Financial markets were among the first to adopt new technologies, and that has certainly been true of the derivatives markets, which were early adopters of electronic trading. Going forward, new capabilities will transform the way industry participants communicate, analyze, and trade.

I sat down with Google Cloud’s Phil Moyer and former SEC Commissioner, Troy Paredes, for a fireside chat at FIA Boca 2022 to discuss the future of markets and policy, the new technologies that are already paving the way for greater speed and transparency, and how cloud can help promote greater resiliency, performance, and security to enable the long-term vision for the market. The following is a summary of our discussion.

The current state of cloud technology

When it comes to technology adoption, we’re seeing the market and participants adopt cloud technologies, and increasingly, machine learning (ML) on a wider scale. Cloud technology allows for easier, faster, and much more secure experimentation with large datasets and ML.

A recent Google sponsored study by Coalition Greenwich (September, 2021) showed that more than 93% of trading systems, exchanges, and data providers are in some way providing services on the cloud. The same study, revealed that about 72% of the financial industry across the buy side and sell side, intend to consume public cloud-data based market data within the next 12 months.

Data-driven decision-making and risk management have always been, and continue to remain, the cornerstones of the financial markets. Over time, technology innovation has facilitated access to better insights from data, and therefore, better decision-making and the ability to manage risk. That expectation is now mainstream, and will continue to grow in sophistication.

The multi-phased technology trajectory

The movement of exchanges to the cloud will occur in a “crawl-walk-run” fashion, with low-hanging fruits the first to be picked in the near term while bigger, paradigmatic changes will occur over the medium and long term. Some organizations are starting all three stages simultaneously, understanding that each will move at an independent cadence.

The “crawl” phase is one in which foundations are built, starting with organizations moving data to the cloud and experimenting with some degree of analytics. It’s one of the most important phases because it’s where the opportunity to increase transparency and risk management takes shape.

In moving to the cloud, the infrastructure – which in the past relied on a combination of people, processes, and some technology – becomes the code that runs applications. This early phase is key to empowering organizations to shift to a cloud-based, agile-first operating model that makes it easier and more seamless to launch new products in the future, including by freeing up people and resources from IT management to more mission-focused work.

Establishing the cloud operating model simplifies the “walk” and “run” phases where compliance is more automated, latency-sensitive applications are more readily available, and the next generation of exchanges, market participants, and regulators is better prepared to meet future challenges.

The “walk” phase is where much of the innovation happens. Exchanges are making significant progress in leveraging foundational data decisions in the “crawl” phase and innovations in the cloud to improve settlement, clearing, risk management, collateral management, and compliance, and launch new products.

And finally, the “run” phase is where organizations will start to move the latency-sensitive markets to the cloud, as the markets increasingly will demand low-latency and high performance along with transparency and analytics to solve historical obstacles to market access.

Opportunities for both regulators and market participants

Any time significant technological change takes place, regulators explore its implications, particularly with respect to their ability to meet their regulatory objectives.

Increasingly, we are seeing technological change driving more opportunities for regulators and market participants alike. Such changes may also allow better protection of the marketplace, with greater integrity and transparency.

Over time, regulatory regimes – rules, regulations, statutes, interpretations, and guidance – will also adjust to new technologies, both benefiting the marketplace and advancing regulatory goals.

As one example, the cloud is increasing the ability to meet compliance obligations by allowing compliance to be built into transactions. Moreover, predicated on the vision of real-time regulatory reporting, and given the pace of technological change in the marketplace over the last several years, various regulators have been using more advanced analytics. This trend will continue to help them more effectively and efficiently meet their objectives, and monitor and meet the expectations they have for the entire market.

Machine learning’s role in the financial markets

Google Cloud’s head of AI and Industry Solutions, Andrew Moore, said that ML will be doing three key things for us in the next 10 years: giving us meaning, providing concierge services, and serving as a guardian. Extracting information that is critical to investor decision-making can be extremely important. With more data than ever, ML can increase the ability to process it while also becoming more accessible in the cloud and better supporting regulatory objectives.

The technology will likely manifest in trading and anti-money laundering activities as they relate market functions, as well as managing a wide variety of risks – supporting the interests of both investors and regulators in terms of decision-making, surveillance, and protections.

Rather than taking individuals out of the equation, the digitization of markets, assets, and guard rails combined with ML will allow people to focus their expertise in different ways to achieve key objectives.

Building the market foundation for the future

The goals of operational resiliency, security, and privacy will continue to be critical for building the market foundation for both participants and regulators. While technology promises to create advantages in concrete, tangible ways, it will be important to scrutinize potential risks and concerns.

Priority one for technology providers is to build an environment of trustless security, including encryption at motion and encryption at rest, ensuring that markets are operationally resilient while instilling confidence for any exchange that runs on top of that infrastructure. Multicloud architectures and approaches are likely also to be part of the solution for operational resilience.

Throughout time, liquidity has been the outcome of improved access, transparency, and security. Technology providers are responding by sharing both the responsibility for, and fate of, the markets of the future to build an efficient, faster, and more transparent and secure financial industry.

You can learn more about our approach in our newest white paper, Building the financial markets foundation for the future.

At one point or another, many of us have used a local computing environment for machine learning (ML). That may have been a notebook computer or a desktop with a GPU. For some problems, a local environment is more than enough. Plus, there’s a lot of flexibility. Install Python, install JupyterLab, and go!

What often happens next is that model training just takes too long. Add a new layer, change some parameters, and wait nine hours to see if the accuracy improved? No thanks. By moving to a Cloud computing environment, a wide variety of powerful machine types are available. That same code might run orders of magnitude faster in the Cloud.

Customers can use Deep Learning VM images (DLVMs) that ensure that ML frameworks, drivers, accelerators, and hardware are all working smoothly together with no extra configuration. Notebook instances are also available that are based on DLVMs, and enable easy access to JupyterLab. 

Benefits of using the Vertex AI custom training service

Using VMs in the cloud can make a huge difference in productivity for ML teams. There are some great reasons to go one step further, and leverage our new Vertex AI custom training service. Instead of training your model directly within your notebook instance, you can submit a training job from your notebook.

The training job will automatically provision computing resources, and de-provision those resources when the job is complete. There is no worrying about leaving a high-performance virtual machine configuration running.

The training service can help to modularize your architecture. As we’ll discuss further in this post, you can put your training code into a container to operate as a portable unit. The training code can have parameters passed into it, such as input data location and hyperparameters, to adapt to different scenarios without redeployment. Also, the training code can export the trained model file, enabling working with other AI services in a decoupled manner.

The training service also supports reproducibility. Each training job is tracked with inputs, outputs, and the container image used. Log messages are available in Cloud Logging, and jobs can be monitored while running.

The training service also supports distributed training, which means that you can train models across multiple nodes in parallel. That translates into faster training times than would be possible within a single VM instance.

Example Notebook

In this blog post, we are going to explain how to use the custom training service, using code snippets from a Vertex AI example. The notebook we’re going to use covers the end-to-end process of custom training and online prediction. The notebook is part of the ai-platform-samples repo, which has many useful examples of how to use Vertex AI.

Custom model training concepts

The custom model training service provides pre-built container images supporting popular frameworks such as TensorFlow, PyTorch, scikit-learn, and XGBoost. Using these containers, you can simply provide your training code and the appropriate container image to a training job. 

You are also able to provide a custom container image. A custom container image can be a good choice if you’re using a language other than Python, or are using an ML framework that is not supported by a pre-built container image. In this blog post, we’ll use a pre-built TensorFlow 2 image with GPU support.

There are multiple ways to manage custom training jobs: via the Console, gcloud CLI, REST API, and Node.js / Python SDKs. After jobs are created, their current status can be queried, and the logs can be streamed.

The training service also supports hyperparameter tuning to find optimal parameters for training your model. A hyperparameter tuning job is similar to a custom training job, in that a training image is provided to the job interface. The training service will run multiple trials, or training jobs with different sets of hyperparameters, to find what results in the best model. You will need to specify the hyperparameters to test; the range of values to explore for those hyperparameters; and details about the number of trials.

Both custom training and hyperparameter tuning jobs can be wrapped into a training pipeline. A training pipeline will execute the job, and can also perform an optional step to upload the model to Vertex AI after training.

How to package your code for a training job

In general, it’s a good practice to develop your model training code that is self-contained when especially executing them inside containers. This means the training codebase would operate in a standalone manner when executed. 

Below is a template of such a self-contained, heavily-commented Python script that you can follow for your own projects too.

  # Imports go here
import tensorflow_datasets as tfds
import tensorflow as tf
…
# Define the hyperparameters and constants like epochs, batch size, number of GPUs, etc
parser = argparse.ArgumentParser()
parser.add_argument('--lr', dest='lr',
                   default=0.01, type=float,
                   help='Learning rate.')
parser.add_argument('--epochs', dest='epochs',
                   default=10, type=int,
                   help='Number of epochs.')
...
args = parser.parse_args()
...
# Prepare data loaders
def make_datasets_unbatched():
 # Scaling CIFAR10 data from (0, 255] to (0., 1.]
 def scale(image, label):
   image = tf.cast(image, tf.float32)
   image /= 255.0
   return image, label
 datasets, info = tfds.load(name='cifar10',
                           with_info=True,
                           as_supervised=True)
 return datasets['train'].map(scale).cache().shuffle(BUFFER_SIZE).repeat()
# Build our model, compile, and train it
model = [define your model]
model.compile(loss=..., optimizer=..., metrics=...)
model.fit(...)
# Serialize our model
model.save(MODEL_DIR)

Note that the MODEL_DIR needs to be a location inside a Google Cloud Storage (GCS) bucket. This is because the training service can only communicate with that and not with our local system. Here is a sample location inside a GCS Bucket to save a model: gs://caip-training/cifar10-model where caip-training is the name of the GCS bucket.

Although we are not using any custom modules in the above code listing, one can easily incorporate them as we would normally inside a Python script. Refer to this document if you want to know more. Next up, we will review how to configure the training infrastructure, including the type and number of GPUs to use, and submit a training script to run inside the infrastructure. 

How to submit a training job, including configuring which machines to use

To train a deep learning model efficiently on large datasets, we need hardware accelerators that are suited to run matrix multiplication in a highly parallelized manner. Distributed training is also common when it comes to training a large model on a large dataset. For this example, we will be using a single Tesla K80 GPU. Vertex AI supports a range of different GPUs (find out more here). 

Here is how we initialize our training job with the Vertex AI SDK:

  job = aiplatform.CustomTrainingJob(
   display_name=JOB_NAME,
   script_path="task.py",
   container_uri=TRAIN_IMAGE,
   requirements=["tensorflow_datasets==1.3.0"],
   model_serving_container_image_uri=DEPLOY_IMAGE,
)

(aiplatform is aliased as from google.cloud import aiplatform)

Let’s review the arguments:

• display_name refers to a unique identifier to the training job used for easily locating it. 
• script_path refers to the path of the training script to run. This is the script we discussed in the section above.
• container_uri refers to the URI of the container that will be used to run our training script. For this, we have several options to choose from. For this example, we will use gcr.io/cloud-aiplatform/training/tf-gpu.2-1:latest. We will use this same container for deployment as well but with a slightly changed container URI. You can find the containers available for model training here and the containers available for deployment purposes can be found here. 
• requirements let us specify any external packages that might be required to run the training script. 
• model_serving_container_image_uri specifies the container URI that would be used during deployment. 

Note: Using separate containers for distinct purposes like training and deployment is often a good practice, since it isolates the relevant dependencies for each purpose.

We are now all set up to submit a custom training job:

  model = job.run(
       model_display_name=MODEL_DISPLAY_NAME,
       args=CMDARGS,
       replica_count=1,
       machine_type=TRAIN_COMPUTE,
       accelerator_type=TRAIN_GPU.name,
       accelerator_count=TRAIN_NGPU
   )

Here, we have:model_display_name that provides a unique name to identify our trained model. This comes in handy later down the pipeline when we would deploy it using the prediction service. args are our command-line arguments typically used to specify things like hyperparameter values.replica_count denotes the number of worker replicas to be used during training. machine_type specifies the type of base machine to be used during training. accelerator_type denotes the type of accelerator to be used during training. If we are interested in using a Tesla K80, then TRAIN_GPU should be specified as aip.AcceleratorType.NVIDIA_TESLA_K80. (aip is aliased as from google.cloud.aiplatform import gapic as aip.)accelerator_count specifies the number of accelerators to use. For a single host multi-GPU configuration, we would set the replica_count to 1 and then specify the accelerator_count as per our choice depending on the resource available under the corresponding compute zone. 

Note that model here is a google.cloud.aiplatform.models.Model object. It is returned by the training service after the job is completed. 

With this setup, we can actually start a custom training job that we can monitor. After we submit the above training pipeline, we should see some initial logs resembling this:

The link highlighted  in Figure 2 will redirect to the dashboard of the training pipeline which looks like so:

As seen in Figure 3, the dashboard provides a comprehensive summary of all the necessary artifacts related to our training pipeline. Monitoring your model training is also very important especially to catch any early training bugs. To view the training logs, we need to click the link beside the “Custom job” tab (refer to Figure 3). There also we are presented with roughly similar information as shown in Figure 3 but this time it includes the logs as well: 

Note: Once we submit the custom training job, a training pipeline is first created to provision the training. Then inside the pipeline, the actual training job is started. This is why we see two very similar dashboards above but they have different purposes. Let’s check out the logs (which is maintained using Cloud Logging automatically):

With Cloud Logging, it is also possible to set alerts on the basis of different criteria. For example, alerting the users when the training job fails or completes so that some immediate action could be taken. You can refer to this post for more details.  

After the training pipeline is completed, on your end, you will notice the success status:

Accessing the trained model

Recall that we had to serialize our model inside a GCS Bucket in order to make it compatible with the training service. So, after the model is trained, we can access it from that location. We can even directly load it using the following line of code:

  model = tf.keras.models.load_model('gs://[your-bucket-name]/[model-name]')

Note that we are referring to the TensorFlow model that resulted from training. The training service also maintains a similar “model” namespace to help us manage these models. Recall that the training service returns a  google.cloud.aiplatform.models.Model object as mentioned earlier. It comes with a deploy() method that allows us to deploy our model programmatically within minutes with several different options. Check out this link if you are interested in deploying your models using this option. 

Vertex AI also provides a dashboard for all the models that have been trained successfully and it can be accessed with this link. It resembles this:

If we click the model as listed in Figure 7, we should be able to directly deploy from the interface:

In this post, we will not be covering deployment, but you are encouraged to try it out yourself. After the model is deployed to an endpoint, you will be able to use it to make online predictions.

Wrapping Up

In this blog post, we discussed the benefits of using the Vertex AI custom training service, including better reproducibility and management of experiments. We also walked through the steps to convert your Jupyter Notebook codebase to a standard containerized codebase, which will be useful not only for the training service, but for other container-based environments. The example notebook provides a great starting point to understand each step, and to use as a template for your own projects.

“Hello, Marks & Spencer. How may we help you?”

As one of the biggest and best-loved retail brands in the UK, Marks & Spencer (M&S) is known for the personalized service it provides to its 30 million loyal customers. For 135 years and in 57 countries around the world, M&S has worked to meet and exceed customer expectations for quality and service. Since the advent of the telephone, this has meant cheerfully servicing customers who call in to M&S branches or into their contact centers, no matter what their request might be.

“Retail is in a state of flux and M&S is transforming to better serve our customers so that we can compete and win. Automating calls into our stores with Google voice recognition gives us every opportunity to get things right for our customers and keep them coming back again and again.”

—Akash Parmar, Enterprise Architect (Digital Customer Engagement), Marks & Spencer

The company now has a goal of bringing one third of its business online by 2022. In order to engage more customers online, it has opened a new voice channel hosted on Google Cloud.

The company previously had switchboards in 13 different stores across the UK and Ireland (UKI), handling up to nine million calls a year. But as its retail offering evolved across multiple channels, it was becoming increasingly difficult to quickly and effectively answer customer service requests using an outdated switchboard model.

Customers might call in to order an outfit they had seen in a store, to inquire about returning a dress they bought online, or to recover a lost umbrella in a food hall. Each of these different requests required a different routing response from staff, and if the company didn’t act soon, it knew that the cost of managing the increase in call volume would lead to a significant cost impact. M&S decided it was time to make a technological leap forward to meet customers’ expectations in the new, omnichannel retail environment.

“Retail is in a state of flux and M&S is transforming to better serve our customers so that we can compete and win,” says Akash Parmar, Enterprise Architect for Digital Customer Engagement at M&S. “Automating calls into our stores with Google voice recognition gives us every opportunity to get things right for our customers and keep them coming back again and again.”

Boosting opportunities for customer engagement with voice recognition

M&S customers were used to dealing with their local store for anything they needed. But as stores were completely separate from the online business, customers weren’t able to purchase something they’d seen online by calling stores because the store staff didn’t have access to platforms needed to place an online order securely. For a company that places a very high importance on customer experience, this was unacceptable.

Akash Parmar, Enterprise Architect for Customer Engagement, was set a the goal by Chris McGrath, M&S Programme Manager, to ensure that the right channel and the right level of assistance was available to customers at any point before, during, or after purchase. Akash set himself the challenge of building a platform that could adapt to all of these channels and scale very quickly.

“We didn’t have the resources to build a speech recognition platform. DVELP removed that obstacle. It understood what we wanted to achieve and how Google Voice APIs and Twilio could get us there. Whatever we want, DVELP builds it for us. DVELP always presents options, never problems.”

—Akash Parmar

In 2018, Akash reached out to Google Cloud partner DVELP, one of the UK’s leading experts on the Twilio programmable contact center platform and Google speech recognition technology. DVELP recommended a Google Cloud-based natural language speech recognition platform that leverages the audio stream intent detection functionality in the Contact Center AI solution, Dialogflow, as the heart of an inbound-call-handling strategy. This strategy was designed to improve routing accuracy, give customers more self-service options, and increase analyst visibility into customer journeys.

“We didn’t have the resources to build a speech recognition platform. DVELP removed that obstacle,” says Akash. “They understood what we wanted to achieve and how Google Cloud Voice APIs and Twilio could get us there. Whatever we want, DVELP builds it for us. DVELP always presents options, never problems.”

Using Google speech recognition to improve customer experience

M&S wanted to use natural language to enable customers to speak and state what help they required rather than choose from a list of options. This would help them answer the millions of calls coming in and figure out what customers needed quickly.

In order to do that, DVELP needed to consider how best to address tying customer intent to actions, while maintaining flexibility. DVELP recommended the unconventional choice of not referencing intent in the application layer, but mapping the available actions to the information required to perform them. These actions were then used to build a “declarative dictionary” for the customer service team.

By focusing on actions rather than intents, the solution enables the customer services team to configure actions to intents in virtually any combination of key-value pairs. Leveraging the fact that Dialogflow can detect and respond to customer intents in real time, M&S has already reached 92% accuracy in translating customer declarations to actionable intents.

“With Google Cloud speech recognition and Contact Center AI solutions such as Dialogflow, there’s no information that we can’t make sense of. No matter where you call from, who you are, your age, your gender: you speak, and we understand.”

—Akash Parmar

Akash recalls that once customers became comfortable with the prompt, “in a few words, how may we help you?” they started providing simple, concise responses, and the customer learning curve quickly leveled out. At that point, Google Cloud speech recognition and Dialogflow took over. “The technology worked perfectly and the result was like magic,” says Akash.

“With Google Cloud speech recognition and Contact Center AI solutions such as Dialogflow, there’s no information that we can’t make sense of,” he says. “No matter where you call from, who you are, your age, your gender: you speak, and we understand.”

Enabling self-service contact center improvements with Dialogflow

It was important to M&S that contact center employees be self-sufficient in updating the platform to reflect changes in demand. They needed to be able to easily react to a spike in inquiries about a special offer, for example, without relying on the engineering team. At the same time, neither Akash nor the DVELP team wanted the staff to have to learn error-prone JSON inside contexts, or write responses in order to get necessary information from customers.

DVELP’s creative solution was to fill out the “Action and parameters” section of every intent. This is usually reserved for collecting information from customer declarations, but was also easily adapted to implementing custom key-value pairs. This is particularly helpful in making sure that the contact center is ready to handle new promotions as they arise. As sales and special events are communicated to the contact center from the head office, staff can program specific vocabulary directly into Dialogflow, thanks to Contact Center AI, ensuring that the M&S system is immediately ready to handle related customer calls.

Rolling out the platform to the UK and Ireland

In just a few months after going live, calls are being efficiently routed to the contact center and its existing customer service platform. At the contact center, staff can quickly and easily respond to customer requests, place orders, and process returns. Thanks to the natural language capabilities of Google Cloud, a simple customer request like “order the red children’s dress in the Bath high street window in size six” not only gets correctly routed, but provides data points for future personalized interactions.

Being able to accurately recognize customer intent 92% of the time after less than four months since deployment is an important milestone for M&S. With a concurrent 89% voice-to-text accuracy rate for Dialogflow transcriptions, M&S has rolled out the successful speech recognition platform to all of its stores in UK and Ireland and customer service contact centers.

Akash is so pleased with the performance of the new Google Cloud platform that he’s focusing on what new functionality he can add next to improve customer experience even more.

“We’re working on collecting product codes from customers using natural language so we can give them stock availability details,” he shares. “We also want to use Google Cloud to enable a more conversational experience when customers are searching for help or FAQs on our website. At the same time, we’re looking at Contact Center AI and Dialogflow to provide a virtual assistant experience for our webchat journey. Thanks to our new voice solution, we can clearly understand the key issues that our customers face on a day-to-day basis; the aim now is to start solving these issues through self-service and automation.”

Anyone who has ever coached or played on a sports team, or had a child involved in sports, knows how difficult scheduling and logistics can be. From game and practice schedules, to uniforms and who’s bringing the snacks, it can be a lot for coaches, administrators, parents, and players to manage.

It’s no wonder that TeamSnap, a sports team, club, and tournament management app, has exploded in popularity worldwide. By syncing events to everyone’s personal calendars and providing messaging and payment tracking, TeamSnap makes communication and organization easy.

TeamSnap markets its app to coaches, players, and clubs via targeted YouTube ads. It also uses Google AdWords and DoubleClick to advertise on search results and run programmatic campaigns. These methods have been highly effective, helping TeamSnap grow to millions of users worldwide and become one of the most popular apps in the iOS app store.

As its business and data grew, TeamSnap was challenged to track ROI and measure the customer journey across channels and devices over time. The company’s marketing budget grew quickly, making it even more important to spend wisely. With data in Google Analytics 360, DoubleClick Campaign Manager, Google AdWords, and Salesforce, TeamSnap needed a way to link and correlate those data sources in a scalable, timely, and cost-effective way to understand the true impact of its digital marketing across websites and mobile apps.

To avoid the painstaking manual process of pulling data from multiple sources, TeamSnap began using Google Analytics 360, which integrates with Google BigQuery, to provide a fully managed big data analysis service. TeamSnap analyzes the data using Tableau, which connects directly to Google BigQuery for fast analytics and helps the company share and collaborate on that information with self-service ease.

The combination allows TeamSnap to easily track the activity of millions of users with self-service ease, without worrying about the scalability or availability of the big data platform.

“Before Google Analytics 360, Google BigQuery, and Tableau, tracking our return on ad spend was difficult because we had so much data,” says Ken McDonald, Chief Growth Officer at TeamSnap. “We didn’t always have insights to make the best choices. We don’t have that problem anymore because we’ve moved to real-time reporting. We find additional revenue growth opportunities almost daily.”

Making Ad Dollars Work Harder

TeamSnap now automatically imports unsampled Google Analytics 360 logs into the Google BigQuery data warehouse. To import data from other sources such as Google AdWords, DoubleClick, and YouTube, TeamSnap uses Google BigQuery Data Transfer Service. With all relevant data consolidated in Google BigQuery, TeamSnap can use Tableau to perform advanced analytics on its digital marketing, executing ad-hoc analyses in seconds, while eliminating data sampling issues, to improve accuracy. These analyses can also be reused and shared with internal and external stakeholders via Tableau Online, promoting governed reuse and consistency.

“Using Google Analytics 360 and Google BigQuery with Tableau to track our return on ad spend is ideal,” says Ken. “It’s easy to use SQL to query the data or explore it with drag-and-drop ease.”

With Google BigQuery, Ken and his team can bring all the data from the TeamSnap billing systems, internal CRM, and other Google services into one straightforward dataset that everyone uses. With Tableau, users are able to perform self-service analytics on this data and provision analyses via shared dashboards that communicate the same consistent truth across the company. These dashboards provide a single view of the business to discover new patterns and questions worth analyzing.

All of this results in enormous time savings because no one is re-inventing the wheel. “Using these tools, we immediately reallocated $300,000 of ad spend that was performing poorly, generating 200% ROI in the first two days,” says Ken.

Ken now spends his time analyzing data instead of trying to pull it all together, identifying pockets of inefficient spend in real time and reallocating those marketing dollars toward better performing campaigns.

“Before, we could only focus on the largest campaign-level datasets because it was so time consuming to pull the data,” he says. “With Google BigQuery and Tableau, we can examine our advertising ROI much more granularly and reallocate more than $10 million in ad spend annually to grow the company faster and more efficiently.”

More Effective A/B Testing

To make sure it is delivering the best customer experiences, TeamSnap uses Google Optimize to run A/B tests on its website. It uses Google BigQuery and Tableau to verify and supplement these findings by measuring longer-term customer behavior across devices, spanning both web and mobile apps.

By pulling in data from Google Optimize, Google Analytics 360, Salesforce, and in-house billing and CRM systems, and understanding it with Tableau, TeamSnap has increased the accuracy and effectiveness of its A/B testing, gaining a more complete picture of customer onboarding and activity. In some cases, it found that short-term indicators it previously trusted were actually poor predictors of long-term behavior.

“Integration between Google Analytics 360 and Google BigQuery is seamless, giving us much more confidence in our A/B testing,” says Ken. “We’re constantly finding new and interesting ways to use our digital marketing data. Often, making a simple change can increase revenue by hundreds of thousands of dollars a year.”

Improving Product Quality

TeamSnap also uses Google BigQuery and Tableau to improve its own product, tracking customer activity at such a granular level that usability and functionality issues can be exposed and addressed faster. It’s also increasing customer engagement by verifying that potential customers are coming in through the right onboarding path—for example, a coach versus a player, or a consumer versus a club or other sports business. Using A/B testing to make sure customers are routed to the appropriate flow, TeamSnap drove $4 million in additional customer value each year.

“We initially chose Google BigQuery and Tableau to help with marketing, but we realized quickly that they could help us on the product side as well,” says Ken. “Most of the testing we do is about making things better and easier for our customers, and we’re accelerating that process with Google BigQuery and Tableau.”