hybrid-first” mindset starts with the tools people use every day to connect, create, and collaborate, and Google Workspace will continue to play a crucial role in that evolution.

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Transitioning from Amazon Glacier to Google Cloud

DOWNLOAD WHITEPAPER

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This comprehensive guide provides a detailed process for migrating archival workloads from Amazon Glacier to Google Cloud Storage Nearline in the Indian context. The process involves carefully planning data retrieval and staging strategies to ensure an efficient and cost-effective migration.

The whitepaper includes:

  • Different storage methods on Amazon Glacier and their respective retrieval processes.
  • Recommendations on managing retrieval costs to avoid high charges from Amazon Web Services.
  • The recommended rate for data availability and download to prevent unnecessary repetition of the process.
  • Utilization of Google Compute Engine for data staging, if stored directly in Amazon Glacier.
  • Use of command-line utility, gsutil, or the Storage Transfer Service for transferring data from the staging location to Google Cloud Storage Nearline.
  • Insights to achieve a streamlined and economical migration process from Amazon Glacier to Google Cloud Storage Nearline.
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RAMPing Up Cloud Migration Process

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Cloud migration tasks and post migration management challenges are now a thing of the past! Google Cloud's Rapid Assessment & Migration Program (RAMP) offers a holistic framework based on tangible customer TCO and ROI migration analyses.

As enterprises accelerate their migration to the cloud, they experience more notable mid- and late-phase migration challenges. Specifically, 41% face challenges when optimizing apps in the cloud post-migration, and 38% struggle with performance issues on workloads migrated to the cloud. Further, organizations have also increased reliance on outside consultants and other service providers for early-stage cloud migration tasks to ongoing management post-implementation.1

To help customers through these challenges with a simple, quick path to a successful cloud migration, Google Cloud created our comprehensive Rapid Assessment & Migration Program (RAMP). And we’ve got some exciting developments to share with our customers and partners:

Expanded focus on post-migration TCO/ROI


Given the complex nature of cloud migrations, we are committed to meeting our customers where they’re at in their cloud journeys and partnering with them to achieve their business goals — be it building customer value through innovation, driving cost efficiencies, or increasing competitive differentiation and productivity. RAMP is a holistic framework based on tangible customer TCO and ROI analyses, that supports our customers’ journeys all the way through: from assessing their digital landscapes across multiple sources including on-prem and other clouds, and identifying prioritized target workloads to building a comprehensive migration and modernization plan.

Accelerate positive outcomes with expert partners


Customers can also now expect a more streamlined migration experience through our ecosystem of partners who have completed their cloud migration specialization. Last week, we announced industry-leading updates to our partner funding programs with new assessment and consumption packages that simplify and accelerate our customers’ journey to Google Cloud, at little-to-no cost. These packages offer prescriptive pathways for infrastructure and application modernization initiatives, empowering our partners to support our customers at every stage — from discovery and planning to migration and modernization.

Through our partner ecosystem, our customers can expect:

  • Distinct funding packages for assessment, planning, and migration
  • Faster approval processes for accelerated deployments
  • More partners eligible to participate in RAMP and access these new funding packages

Sustainability through migration


Another major focus area for RAMP is helping enterprises optimize their migration planning and maximize their ROI by including their business and technical considerations early in the process and including any sustainability goals they may have. To aid with their sustainability efforts, we are excited to share that customers can now receive a Digital Sustainability Report along with their IT assessments – enabling sustainability to be built into their migration strategies. The report provides actionable insights to measure and reduce their environmental impact, and is based on some of Google Cloud’s own best practices, having been carbon-neutral for decades and looking to run on carbon-free energy by 2030.

We are committed to solving complex problems for our customers and partners, and these updates are a reflection of the feedback we receive. Simplify your cloud migration strategy today by requesting your free assessment, finding a partner to work with, or talking to your existing partner to get started.

1. Forrester Consulting, State Of Public Cloud Migration; A study commissioned by Google, 2022

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Financial Firms Can Enjoy These 8 Benefits by Migrating and Running on Google Cloud VMware Engine

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IDG's recent whitepaper, Financial Services Spotlight: Elevating agility and security in the cloud, points at the 8 benefits of “lifting and shifting” on-premises applications and workloads to Google Cloud VMware Engine. Learn more!

The COVID-19 pandemic brought dramatic changes to the financial services industry. Already under pressure from nimble young fintechs to modernize, established banks and insurers were undergoing incremental digital transformation. But in 2020, they hit the gas pedal. Branches closed and remote work became the norm. Almost overnight, employees needed secure remote access to corporate systems, and customers expected to be able to complete even complex transactions whenever they wanted, on whatever device. Even as things return to normal, many of these shifts are likely to be permanent. According to Forrester Research, nearly 90% of global financial services CIOs and SVPs believe that improving their application portfolio is key to improving customer experience and driving revenue.1 The problem? Replacing legacy systems with cloud-based SaaS enterprise software is a massive, time- and resource-intensive process. 

IDG’s recently completed white paper, Financial Services Spotlight: Elevating agility and security in the cloud, highlights an alternative to the all-or-nothing approach to replatforming: “lifting and shifting” on-premises applications and workloads to the cloud without rewriting them. In this way, you keep your organization’s familiar architecture, but give it the scalability and cutting-edge technology of a modern cloud environment. That’s the promise of Google Cloud VMware Engine brings to the financial services industry.

Here’s a quick overview of the insights that the IDG study uncovers. Download the complete white paper.

Simpler migration, rich rewards

Google Cloud VMware Engine helps financial services companies seamlessly migrate and run 

VMware workloads natively on Google Cloud. Once in the cloud, firms can take advantage of Google Cloud services, access a robust third-party cloud ecosystem, and use the same VMware tools, processes, and policies their teams already know. The IDG study found that migrating to Google Cloud with Google Cloud VMware Engine offers multiple benefits:

  1. Create new customer experiences. Migrating to Google Cloud puts modern, cloud-native architectures and technologies — such as containers and microservices — easily within reach. These make it possible for financial institutions to quickly and securely launch new applications and update them on a continuous basis using DevOps pipelines. They also allow firms to craft more personalized customer experiences across channels using Google Cloud’s native AI and data analytics.
  2. Deliver new services. After migrating, financial services organizations can connect to multiple third-party service providers via cloud-based APIs to bring new, diverse services to their customers — without having to build from scratch. 
  3. Make the best use of IT resources. When your data and applications reside in Google Cloud, you’re no longer constrained by the physical storage and compute limits of on-premises infrastructure. This means your company can match capacity to demand — even during unexpected peaks. You also gain more visibility into your hybrid cloud environment with Google Cloud’s operations suite, which offers intelligent analysis and easier troubleshooting for your platform and applications.
  4. Gain fresh insights. The key to understanding what customers need and when they need it resides within your data, and data analytics in the cloud help you uncover those insights. Your company can connect to Google Cloud’s serverless data warehouse, BigQuery, which leverages data to deliver valuable insights for personalized customer experiences, rich compliance reporting, new product development, intelligent fraud detection, and more. 
  5. Choose what to move. Data governance regulations and requirements specific to the financial services industry mean that some data must remain on premises. Google Cloud VMware Engine lets you easily manage a hybrid cloud/on-premises environment to keep sensitive data fully under your control.
  6. Become more resilient. Google Cloud VMware Engine gives financial services firms a distributed architecture and centralized control for their applications to support vital business continuity functions, such as backup and disaster recovery. This is on top of the performance and availability of Google Cloud’s global infrastructure.
  7. Improve security. Using cloud-native application frameworks, administrators can issue patches and software updates centrally and automatically across their organizations. This reduces the risk of errors and security vulnerabilities. Firms also tap into the security features and capabilities of Google Cloud, including always-on encryption and AI-powered threat detection.
  8. Redirect IT resources. Migrating virtualized workloads to the cloud can free up talent and budget to develop new products and services — time that was previously spent on maintaining complex on-premises infrastructure. That means less effort spent keeping the lights on, and more resources directed toward creating innovative and differentiating customer experiences.

IDG research concluded that migrating business applications to Google Cloud with Google Cloud VMware Engine can help financial services companies stay ahead of change without incurring further technical debt from their legacy IT systems. Working with cloud-based systems can give your financial services company much of the scale, speed, and agility of a startup while still enjoying the benefits of being an established organization.

Read the complete white paper to learn more about the ways in which Google and VMware work together to accelerate digital transformation for financial services firms.

1. Vmware-forrester-financial-services-modern-app-report.pdf, A commissioned study conducted by Forrester Consulting on behalf of VMware, 2020

Whitepaper

Strengthening Operational Resilience Migrating to Google Cloud

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The most insightful time you'll spend today!

Operational resilience continues to be a key focus for financial services firms. Regulators from around the world are refocusing supervisory approaches on operational resilience to support the soundness of financial firms and the stability of the financial ecosystem. Our new white paper discusses the continuing importance of operational resilience to the financial services sector, and the role that a well-executed migration to Google Cloud can play in strengthening it.

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