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Swarovski’s Journey towards Online and Offline Conversion with Predictive Analytics
Luxury brand and leader in crystals and glass production, Swarovski has charmed customers with its exquisite collections for over 125 years. To understand their customers better and map their online behaviors, Swarovski had to overcome prediction hurdles as majority of the purchases are not frequent or habitual. They are mostly impulse buys or have no rational behind the purchase in order for the brand to accurately map customers’ interest and delight them with relevant personalization or website customization strategy.
Swarovski used a machine learning (ML) model to predict the most performing SKUs and list of products based on both online and offline indicators to target buyers. A score was assigned to each product in the list and was personalized at the country level that delivered relevant insights. Swarovski is aiming to expand the product listing page to personalize at customer level. Watch the video to dive deep into Swarovski’s data analytics efforts to answer complex questions, reporting and prediction using both online and offline data.
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Hospitals Can Offer Interconnected Patient Experiences Using Google’s Natural Language Services
Machine Learning (ML) in healthcare helps extract data from conversations, medical records, forms, research reports, insurance claims and other documents across the care value-chain to help care providers have a holistic view of their patients to draw insights for diagnoses and treatments. With Natural Language Processing(NLP), healthcare organizations can program computers and systems to process and analyse large volumes of human communication in form of spoken texts, written documentation and utterances. Watch the video to learn how the healthcare community can leverage Google’s NLP services to process structured and unstructured data to offer interconnected experiences for patients.

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Contact centers can transform customer experience using AI-driven speech analytics to evaluate every customer interaction and use it as a ‘data point’ to identify key patterns, enquiries, pain points, reviews and feedback to enhance customer experience with real-time, personalized recommendations. Knowlarity’s AI-based cloud telephony solutions for businesses built with Google Cloud takes speech analytics to another level!
Download the article to empower your contact centers with AI-powered speech analytics to make predictive analysis, reduce call handling time and volume as well as train contact center agents to provide customers with quick and real-time feedback.
Contact Center AI & Automation Anywhere Help Virtual Agents Deliver Next Level CX

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With the advent of the pandemic, contact center traffic has increased by as much as 300%, taxing center capabilities. To help handle the surge, and keep up with heightened customer demands, many customer experience providers have deployed automation in the form of virtual agents that serve as the first—and, sometimes the last—line of customer support. How effective these virtual agents are in delivering timely, complete customer service depends in large part on the contact center’s infrastructure and the power of the automation solution.
Customer support providers can now start to reimagine customer experience with Conversational AI and take advantage of the Google Cloud-Automation Anywhere partnership to modernize their operations.
Bringing together the Google Cloud Contact Center AI (CCAI) solution and the Automation Anywhere cloud-native Automation 360 platform, the partnership helps contact centers remain competitive by improving their performance metrics and customer satisfaction to exceed the continuously growing expectations.
Information here, there, anywhere—with limited access
Over the years, many contact centers have accumulated a multitude of systems, often creating a disconnected infrastructure. As a result, both human and virtual agents alike are challenged to keep up with growing customers’ expectations for timely, accurate, and complete service. Without up-to-date software and infrastructure, human agents have to perform a “swivel-chair” maneuver, logging in to the different systems, sifting through records, copying the needed information, and deciding what the next action should be. This approach is not conducive to achieving lower average handle times (or AHT), reduced processing errors, or increased customer satisfaction.
In such a siloed environment, virtual agents may not connect to all the relevant data systems and applications. That also limits what they can do to support human agents. Typically, a virtual agent can handle basic customer requests, such as providing banking customers with their account balances. For more complex requests, such as applying for a line of credit, a virtual agent still must transfer customers to human agents, and the swivel-chair maneuver begins.
Enter the RPA-assisted AI-powered virtual agent
The combination of Google Cloud Contact Center AI and Automation Anywhere’s Automation 360 RPA platform can help contact centers get the maximum benefit from utilizing virtual agents, enriching customer engagements. Further, by integrating automation, opening up APIs, and creating new processes for virtual agents, customer experience teams can help streamline operations by enabling users to quickly access the information they require. This helps minimize the need for the “swivel-chair” maneuver.
Automation 360 makes it possible for the CCAI virtual agents to access all systems and applications in both legacy and modern infrastructure, helping resolve every case quickly and easily. Not only can the virtual agents respond faster with answers, but they can complete more complex end-to-end requests. With RPA, customers are reporting 66% improved efficiency of contact center operations while exceeding their AHT reduction goals.
This video illustrates how such automation can dramatically reduce the processing time of a customer service request, and you can read more about the details of contact center automation, as well.
Additionally, a comprehensive development platform, the Google Cloud CCAI Dialogflow, powers virtual agents—chatbots and voicebots—with Conversational AI to deliver lifelike customer experiences anytime a customer reaches out to a brand. CCAI Agent Assist helps human agents with turn-by-turn guidance, ready-to-deliver answers, and ready-to-send responses. CCAI Insights identifies key metrics such as call drivers and customer sentiment for workflow optimization.
Living up to their potential
TELUS International, a leading digital customer experience provider and long-time partner of both Google Cloud and Automation Anywhere, has been leveraging virtual agents to enhance the employee and customer experience.
“We are very proud of the enormous value that we can provide our clients through combining our expertise in customer experience and digital transformation alongside the innovative solutions of our technology partners,” Jim Radzicki, CTO of TELUS International, explains. “For instance, through leveraging Google Cloud Contact Center AI and Automation Anywhere’s RPA integration, we are able to expand the capabilities of virtual agents to process a wider variety of customer requests while allowing our team members to focus on the most critical conversations and creating a meaningful connection with every customer.”
With CCAI and Automation 360, virtual agents can help contact centers deliver 24/7, comprehensive, accurate service, all of which helps eliminate wait times—even with heavy traffic. And this is just the start.
With deeper integration planned between Automation 360 and CCAI, RPA can augment CCAI Agent Assist’s abilities to help human agents by bringing untapped case-sensitive information to their fingertips. Furthermore, Automation 360 Bot Insight can complement CCAI Insights’ as well as TELUS International’s Intelligent Insights, a tool-agnostic platform to monitor and manage RPA solutions and bots, with backend data access metrics.
At Google Cloud and Automation Anywhere, we’ll continue developing our contact center solution to extend automation capabilities for better customer service and greater customer satisfaction. Instead of constantly swiveling, agents can once again be at the center of the call center action, taking their work—and the experience of their customers—to the next level.
This Chart, from Home Depot, Dramatically Demonstrates the Power of a Cloud Data Warehouse

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The Home Depot (THD) is the world’s largest home-improvement chain, growing to more than 2,200 stores and 700,000 products in four decades. Much of that success was driven through the analysis of data. This included developing sales forecasts, replenishing inventory through the supply chain network, and providing timely performance scorecards.
However, to compete in today’s business world, THD has taken this data-driven approach to an entirely new level of success on Google Cloud, providing capabilities not practical on legacy technologies.
The pressures of contemporary growth that drove much of the work are familiar to many businesses. In addition to everything it was doing, THD needed to better integrate the complexities in its related businesses, like tool rental and home services. It needed to better empower teams, including a fast-growing data analysis staff and store associates with mobile computing devices. It wanted to better use online commerce and artificial intelligence to meet customer needs, while maintaining better security.
Even before addressing these new challenges, THD’s existing on-premises data warehouse was under stress as more data was required for analytics and data analysts were utilizing the data with increasingly complex use cases. This drove rapid growth of the data warehouse, but also created constant challenges for the team in managing priorities, performance, and cost.
In order to add capacity to the environment, it was a major planning, architecture, and testing effort. In one case, adding on-premises capacity took six months of planning and a three-day service outage. Within a year, capacity was again scarce, impacting performance and ability to execute all the reporting and analytics workloads required. The capacity refresh cycles were shrinking, and the expecations for data were growing. There had to be a better way.
Still, THD did not take its move to the cloud lightly. A large-scale enterprise data warehouse migration involves tremendous effort among people, process, and technology. After careful consideration, THD chose Google Cloud’s BigQuery for its cloud enterprise data warehouse.
BigQuery, a scalable serverless data warehouse, was better on cost, infrastructure agility, and analytics capability, driving better insights with improved performance. There are no service interruptions when capacity is added, and that capacity can be added within a week (and soon same day). It doesn’t require complex system administration, and its standard SQL support means people can easily ramp up quickly. Valuable BigQuery products like Identity and Access Management meant THD could create many separate Google Cloud projects, while ensuring that different teams weren’t interfering with each other or accessing protected data.
THD also utilizes BigQuery’s flat-rate monthly pricing model that allows teams to budget their capacity based on need and provides billing predictability. The capacity not being used by a given project is available for enterprise use. This ensures no surprises when the monthly bill arrives and provides all analytical users access to significant computing power.
While THD’s legacy data warehouse contained 450 terabytes of data, the BigQuery enterprise data warehouse has over 15 petabytes. That means better decision-making by utilizing new datasets like website clickstream data and by analyzing additional years of data.
As for performance, look at this chart:
With the cloud EDW migration complete, and the legacy on-premises data warehouse retired, analysts now execute more complex and demanding workloads that they would not have been able to complete before, such as utilizing Datalab for orchestrating analytics through Python Notebooks, utilizing BigQuery ML for machine learning directly against the BigQuery data (no movement of large datasets), and AutoML to help determine the best model for predictions.
Additionally, engineers at THD have adapted BigQuery to monitor, analyze, and act on application performance data across all its stores and warehouses in real time, something that was not practical in the on-premises system.
With over 600 projects that THD now has on Google Cloud, the BigQuery story is just one of the many ways that Google Cloud is working with THD to deliver meaningful business results, every day.
How Google Cloud’s PSO Supports Customers’ Migration Goals

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Google Cloud’s Professional Services Organization (PSO) engages with customers to ensure effective and efficient operations in the cloud, from the time they begin considering how cloud can help them overcome their operational, business or technical challenges, to the time they’re looking to optimize their cloud workloads.
We know that all parts of the cloud journey are important and can be complex. In this blog post, we want to focus specifically on the migration process and how PSO engages in a myriad of activities to ensure a successful migration.
As a team of trusted technical advisors, PSO will approach migrations in three phases:
- Pre-Migration Planning
- Cutover Activities
- Post-Migration Operations
While this post will not cover in detail all of the steps required for a migration, it will focus on how PSO engages in specific activities to meet customer objectives, manage risk, and deliver value. We will discuss the assets, processes and tools that we leverage to ensure success.
Pre-Migration Planning
Assess Scope
Before the migration happens, you will need to understand and clarify the future state that you’re working towards. From a logistical perspective, PSO will be helping you with capacity planning to ensure sufficient resources are available for your envisioned future state.
While migration into the cloud does allow you to eliminate many of the considerations for the physical, logistical, and financial concerns of traditional data centers and co-locations, it does not remove the need for active management of quotas, preparation for large migrations, and forecasting. PSO will help you forecast your needs in advance and work with the capacity team to adjust quotas, manage resources, and ensure availability.
Once the future state has been determined, PSO will also work with the product teams to determine any gaps in functionality. PSO captures feature requests across Google Cloud services and makes sure they are understood, logged, tracked, and prioritized appropriately with the relevant product teams. From there, they work closely with the customer to determine any interim workarounds that can be leveraged while waiting for the feature to land, as well as providing updates on the upcoming roadmap.
Develop Migration Approach and Tooling
Within Google Cloud, we have a library of assets and tools we use to assist in the migration process. We have seen these assets help us successfully complete migrations for other customers efficiently and effectively.
Based on the scoping requirements and tooling available to assist in the migration, PSO will help recommend a migration approach. We understand that enterprises have specific needs; differing levels of complexity and scale; regulatory, operational, or organization challenges that will need to be factored into the migration. PSO will help customers think through the different migration options and how all of the considerations will play out.
PSO will work with the customer team to determine the best migration approach for moving servers from on-prem to Google Cloud. PSO will walk customers through different migration approaches, such as refactoring, lift-shift, or new installs. From there, the customer can determine the best fit for their migration. PSO will provide guidance on best practices and use cases from other customers with similar use cases.
Google offers a variety of cloud native tools that can assist with asset discovery, the migration itself, and post-migration optimization. PSO, as one example, will help work with project managers to determine the best tooling that accommodates the customer’s requirements for migrating servers. PSO will also engage Google product team to ensure the customer fully understands the capabilities of each tool and the best fit for the use case. Google understands from a tooling perspective, one size does not fit all, thus PSO will work with the customer on determining the best migration approach and tooling for different requirements.
Cutover Activities
Once all of the planning activities have been completed, PSO will assist in making sure the cutover is successful.
During and leading up to critical customer events, PSO can provide proactive event management services which deliver increased support and readiness for key workloads. Beyond having a solid architecture and infrastructure on the platform, support for this infrastructure is essential and TAMs will help ensure that there are additional resources to support and unblock the customer where challenges arise.
As part of event management activities, PSO liaises with the Google Cloud Support Organization to ensure quick remediation and high resilience for situations where challenges arise. A war room is usually created to facilitate quick communication about the critical activities and roadblocks that arise. These war rooms can give customers a direct line to the support and engineering teams that will triage and resolve their issues.
Post-Migration Activities
Once cutover is complete, PSO will continue to provide support in areas such incident management, capacity planning, continuous operational support, and optimization to ensure the customer is successful from start to finish.
PSO will serve as the liaison between the customer and Google engineers. If support cases need to be escalated, PSO will ensure the appropriate parties are involved and work to get the case resolved in a timely manner. Through operational rigor, PSO will work with the customer in determining if certain Google Cloud services will be beneficial to the customer objectives. If services will add value to the customer, PSO will help enable the services so it aligns with the customer’s goal and current cloud architecture. In cases where there are missing gaps in services, PSO will proactively work with the customer and Google engineering teams to close the gaps by enabling additional functionality in the services.
PSO will continue to work with the engineering teams to consistently review and provide recommendations on the customer’s cloud architecture in ensuring the most optimal and cost efficient design along with adhering to Google’s best practices guidelines.
Aside from migrations, PSO is also responsible for providing continuous training of Google Cloud to customers. To ensure consistent development of Google Cloud, PSO will work with the customer to jointly develop a learning roadmap to ensure the customer has the necessary skills to succeed in delivering successful projects in Google Cloud.
Conclusion
Google PSO will be actively engaged throughout the customer’s cloud journey to ensure the necessary guidance, methodology, and tools are presented to the customer. PSO will engage in a series of activities from pre-migration planning to post migration in key areas such as capacity planning to ensure sufficient resources are allocated for future workloads to providing support on technical cases for troubleshooting. PSO will serve as a long-term trusted advisor who will be the voice of the customer and provide the reliability and stability of the customer’s Google Cloud environment.
Click here if you’d like to engage with our PSO team on your migration. Or, you can also get started with a free discovery and assessment of your current IT landscape.
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