Accuracy and Real-time Updates with Google Maps’ On-Demand Rides and Delivery Solution Impacts CX
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Last year we launched our on-demand rides and delivery solution to help businesses improve operations as well as transform the driver and customer journey from booking to arrival or delivery. When it comes to on-demand rides and deliveries, every minute matters. When users book a ride or order food, they want a seamless experience and real-time accurate updates. Today, we’re taking a closer look into data quality improvements for location, time and distance accuracy, and motorbike routes.
Machine learning helps drive location accuracy
Location accuracy stands at the base of our customers’ operation. The location signals that are coming from mobile devices can sometimes be off for various reasons and a driver’s location can get stuck, or jump around.
Recently we developed mechanisms in our fleet management product that can take in multiple location signals and determine the most reliable location to use for a given vehicle. With that, we noticed drastic improvements:
- Eliminated long periods of location ‘stuckness’ almost completely; a vehicle is considered ‘stuck’ when we think it’s moving but the measured location is not
- Reduced the jumpiness of the location signal by 52%-86%: ‘jumpiness’ is when a vehicle shows a sudden and usually drastic change in location. A jump is determined to exist when the speed the vehicle had to go at in order to cover the distance it did is unrealistic
- Reduced the average jump distance by 44%-86% : ‘jump distance’ is the distance between two consecutive location pings when we determined a ‘location jump’ has occurred.
Dunzo, a local e-commerce platform in India, explains how integrating the order tracking capability within Google Maps Platform’s On Demand Rides and Delivery solution has helped reduce support calls by 90%. The out-of-the-box solution helped Dunzo’s motorbike delivery partners with updating location sync to reduce stuckness and jumpiness as well as deliver premium user experiences.
When the vehicle location is more reliable, the dispatch decision is of higher quality, meaning there is a higher chance you will be able to make the optimal decision. This can lead to less wait time for consumers, increased driver happiness and fewer cancellations.
Improvements in ETA accuracy in motorbike routes
In many geographic areas, road space is limited and car ownership is prohibitively expensive so motorbike is a prominent transportation mode. Motorbike mapping requires unique routing, ETA models, and navigation capabilities. Improvements in motorbike routing and ETA estimation have enabled customers like Gojek to offer better overall services, even in geographies with poor wifi or missing roads.
Recently, we further improved ETA outcomes by developing new machine learning models trained specifically for motorbikes. These models help our systems account for differences in congestion and traffic flow that arise in different regions and scenarios.
Globally, we measured an ~8% improvement in ETA accuracy for riders in the general public, and a ~6% improvement in on-demand rides and deliveries ETA accuracy. In this on-demand economy, where consumers are accustomed to real-time trip and order progress, these improvements will significantly improve their experience.
We will continue to innovate on both our car and motorbike on-demand rides and deliveries capabilities based on customer demand and requirements. We are committed to the success of our customers by building a seamless experience for all parties—consumers, drivers, and fleet operators—in the rides and deliveries journey.
For more information on Google Maps Platform, visit our website.
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Bra Fit or Brad Pitt? Fun Moments of Using AI for Contact Centers
For many enterprises, the wish to tap into the power of AI is negated only by a lack of know-how: How AI works, what sort and how much data they will need to gather and structure correctly, and how to use the platforms that make AI adoption easier.
Google understands that. Which is why the AI solutions they have built “are basically plug and play, ready to go with our partners, so that you (businesses) can have immediate business value for your specific use case, and for your specific workflow without a deep investment of any kind into machine learning,” says Levent Besik, Group Product Manager, Google Cloud.
Among the more interesting use cases that’s seeing adoption is contact center AI.
“So one thing that we see quite often, especially from our B2C customers, is that they often have this growing pain in their call centers. They face a trade-off between operational efficiency and great customer service. With the advances in language and conversational AI, that doesn’t have to be the case anymore,” says Besik.
That’s exactly the problem in front of Akash Parmar, Enterprise Architect,. “One of the big challenge we had was: how do we effectively manage 14 million calls, which come into our contact center, stores and head office? In the past, these calls were managed by completely different platforms with their own IVRs, with their own routing and reporting solutions. It was expensive, plus the experience across them was very inconsistent.”
Digging deeper, Parmar and team figured that the challenge was in the fact that an IVR couldn’t really capture the hundreds of reasons customers call.
To get around the problem Parmar and team decided to let customer tell them why they were calling. They then used AI to decipher what the customer was saying, extract the customer’s intent from that, and then help them directly, or re-route them to the best department.
Overall, it proved to be a big success, says Parmer. Although there were funny moments.
“In our early days, we were getting some transcriptions from the Speech API, which were not what we expected. It was not word by word and we had to kind of train the model to make sense of it. For example, we started to get calls about Brad Pitt. We were like, we’ve really won the Oscar here. Brad Pitt is calling us. It was not actually call for Brad Pitt. These were calls about bra fit, which it’s a big business for M&S,” remembers Parmer.
Find out how three companies, including Marks and Spencer, are using AI today.
Manipal Group: Delivering High-Quality Patient Care with Google Cloud
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One of India’s best-known healthcare brands, Manipal Group prides itself on clinical excellence and a patient-centric approach. From humble beginnings in 1953 as a single teaching hospital—the Kasturba Medical College—in a university town in Karnataka, India, Manipal Group has grown to a presence in seven cities in India and operations in Malaysia and Nigeria. “Our founder, Dr T. M. A. Pai, established Kasturba Medical College just six years after India gained independence,” says C. G. Muthana, Chief Operating Officer, Teaching Hospitals, Manipal Health Enterprises Pvt. Ltd, part of Manipal Group.
Manipal Health Enterprises Pvt. Ltd operates 11 corporate, or for-profit, hospitals and five teaching hospitals. “We have about 7,000 beds India-wide, and on that measure, we are the third-largest private-sector healthcare provider in India today,” says Muthana. The organization now employs about 6,000 people in its corporate hospitals and 5,000 people in its teaching hospitals.
Manipal Health Enterprises Pvt. Ltd acquires and operates world-class medical technologies in its teaching and corporate hospitals. However, with 75% of corporate hospital wards allocated to fee-paying private patients—compared to just 25% of the wards in teaching hospitals—the differences in information technology budgets are significant. “In the general wards that comprise most of the wards in teaching hospitals, patients are typically treated for free or at heavily subsidized rates,” explains Muthana. “So revenues and costs of delivery vary between hospitals, while employee costs remain similar.”
Rostering nurses a critical task
Rostering nurses to work shifts is one of the most important tasks at Manipal Group corporate and teaching hospitals. As at all hospitals, nurses administer medications, monitor patients, maintain records, manage intravenous lines, and work with doctors to heal patients. They can also provide advice and support to patients and loved ones, including teaching them how to administer medication outside a hospital setting. If too few nurses are rostered for a particular shift, the quality of patient care may suffer.
Google Cloud results
- Enabled the hospital to correctly size its nursing workforce
- Lowers stress on nurses by delivering more equitable rostering
- Presents opportunity to better manage nurses’ leave and other administration tasks
However, rostering at the group’s hospitals was a time-consuming exercise. Senior nurses on each ward would have to spend up to 45 minutes per day manually amending paper-based rosters to accommodate requested changes to duty shifts for personal or other circumstances. The organization began receiving complaints from patients, doctors, and other hospital staff that, on occasion, too few nurses were rostered on for certain shifts—particularly at its flagship hospital in Bangalore. “We had based our rostering calculations on the number of beds occupied by patients and, when we investigated, we found at a macro level, we were rostering on the correct number of nurses,” says Muthana. “However, on some days, on wards optimally staffed by, say, 10 nurses per shift, we might have 14 nurses rostered on for one shift and seven rostered on for another shift. Those times we had seven, we had a clear shortage.”
In 2012, Muthana asked a consultant to develop algorithms to help automate the rostering. “Unfortunately, there were so many variables, the consultant failed to solve the problem,” he says. The Chief Operating Officer’s next step was to ask the founder and Chief Executive Officer of predictive analytics business Retigence Technologies—already working with the business on a materials management project—to develop an application to manage rostering.
Removing the daily drudgery
“Our plan with the automation project was to relieve our senior nurses of the daily drudgery of amending the rosters and to deliver rosters that were as fair as possible to all our staff,” says Muthana. “We also saw an opportunity to reduce our costs by reducing the overall number of nurses needed to look after our patients.”
Retigence Technologies’ team members then worked with the group’s nurses to capture the variables and requirements for the project. For example, a minimum number of nurses with one year experience or more needs to be rostered on for each shift. Retigence Technologies then started building an application using compute resources available through Compute Engine, a Google Cloud product. “We selected Google primarily because of its pioneering work in artificial intelligence (AI) and machine learning,” says Srinibas Behera, founder and Chief Executive Officer of Retigence Technologies.
With the nurses rostering application developed, Manipal Health Enterprises Pvt. Ltd undertook several pilots to build user acceptance. “Some of our senior nurses took time to accept the fact automation removed their control over rostering assignments, but were finally convinced by the better transparency the product offered,” says Muthana. The organization deployed the application to a smaller hospital and secured user support before rolling out the application to its Bangalore flagship.
Eliminating stress
Deploying the application has enabled Manipal Health Enterprises Pvt. Ltd to remove a buffer of about 100 nurses retained to accommodate the variations in number of nurses rostered for individual shifts. “The savings on those salaries more than paid for the cost of developing the application,” says Muthana.
The application also enabled the organization to reduce the stress on nurses—both the nurses in charge of the rosters and the nurses subject to the rosters. “Night shifts were more equitably distributed among the nurses, while we have been able to reduce the 45 minutes per day required to amend rosters to just 10 minutes,” says Muthana. “In Bangalore alone, we have 51 nurses in charge of rostering—so the combined saving there equates to nearly 30 hours per day.” This is freeing up these senior nurses to complete more important tasks.
The application was subsequently implemented at Kasturba Hospital, Manipal, again reducing the time needed to generate complete nursing rosters to less than 10 minutes.
Managing leave and training
The organization now plans to extend the application to manage leave and training for its nurses and other employees. “I would like to see every employee given an annual leave plan that is added to the roster at the start of the year,” says Muthana. “The flexibility and control afforded by the application would enable us to address challenges such as managing leave across a workforce with high attrition rates.” The organization would also be able to create a calendar to ensure nurses receive all their required training.
“We also plan to keep fine-tuning the application to deploy nurses more efficiently and continue to reduce their stress levels,” adds Muthana. “We also want to create a nursing load indicator tailored to patients’ specific circumstances. For example, a sedated patient may not require much nursing care, whereas a patient who comes in with a broken leg and may be on a ventilator may require assistance from three nurses at once.” The business plans to use Google’s AI and machine learning APIs in the future to improve the value and user experience of the product.
AI Solutions for Government Organizations: How to Get Started
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Cloud-native features are helping public sector teams innovate faster than ever. Ideas discussed in a morning meeting can be a working proof of concept later that day. Managed services can remove administrative burden and reduce the steps needed to design and provision cloud infrastructure. Security can be built-in from the beginning with Identity and Access Management (IAM), Virtual Private Cloud Service Controls (VPC-SC), and Data Loss Prevention (DLP). Short-lived services and infrastructure-as-code allow rapid and cost-effective prototyping. These technologies can be used to architect a solution that follows the principle of least privilege and helps you secure your data.
So the question becomes: given the complex problems agencies face, where do you start? Google Public Sector now offers “Getting Started” and “Scaling” service offerings for CCAI, DocAI, and BigQuery to help you jumpstart your AI journey, based on where you are.
Complex problems, simpler AI-based solutions
Solving more challenging problems with cloud-native technology doesn’t have to be overwhelming. You can approach them the same way you might solve a puzzle: start with one piece that follows another until the larger picture takes shape. Though you can simplify the steps, solving these problems still requires powerful tools. Google Cloud’s AI/ML capabilities may be the answer for your team.
Google Public Sector is making it easier to get started with advanced technologies, beginning with artificial intelligence and machine learning (AI/ML) workloads for government organizations, and it’s something you can do now, one piece at a time.
Automating your FAQs with CCAI
Does your agency require a team to answer commonly asked questions? What if you could train an agent to answer questions immediately and operate 24/7? Contact Center AI (CCAI) can do this and more. Already using CCAI and need the agent to level up to address complex interactive dialogs? Getting Started with CCAI and Scaling with CCAI are new Google service offerings specifically designed to help public sector organizations tackle situations like these.
Automate data entry with DocAI
How many hours does your team spend manually reviewing or entering data from standardized forms? What if you could automatically pull data right from the page? Google Document AI (DocAI) specializes in exactly this—even if the form has handwritten text. Getting Started with DocAI and Scaling with DocAI are new service offerings that help you remove this burden from your team. DocAI automates data entry and makes that data available to other teams while prioritizing both security and ease of use.
Making data and insights accessible with BigQuery
Then there’s all your existing data. You may have years of it stored in many places, and you may not have a way to make use of it when you need it. BigQuery is Google’s enterprise data warehouse. It was designed for data analytics—looking back at historical data to make conclusions about it. But BigQuery’s analytics don’t stop there. It can also look forward in time to make predictions, often using the same datasets. Getting Started with BigQuery and Scaling with BigQuery are new service offerings that help you take your first steps toward AI/ML capabilities by starting with a single table or pipeline that can help make sense of all your data.
The best help is the kind that meets you where you are and gets you where you want to be. Google Public Sector’s new service offerings do just that: help you work through complex problems by meeting you wherever your starting line is, whether you’re ready to start or ready to scale. Let us know if you would like us to contact you about the services mentioned in this article. Let’s solve your highest impact problems together, one puzzle piece at a time.
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.
You Can Now ‘Listen’ to Over 50 Tech Blogs on Google Cloud Reader
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🎧 Prefer to listen? Check out this episode on the Google Cloud Reader podcast
If you’re anything like me, you love reading, but also appreciate that sometimes your eyes need to be doing other things; whether it’s finding your exit off the highway, or keeping your puppy from destroying the couch.
And sometimes the thought of sitting down to read something just feels like it’s going to take valuable multi-tasking time away from my day. I know, I know, multitasking can be frowned upon, but it’s the way I live a good chunk of my life, and it’s working out so far. And while I’m not alone in my multitasking, I’m also not alone in my desire for a non-visual way to get this content, or any content.
*Google Cloud Reader enters the chat*
Google Cloud Reader is a podcast that lets you listen to the Google Cloud Blog posts that aren’t as dependent on visuals. This means they’re articles that are, or are adapted to be, less focused on graphs, or code samples, and instead describe the meaning behind those visual aids.
It’s an easy, audible way to absorb content around all things new in Cloud, while still being able to make sure Ruthie doesn’t eat my work from home equipment.
So by now you’re probably thinking “OK, so you started a podcast during the pandemic, even though you definitely seemed like the type to start making sourdough”—and you’re right. My 53 plants agree with you. But rest assured, one can listen to an episode of this podcast *while* creating a macramé plant hanger, or waiting for bread to rise—multitasking, am I right?
We’re a little over 50 episodes/macrame plant hangers in, so you should check it out (Ruth and I would appreciate it).
Some of my personal favorites
- Beginners Guide to Painless Machine Learning – Learn how to get started with Google Cloud AI tools
- Introducing GKE Autopilot: A Revolution in Managed Kubernetes – Learn more about GKE Autopilot, a revolutionary mode of operations for managed Kubernetes that lets you focus on your software, while GKE Autopilot manages the infrastructure.
- Cook up your own ML recipes with AI Platform – Learn about Mars Wrigley’s new ML-inspired recipe experiment on Google Cloud and how you can get started with your own.
- Recovering Global Wildlife Populations using ML – Review Google’s Wildlife Insight’s ML project and help users create an image classification model for motion-sensor cameras (called camera traps) used to help protect wildlife in an non-invasive way by collecting and tagging species via pictures.
Let me know your favorite episodes, and what other articles you’d like to hear on Twitter @jbrojbrojbro!
No matter why you prefer an audio format, we’ve got you covered; Google Cloud Reader, where we read the tech blog for you, and to you.
Get all the Google Cloud Reader on your favorite podcast platform, including Google Podcasts, Apple Podcasts, and Spotify.
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