Providing best-in-class customer service is crucial for the success of your business. Contact centers are a critical touch point, as they have to balance between representing your brand and prioritizing customer care. When your customers seek help and support, they expect efficient service that is accessible through modern voice and digital channels. In short, customer expectations are increasing—and that’s a problem if your contact center infrastructure and solutions are becoming outdated.

All of these factors are why today, we’re announcing Google Cloud Contact Center AI Platform, an expansion to Contact Center AI that offers an out-of-box, end-to-end solution for the contact center. It brings together the advantages of AI, cloud scalability, multi-experience capabilities, and tight integration with customer relationship management (CRM) platforms to unify sales, marketing, and support teams around data across the customer journey.

Improving customer experiences from all angles

Google Cloud’s Contact Center AI helps you leverage AI to scale your contact center interactions while maintaining a high level of customer satisfaction. Over the last two years, we have built a large group of partners, including the largest contact center and customer experience ISVs and our system integrator ecosystem, to bring Contact Center AI to customers. Today, we are helping enterprises across industries and geographies to cost-effectively reimagine contact center experiences. For example, Marks & Spencer reduced in-store call volume by 50%, and similarly, The Home Depot improved call containment by 185%, all while significantly increasing customer self-service engagement.

Adding to our Contact Center AI capabilities, Contact Center AI Platform is purpose-built for customer relationship management, extending your ability to offer personalized customer experiences that are consistent across your brand, whether delivered through a virtual agent, a human agent, or a combination of both. It eliminates many long-running pain points, from managing data fragmentation to replacing rigid customer experience flows with more engaging, personalized, and flexible support. With this addition, Contact Center AI now lets you:

• Orchestrate the customer journey by creating modern experiences that can be embedded in their chosen channels with mobile/web software developer kits (SDKs), compatible with iOS and Android;
• Leverage CRM as a single source of insight into the customer experience, to unify content, increase personalization, and automate processing with CRM data unification;
• Manage multiple channels without pivoting across voice, SMS, and chat support;
• Predict customer needs and route calls appropriately with AI-driven routing, based on both historical CRM data and real-time interactions;
• Automate scheduling, schedule adherence monitoring, and manage employee scheduling preferences with Workforce Optimization (WFO) integration;
• Provide customers with self-service via web or mobile interfaces using Visual Interactive Voice Response (IVR).

Helping you do more with contact centers

The addition of Contact Center AI Platform provides your partners the ability to integrate with Contact Center AI, so you can enjoy a more seamless experience operating your customer service center, with a complete view of the customer in a single workspace that includes real-time AI intelligence, native agent call controls, and real-time call transcription. For example, we are expanding our partnership with Salesforce to integrate Contact Center AI with Service Cloud Voice to deliver a unified Service Cloud agent console and Customer 360.

“Customers are continually raising their service expectations, and our research tells us 79% of consumers believe the experience a company provides is as important as its products and services,” said Ryan Nichols, SVP & GM, Contact Center, for Salesforce Service Cloud. “Through intelligence, workflows, and a deeper understanding of the customer, Salesforce’s Service Cloud Voice paired with Google’s Contact Center AI will empower agents with a seamless experience to help them wow customers.”

We are also excited to partner with UJET, an innovative and experienced Contact Center as a Service (CCaaS) provider. UJET offers secure user-centric design, scalability, and mobile-focused solution, with turnkey implementation, strong omnichannel capabilities, and best-in-class user experience, making their product a natural fit into Google’s contact center vision. To learn more about the partnership, see here.

Delivering impact for customers

Contact Center AI is already making a difference for our customers such as OneUnited Bank, the largest Black-owned bank in the U.S. “OneUnited Bank has been in partnership with Google Cloud and UJET, as well as a long-standing customer of Salesforce. The expansion and enhancements of Google Cloud’s Contact Center AI, along with its deeper integration with Salesforce, means better return on investment as we drive towards evolving our contact center to deliver exceptional client experiences,” said Teri Williams, President and Chief Operating Officer at OneUnited Bank.

Fitbit, which boasts more than 29 million active users, is also reaping the benefits. “Fitbit relies on Google Cloud and UJET to provide support to our customers with a mobile-first approach. This collaboration, in combination with a strong Salesforce integration, has helped us modernize our entire customer support experience,” stated Cassandra Johnson, VP, Devices & Services Customer Care & Vendor Management Office, at Google.

According to industry analyst Sheila McGee-Smith of McGee-Smith Analytics, “Google Cloud’s Contact Center AI is already a force in the contact center industry thanks to its early focus on AI for customer experience.” She continued, “Through their partnerships with UJET and Salesforce, as well as these expanded capabilities, Google Cloud’s Contact Center AI Platform will help define the future of customer service by powering more secure, engaging, and personalized customer experiences.”

Contact Center AI Platform is supported by a host of integration partners, including Accenture, CDW, Cognizant, Deloitte, HCL, IBM, Infosys, Quantiphi, Tata Consultancy Services, and Wipro. We will also continue to partner closely with the contact center and customer experience (CX) ISVs that our customers already rely on. If you already have a contact center solution provider, you can still integrate Google Cloud’s Contact Center AI into your existing environment.

To learn more about how you can leverage the power of AI to reimagine your contact center experience, visit our Contact Center AI page.

Editor’s note: In this blog post we learn how TapClicks migrated to Google Cloud to offer their marketing customers a unified platform for data management, operations, insights, and analysis.

TapClicks is a smart marketing cloud, powered by data, that unifies our customer’s marketing. By choosing to migrate our core applications last year to Google Cloud, we cut costs, solved data-sharing concerns for our customers, and opened our stack up to a new ecosystem of possibilities. 

The core problem that we’re solving for our customers is how to manage their marketing infrastructures data and operations. Life isn’t easy for marketers now. There are 7,000 different vendors servicing this space today – creating much complexity between digital agencies, media, and brands. Marketers face challenges in navigating all of these systems, logging in and out, understanding pacing goals, and managing the flow of marketing data so they can analyze and report internally as well as to their clients at scale.

We unify omnichannel campaign data (250 API connectors and 6000 Smart Connectors ™ ) from a plethora of marketing sources on an automated data warehousing solution, creating simplicity for organizations. Over 4,000 agencies, media companies, and brands use our Marketing Operations and Data Management Platform, which imports data at scale and creates an automatic data warehouse on Google Cloud. Teams can also leverage TapClicks, like our world class Facebook connector, to import data directly into Google Data Studios.  Beyond importing and storing, we also provide data exporting to other Google solutions like Google Data Studio and Google Sheets.  We also create interactive dashboards that let stakeholders and clients analyze their data, as well as automated, multi-channel reports that go out to clients at specified times. So channel comparisons, optimizations, attribution, and calculations are easily performed.  Some of our customers are able to generate hundreds of thousands of individual reports and dashboards for their clients.

Although we may be best known for our reporting and analytics, we also empower teams managing the marketing operations workflow from customers and internal stakeholders, especially at scale. Our user-friendly, configurable system helps manage their orders and campaigns. Through automation of this process, we deliver tremendous amounts of efficiency, time saving, cost savings, and reduction of errors. The combination of these solutions makes up our unified platform, with additional capabilities like marketing intelligence that offers competitive and brand-level analysis. This is a disruptive solution in use by all leading media companies, agencies and many brands.

Partnering for possibilities

We faced a few challenges with our original tech stack, which included a mix of the leader in web services revenue, leaders in high performance data warehousing, as well as vendors on bare metal servers. 

• One challenge was around costs, which were growing. 
• Second, many of our customers work with multiple brands, and are very hesitant to share their data with the leader in web services, who’s often viewed as their competitor. 
• Third, these vendors are more focused on their own revenue rather than a true long term partnership that would enable their customers to enjoy similar success as they have experienced.

When looking at other cloud providers, Google Cloud emerged for us as the front runner. They were competitive on costs, and their native Kubernetes support was superior— a big selling point for our DevOps team. There’s also a movement in the marketing and advertising industry away from AWS toward Google Cloud because of the data-sharing concern. Finally, most of our customers are already using Google Cloud tools, so there’s brand recognition and familiarity there, and easier integrations with their own systems.

Migrating to Google Cloud

Our migration, which took about five months, involved moving a significant chunk of our infrastructure, including our core applications, using Google Kubernetes Engine (GKE). In our legacy architecture, each of our clients was assigned to one of our virtual machines (VMs), and there was a lot of unused capacity because we had to provision for the max usage. We appreciated GKE’s cloud native capabilities, especially autoscaling, a huge benefit for our web application. We have varying usage patterns during the day, and though our application is mostly used during business hours, there are also days in the month of higher usage, and autoscaling saves us time and costs. GKE also makes deployments much easier, and we anticipate a lot of benefits there for our developer environments. We’ve moved some of our microservices into GKE and plan to move more in the future. All in all, we were able to migrate our core products and the bulk of our AWS spend successfully to Google Cloud. 

We also moved from our other vendors Relational Database Service (RDS) to running MySQL on our own VMs on Google Cloud, which gives us more flexibility in terms of settings and fine tuning. We’re still trying to find the best mix as we’re modernizing our infrastructure, and we took this opportunity to migrate from MySQL 5.7 to 8.0.  

Our next stage is exploring more of the capabilities and services of Google Cloud, including BigQuery, which we’re considering for our own data warehouse. The fact that we could also run Snowflake on Google Cloud, if needed, was another selling point for our migration. 

We’re especially interested in BigQuery ML’s machine learning and natural language processing capabilities, which enabled better predictive insights. Our customers want insights from their campaigns— which are working, which are paying off, where should they invest next? Using our platform, they’re looking not only to generate reporting, but also identify opportunities to improve campaign performance. We plan to use AI and ML to improve those capabilities, so that our customers can seamlessly unlock insight and intelligence from their marketing data and campaigns.

Double-clicking on Google Cloud

For us, being able to deeply leverage and partner with Google Cloud to deliver those solutions on a single stack is critical, and we think our customers will love it. We see TapClicks and Google Cloud partnering at a level beyond what you typically see in a cloud provider relationship. Already, fifty percent of our company is working with various Google Cloud solutions, and we envision TapClicks and Google Cloud as extensions of each other, providing a single, powerful platform solution. 

Google Cloud understands the partnership concept, and their team was able to shine a light on their services and what they could bring to the table. Compared to our previous experiences, dealing with the Google Cloud team has been a true pleasure. Now that we’ve migrated, we’re ready to take our next steps into the services available to us in the Google Cloud ecosystem, and the problems we’ll continue to solve for our customers. Learn more about TapClicks and BigQuery ML.

Apache Spark has become a popular platform as it can serve all of data engineering, data exploration, and machine learning use cases. However, Spark still requires the on-premises way of managing clusters and tuning infrastructure for each job. Also, end to end use cases require Spark to be used along with technologies like TensorFlow, and programming languages like SQL and Python. Today, these operate in silos, with Spark on unstructured data lakes, SQL on data warehouses, and TensorFlow in completely separate machine learning platforms. This increases costs, reduces agility, and makes governance extremely hard; prohibiting enterprises from making insights available to the right users at the right time.

Announcing Spark on Google Cloud, now serverless and integrated

We are excited to announce Spark on Google Cloud, bringing industry’s first autoscaling serverless Spark, seamlessly integrated with the best of Google Cloud and open source tools, so you can effortlessly power ETL, data science, and data analytics use cases at scale. Google Cloud has been running large scale business critical Spark workloads for enterprise customers for 6+ years, using open source Spark in Dataproc. Today, we are furthering our commitment by enabling customers to:

1. Eliminate time spent managing Spark clusters: With serverless Spark, users submit their Spark jobs, and let them do auto-provision, and autoscale to finish.
2. Enable data users of all levels: Connect, analyze, and execute Spark jobs from the interface of users’ choice including BigQuery, Vertex AI or Dataplex, in 2 clicks, without any custom integrations.
3. Retain flexibility of consumption: No one size fits all. Use Spark as serverless, deploy on Google Kubernetes Engine (GKE), or on compute clusters based on the requirements.

With Spark on Google Cloud, we are providing a way for customers to use Spark in a cloud native manner (serverless), and seamlessly with tools used by data engineers, data analysts, and data scientists for their use cases. These tools will help customers on their way to realize the data platform redesign they have embarked on.

“Deutsche Bank is using Spark for a variety of different use cases. Migrating to GCP and adopting Serverless Spark for Dataproc allows us to optimize our resource utilization and reduce manual effort so our engineering teams can focus on delivering data products for our business instead of managing infrastructure. At the same time we can retain the existing code base and knowhow of our engineers, thus boosting adoption and making the migration a seamless experience.”—Balaji Maragalla, Director Big Data Platform, Deutsche Bank

“We see serverless Spark playing a central role in our data strategy. Serverless Spark will provide an efficient, seamless solution for teams that aren’t familiar with big data technology or don’t need to bother with idiosyncrasies of Spark to solve their own processing needs. We’re excited about the serverless aspect of the offering, as well as the seamless integration with BigQuery, Vertex AI, Dataplex and other data services.” —Saral Jain, Director of Engineering, Infrastructure and Data, Snap Inc.

Dataproc Serverless for Spark

Per IDC, developers spend 40% time writing code, and 60% of the time tuning infrastructure and managing clusters. Furthermore, not all Spark developers are infrastructure experts, resulting in higher costs and productivity impact. With serverless Spark, developers can spend all their time on the code and logic. They do not need to manage clusters or tune infrastructure. They submit Spark jobs from their interface of choice, and processing is auto-scaled to match the needs of the job. Furthermore, while Spark users today pay for the time the infrastructure is running, with serverless Spark they only pay for the job duration.

Spark through BigQuery

BigQuery, the leading data warehouse, now provides a unified interface for data analysts to write SQL or PySpark. The code is executed using serverless Spark seamlessly, without the need for infrastructure provisioning. BigQuery has been the pioneer for serverless data warehousing, and now supports serverless Spark for Spark-based analytics.

Spark through Vertex AI

Data scientists no longer need to go through custom integrations to use Spark with their notebooks. Through Vertex AI Workbench, they can connect to Spark with a single click, and do interactive development. With Vertex AI, Spark can easily be used together with other ML frameworks like TensorFlow, Pytorch, Sci-kit learn, and BigQuery ML. All the Google Cloud security, compliance, and IAM are automatically applied across Vertex AI and Spark. Once you are ready to deploy the ML models, the notebook can be executed as a Spark job in Dataproc, and scheduled as part of Vertex AI Pipelines.

Spark through Dataplex

Dataplex is an intelligent data fabric that enables organizations to centrally manage, monitor, and govern their data across data lakes, data warehouses, and data marts with consistent controls, providing access to trusted data and powering analytics at scale. Now, you can use Spark on distributed data natively through Dataplex. Dataplex provides a collaborative analytics interface, with 1-click access to SparkSQL, Notebooks, or PySpark, and the ability to save, share, search notebooks and scripts alongside data.

Flexibility of consumption

We understand one size does not fit all. Spark is available for consumption in 3 different ways based on your specific needs. For customers standardizing on Kubernetes for infrastructure management, run Spark on Google Kubernetes Engine (GKE) to improve resource utilization and simplify infrastructure management. For customers looking for Hadoop style infrastructure management, run Spark on Google Compute Engine (GCE). For customers, who’re looking for no-ops Spark deployment, use serverless Spark! 

ESG Senior Analyst Mike Leone commented, “Google Cloud is making Spark easier to use and more accessible to a wide range of users through a single, integrated platform. The ability to run Spark in a serverless manner, and through BigQuery and Vertex AI will create significant productivity improvement for customers. Further, Google’s focus on security and governance makes this Spark portfolio useful to all enterprises as they continue migrating to the Cloud.”

Getting started

Dataproc Serverless for Spark will be Generally Available within a few weeks. BigQuery and Dataplex integration is in Private Preview. Vertex AI workbench is available in Public Preview, you can get started here. For all capabilities, you can request for Preview access through this form.

You can work with Google Cloud partners to get started as well.

“We are excited to partner with Google Cloud as we look to provide our joint customers with the latest innovations on Spark. We see Spark being used for a variety of analytics and ML use cases. Google is taking Spark a step further by making it serverless, and available through BigQuery, Vertex AI and Dataplex for a wide spectrum of users.” —Sharad Kumar, Cloud First data and AI Lead at Accenture

For more information, visit our website or the watch announcement video and our conversation with Snap at Next 2021.