How to Use Machine Learning to Achieve 300% Increase in Gross Profits - Build What's Next

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How to Use Machine Learning to Achieve 300% Increase in Gross Profits

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Case Study

Japanese Used Car Operator Uses Machine Learning to Improve Marketing

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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!

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Contact Center AI & Automation Anywhere Help Virtual Agents Deliver Next Level CX

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Contact centers have scaled their performance with Conversational AI. To go a step ahead in elevating customer expectations and experience read how the nexus of Google's CCAI and the cloud-native Automation 360 Platform helps!

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.

Case Study

Tackling Real-Time Bidding Challenges: Arpeely’s Fresh Approach with Google Cloud

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Explore Arpeely's breakthrough in digital advertising. By utilizing Google Cloud's ML capabilities, they've transformed the real-time bidding process, delivering precision, cost-effectiveness, and high-performing results for advertisers. Learn more!

At Arpeely, we’ve developed some of the world’s most advanced advertising technology. Our machine learning (ML) media acquisition platform and “win-win” business model enables customers to bring highly intentful users to their offerings with precision, peace of mind and minimal overhead.

Real-time bidding is a dynamic and intricate process that involves buying and selling ad impressions in milliseconds. Each time a user opens an app or website, advertisers or ad-tech companies acting on behalf of advertisers have milliseconds to bid on ad spaces in real-time auctions, and the highest bidder wins the opportunity to display their ad. This is where Arpeely comes in, leveraging advanced algorithms, innovative UX and funnels to optimize the bidding process and maximize performance for advertisers.

At Arpeely, we use various signals often not used by traditional competitors to outperform the market and zero-down on high-intent and soon-to-be loyal users. For example, in advertising a mobile app, we predict, based on real-time conditions and user context, the user’s likelihood to make an in-app purchase many weeks into the future.

As for the ads themselves, gone are the days of simple banners; today’s ads are “mini products” that captivate and engage users. We employ a wide range of ad formats that go beyond industry standards. Our ads can be immersive videos, compelling messages, interactive experiences like mini-games or mini-apps, or even a multi-step mixture of all of the above. By integrating logic and interactivity, Arpeely enables users to engage with the ad content seamlessly and gauge user intent without leaving their main activity.

Our business model dictates that we don’t get paid if our advertiser doesn’t get paid. We like to say that our algorithms, like water, can trickle into hidden market opportunities missed by the rest of the industry that uses less granular tools. These and other capabilities make Arpeely a strategic partner in the challenging space of media and user acquisition.

Innovation at the edges of data science and engineering

Today, we handle millions of impressions per second and over a billion ML predictions daily. We are directly connected to seven of the world’s largest real-time bidding exchanges, including Google AdX. We also work very closely with our clients, ranging from prominent startups to companies in the S&P Top 20.

Daily, we tackle complex engineering and data challenges on multiple fronts. On the engineering side, we ensure that every real-time auction receives a lightning-fast response within a strict 150ms timeframe. On the data side, we fire multiple ML predictions per auction and ingest TBs of data daily. On the user-serving front, we serve A/B-tested assets across a long tail of geos and devices, with even the slightest fluctuations in load speeds affecting business dramatically.

Right from the start, we knew we couldn’t do it alone when it came to building our technology stack. When you look at available platforms, it’s clear Google Cloud has a robust architecture that is easy to manage, use and scale, especially for our use cases. They also have reliability and feature completeness which are critical in our line of business.

Under the hood

Building upon Google Kubernetes Engine (GKE), we run multiple services that handle our main bidding flows. We utilize Golang for services that run at a large scale and Python for when we prioritize development speed, community and readability. All of these can reach an immensely high scale, which is managed and monitored automatically in GKE. Communication between these services and our Redis (our Google Cloud partner) cluster happens in sub-millisecond latency over Google Cloud’s strong network infrastructure and enables us to run complex real-time logic for every impression.

Once we have tackled the actual bidding, we are left with the challenge of streaming our data into BigQuery for analytics and model training. We utilize a mix of Cloud Pub/Sub, Memorystore and Cloud Storage to create a mechanism capable of ingesting many TBs per day in near real-time without compromising on cost. Real-time data is critical for a company like Arpeely to test and reiterate at a fast pace.

BigQuery is our data warehouse for operational analysis and is an essential part of our business. We use it both as a warehouse, for large-scale computations and in an operational capacity that closes the loop between production, data, and ML retraining. A team of two or three people can manage petabytes at scale with minimal maintenance.

On top of these, we’ve built a state-of-the-art in-house model pipeline suited specifically for ad-tech industry purposes. It allows us to effectively deploy complex solutions — on-the-fly calibration, flexible conversion steps, sampling of heavily imbalanced data sets, adjusting weights, and A/B-tested model deployment and more.

Google Cloud also offers us an entry point for several very useful built-in products that have become deeply embedded in our daily stack and routine. Among them are Operations Suite (formerly Stackdriver), Cloud Profiler, Cloud Storage, Cloud CDN, Cloud SQL, Memorystore, Cloud Scheduler, Error Reporting, and more.

In addition to all the technology, there’s also a human touch. Google’s skilled Customer Success team possesses a unique blend of technical expertise and business acumen, acting as strategic advertisers and opening doors we did not know existed.

Opening the door to the future of advertising

Standardizing on Google Cloud enables us to focus our resources on innovation and growth. Instead of having to research business solutions and invest time integrating disparate technologies, we can tap into a wide range of Google Cloud tools as needed. Thus, it is crucial that we set up a good technological foundation and prepare for future growth of the business.

Google Cloud enables us to focus our resources on innovation rather than our infrastructure. This means we can put more effort into finding ways to match clients with high-value customers and grow their revenues. Even though online advertising has been around for over 20 years and pioneered by Google itself, Google Cloud gives us the impetus to disrupt the market and deliver greater levels of value to our customers today and in the future. 

https://storage.googleapis.com/gweb-cloudblog-publish/original_images/image2_I5Sw9A3.jpg
Arpeely team members

If you want to learn more about how Google Cloud can help your startup, visit our page here to get more information about our program, and sign up for our communications to get a look at our community activities, digital events, special offers, and more.

Check out the Redis listing on Google Cloud Marketplace. Please give it a try and let us know what you think!

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The Secret to Accelerated ML Model Training

As an infrastructure or a data science professional, it’s more critical than ever to keep abreast of the changes taking place to the infrastructure powering machine learning.

If we take a step back, we will realize that there’s been a tremendous amount of progress in machine learning in the last few years, resulting in multiple benefits including higher accuracy in speech and image recognition, for instance. This progress is in part due to the advances in the systems to train models.

One of these advances comes in the type of processors being leveraged to power ML. There’s been an evolution—from CPUs to GPUs to TPUs—each of which bring additional computational power.

Google Cloud TPUs allow data scientists to train their models at lightning speed on Google Cloud’s latest ML hardware.

In this video, you will see how to use your raw data to create different ML applications. More importantly, Zak Stone, Product Manager For TensorFlow and Cloud TPUs, Google Brain, will show you how to accelerate ML model training on TPUs using Google Cloud’s optimized, TensorFlow image classification code. Each step will be explained in detail.

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Explainer

How Visual Inspection AI Transforms the Manufacturing Industry

The pandemic has created demand volatility and has placed lots of pressure on manufacturers. Decreased demand for new products, the disruption of retail channels, and interruptions to supply chain operations have made it very challenging for manufacturers to operate profitable businesses.

This has left manufacturers keen to decrease costs and improve work efficiency by automating many of their work processes.

And many are discovering that visual inspection utilizing AI can help.

This video, hosted by Ying Fei, Product manager, Google Cloud and Sudhindra K Ghanathe, Industry Solutions Lead of Accenutre Google Business Group, Accenture, showcases how world-leading manufacturing companies use visual inspection AI to transform their business. Customers such as Siemens share how they use Google visual inspection AI to automate quality control process, achieve cost savings, and improve work efficiency.

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End Security Risks with the Unattended Projects Recommender Feature

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