Transforming the Contact Center Experience with Artificial Intelligence
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We meet daily with contact center owners and customer experience (CX) execs across all industries, geographies, and business sizes. Looking back at these conversations, it’s crystal clear that 2022 was a high-stakes year for call centers, with three primary challenges trending across all customers and continuing in 2023:
- Many organizations feel pressure to rapidly scale up their call center operations in response to macroeconomic changes. Uncertain conditions are forcing Contact Centers to be ever more cost-effective, and to find ways to generate revenue for the business.
- End users are increasingly demanding and less forgiving when it comes to CX. Users have a choice, and they expect brands to meet them where they are with superior experiences. Connecting with customers where they engage is one of the key components of superior CX—customers should not have to go through elaborate processes or unhelpful phone trees to get help but should rather have service available quickly and easily in their preferred channels. Consumers demand more intimate ways of connecting with brands and Conversational AI can create that critical interaction medium.
- Organizations understand that AI can help address these challenges. However, many business leaders remain unsure how to successfully make the journey. A growing number of offerings are on the market, but many don’t deliver on their promise, with long and expensive integration requirements and unpredictable and underwhelming outcomes.
Helping our customers successfully address these challenges and opportunities was one of our top priorities last year and will continue to be a significant focus in coming months. In this blog post, we’ll review our Contact Center AI (CCAI) news from last year, as a primer for 2023.
Looking back: Why 2022 was a big year for Contact Center AI
In 2022, we increased our strategic investment in CCAI, including expanding it to include a comprehensive, end-to-end contact center solution suite that is user-first, AI-first, and cloud-first. We launched Contact Center AI Platform, our Contact Center as a Service (CCaaS) offering, as part of the CCAI product suite that offers a modern, turnkey solution, designed with user-first, AI-first, and cloud-first design. During Google Cloud Next ‘22, we shared lots of great content on how organizations can use CCAI to improve customer experiences, including these breakout sessions:
- Delight customers in every interaction with Contact Center AI
- Power new voice enabled interfaces with applications with Google Cloud’s speech solutions
We also got a chance to hear how customers are using CCAI to better reach their own customers, including Wells Fargo and TIAA. We partnered with CDW to discuss Providing Better Customer Experiences and with Quantiphi in a webinar called “Elevating the Banking Experience with CCAI Platform.” Just recently, our customer Segra shared their success story.
Through these customer interactions, three key priorities have surfaced as we look forward to 2023: Elevate the customer experience, bring new forms of AI to drive new automation and accelerate time to value.
Looking forward: Elevate CX, integrate new forms of AI, accelerate time to value
1. User-first: Meet them where they are with elevated Customer Experience.
As we have learned, users expect that brands meet them where they are and on their own terms and expectations. To do that, brands must integrate with and adopt the latest user-centric technologies and product best practices from consumer mobile and web apps. Enterprise B2C can’t exist anymore in a parallel world of different and often inferior user experience. Google has over 20 years of experience in building such consumer experiences, with multiple products successfully serving billions of users. Bringing these capabilities and experiences from our consumer products and research teams to our cloud offerings was a key component for our product offerings in 2022 and is a big part of our key investments in 2023. Moreover, a vast majority of CX user journeys start with a query on Google Search or YouTube. Connecting with the users at that point, even before they reach out directly to the contact center is a win-win, saving money for the brand and delivering immediate value to the user. By focusing on the user we created a superior integrated omnichannel experience.
2. AI-first and cloud-first: Quality contact center growth depends on transforming to modern, Cloud, AI solutions.
For contact centers to evolve, they need to transform from cost centers to revenue generators. That requires modern Cloud and AI solutions. Conversational data spans across all parts of the contact center, opening new ways to generate value. Cloud capabilities of privacy, security and scale can enable personalized CX across channels, enabling key omnichannel experiences. From a study by McKinsey: “Cross-channel integration and migration issues continue to hamper progress. For example, 77 percent of survey respondents report that their organizations have built digital platforms, but only 10 percent report that those platforms are fully scaled and adopted by customers. Only 12 percent of digital platforms are highly integrated, and, for most organizations, only 20 percent of digital contacts are unassisted.” Traditional telephony technologies are becoming commoditized and struggle to keep up with ever more complex rule based systems. Leaders in applicative AI and Cloud technology are stepping up as the new partners for brands who understand they need to take the leap to the next generation CX solutions. .
3. Accelerating time to value while future proofing investments with predictable and measurable value
Reducing upfront implementation investment and accelerating time to value can be a challenge for contact center solutions. Scaling Cloud and AI can provide a faster path advanced conversational AI, can help address these challenges. Let’s look at three examples:
- Out of the Box(OOTB) integrated transcription, chat and voice summarization, and topic modeling — This saves customers money by reducing agent handling time for every chat and call, as well as providing valuable insights that can be used for quality management, contact center optimization and automation, agent and user churn prediction, business insights, and revenue opportunities.
- AI based chat and voice calls steering paired with info-seeking virtual agents — Together these deliver higher Customer Satisfaction at scale while reducing cost – by significantly reducing waiting queues and being routed to the wrong agent, as well as automating away total handling time.
- Reduced time to full automation — Reduce the complexity of conversation modeling, prebuilt components and APIs for shorter time to value and more predictable outcomes, and metrics driven ML-Dev & QA tools and playbooks.
With these new capabilities, our customers can now see results as soon as they implement CCAI. We’re excited to get our customers to where they want to be faster!
And there you have it: a quick overview of CCAI and its progress in 2022 and what’s coming in 2023. For more details, check out the documentation or our CCAI solutions page.
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Since 1994, IDOM, Japan’s leading buyer and retailer of used cars, has enjoyed success in the auto industry with a simple yet traditional business model: buy pre-owned vehicles directly from car owners and auction them to third-party dealers, or sell them to other consumers at retail stores.
In an increasingly frugal economy, Japanese consumers are buying fewer new cars. Most young urban workers take public transport, a cheap alternative for getting from point A to point B. Additionally, people who do own cars are keeping them longer: the average period of ownership is 7.5 to 10 years.
Although Japanese consumers are buying fewer new cars, used car sales are steadily on the uptick. Pre-owned car sales in Japan rose by 1.7% in 2015—the first big spike in three years. IDOM dominates this industry with about 40% market share, and it wanted to continue to take advantage of this growing market trend.
To do so, IDOM reinvented its marketing strategy, using Google’s machine-learning technology to make full use of its available customer data. The brand’s main goal was to attract more prospective car sellers to its physical stores because (1) that’s where they could close trade-in deals and (2) sourcing used cars efficiently is integral to the success of its business model.
Secondly, rather than measure marketing success solely on clicks, views, brand awareness, or favorability, IDOM relied on data to determine which advertising techniques—including phone calls and customized ads to prospective sellers—turned a real profit.
After successfully identifying and targeting existing car owners with a high chance of selling their car, it was only natural for IDOM to leverage this approach to identify and target potential customers with a higher chance of buying a car—key for the other side of its business as well. Thus, IDOM also showed customized ads to potential car buyers and prioritized follow-up phone calls to high-value potential car buyers.
Find out how IDOM increased the number of sellers and buyers visiting its stores by a whopping 25% and grew gross profits by 300% in a key market segment. Download now!
Spark on Google Cloud: How this Helps Customers with Agility, Cost Reduction and Time Spent on Spark
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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:
- Eliminate time spent managing Spark clusters: With serverless Spark, users submit their Spark jobs, and let them do auto-provision, and autoscale to finish.
- 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.
- 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.
SystemsResearch@Google (SRG) to Revamp the Future of Hyperscaler Systems
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For over two decades, Google has helped lead the invention of modern cloud systems—defining, designing and deploying warehouse-scale computing as the foundation for reliable, performant, and secure global-scale information services delivered to billions of users around the world. This leadership involves significant innovation across a broad range of systems technologies, including distributed systems, storage systems, databases, analytics, operating systems, wide area and data center networking, cluster computing, ML, video acceleration and more.
Today, we are announcing a significant step in continuing Google’s tradition of innovation and charting its path into the future: the formation of SystemsResearch@Google (SRG). SRG will be a new research team, positioned in the heart of Google’s Cloud and Infrastructure engineering organization, with the mission of shaping the future of hyperscaler systems design for Google and its ecosystem. It is focused on inventing, incubating, and infusing new concepts, designs, and technologies into Google’s applications, systems, and data centers. The team’s position will allow seamless engagement with engineering and product teams, enabling joint exploration in concert with transformative workloads. Beyond Google, the SRG team will look to forge strong relationships with external research communities working on the most pressing systems-research problems.
Critical research at a pivotal time
We are at a time of enormous transition and opportunity, as nearly all large-scale computing is moving to cloud infrastructure, classical technology trends are hitting limits, new programming paradigms and usage patterns are taking hold, and most levels of systems design are being restructured. We are seeing wholesale change with the introduction of new applications around ML training and real-time inference to massive-scale data analytics and processing workloads fed by globally connected edge and cellular devices. This is all happening while the performance and efficiency gains we’ve relied on for decades are slowing dramatically from generation to generation. And while reliability is more important than ever as we deploy societally-critical infrastructure, we are challenged by increasing hardware entropy as underlying components approach angstrom scale manufacturing processes and trillions of transistors.
In the last twenty years, much of the world’s population has gained real-time access to the world’s information and to one another in ways that were previously the stuff of science fiction. The next decade will see computing and associated capabilities undergo an even more profound transformation, bringing real-time insights, sensing, and actuation to trillions of network-connected devices spanning all of the world’s population. Doing so will require fundamental advances in security, reliability, programming models, data analysis, systems for machine learning, networking, storage systems, hardware architecture, and software systems.
SystemsResearch@Google will be co-led by David Culler and Hank Levy, who bring a combination of academic and industrial experience, plus a long history of successful and impactful research in computer systems. Culler is the former Chair of EECS at UC Berkeley, where he worked to create the Division of Data Sciences and became its founding Dean. His research has focused on parallel architectures, clusters, embedded wireless networks, planetary-scale internet services, and sustainability design. He was the founding faculty director of Intel Research Berkeley, co-founded two startups, and worked with Sun Microsystems for a decade. Levy is the former Chair of Computer Science & Engineering at University of Washington, where he worked to create the Paul G. Allen School and became its founding Director. His research has focused on operating systems, distributed systems, computer architecture, and hardware multithreading. Before UW, Levy spent a decade at Digital Equipment Corporation (DEC), where he worked on operating systems and early-generation clustered computer systems; he has also co-founded two startups. Culler and Levy are both Members of the National Academy of Engineering and Fellows of the IEEE and the ACM.
SRG will be located across sites in Google’s Bay Area and Seattle facilities. We are currently building the SRG team, bringing together leading networked systems thinkers from around the world and inside Google. If you are interested in learning more please reach out to us at systemsresearch@google.com.
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An Indian Example of How to Really Up Your Customer Experience Game and Increase Conversion Rates With AI
How about selfie analysis of users to recommend them the right lipstick color?
That’s just one of the many ideas folks at Purplle.com came up with to improve the buying experience of Indian consumers.
And without the power of Google Cloud, it would probably have remained just that…an idea.
But today, thanks to Google Cloud, “Nothing seems impossible,” says Suyash Katyayani, CTO, Purplle.
Purplle.com is an online e-commerce company in India and one of the pioneers in creating a digitally-native beauty brands in India.
“The beauty industry is so data intensive that we needed to have a strong data strategy and we were looking out for solutions which would enable us to have a strong data pipeline and a strong data warehousing solution,” says Katyayani.
That’s when it turned to Google Cloud.
Additionally, Purplle.com, says Katyayani, does not have to worry about at what scale the company operates at because they have access to state-of-the-art infrastructure from Google Cloud available to them so that their developers can run experiments.
“The biggest plus point for us has been the agility that Google Cloud has added,” says Katyayani.
Taking Maps Further: New Website Experience for Product Discovery, Budgeting and Access to Dev Documentation
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For more than 15 years, developers have used Google Maps Platform to deliver location-based experiences to their end users and used location intelligence to optimize their businesses. Along this journey, we’ve made a variety of changes to better support our community as needs have changed and new industries and technologies have emerged. We started rolling out a new website experience, at https://mapsplatform.google.com, to help you better understand the products and solutions best suited to address your objectives. Plus, now you can directly connect to the developer documentation for each product to get started quickly, and you can visualize usage and associated costs to have a better idea of what to expect before getting started.
Getting to your solution faster
Maps, Routes, Places are building blocks that let you develop implementations for any use case. Building for specific use cases, however, typically requires using a combination of APIs and SDKs. To help you quickly understand what’s possible and what you need to build for your use case, you can now visit the solutions tab to select from a list of popular use cases or industries. Once you’ve selected a use case or industry, you’re taken to a page where you can explore relevant products, read helpful blog posts, see how other customers have deployed for similar use cases, and more.
Direct access to developer documentation
Did you know there are more than a thousand pages of developer documentation created to help you get started, unblock you when you’re stuck, and share best practices? Now when you explore a product or solution from the Google Maps Platform website, you can easily navigate back and forth between our website and documentation. Just tap on JS, iOS, Android or API under the product name to get to the documentation you need.
Budgeting for your project
To help you calculate pricing for your project, we’ve introduced a new pricing calculator. Once you find the product and API or SDK you plan to use, pull the slider to reflect your estimated number of monthly requests. This will automatically update the “monthly cost” column for each product and API or SDK you plan to use. If your estimated monthly requests exceed the slider limit, contact our sales team to learn about volume discounts that start at 20% off.
We hope our new website makes it easier to discover our products and solutions, estimate your budget, and start building with our documentation so you can deliver helpful experiences to your users and optimize your business.
For more information on Google Maps Platform, visit https://mapsplatform.google.com.
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