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Nippon India Mutual Fund Re-Invents How Indians Buy with AI
Nippon India Mutual Fund, formerly Reliance Mutual Funds is changing the way Indians purchase funds making it easier and faster, with the help of Google Cloud.
In India, only 3-4 percent of the population has invested in mutual funds. There’s a sizeable market to tap into for mutual fund houses–if they can find ways to make it easier for first-time investors to take the plunge.
As the leading retail asset management company in the country, Reliance Mutual Funds, decided to use voice to facilitate transactions.
“That would create a delightful experience for the investor,” says Arpan Saha, Head of Digital Business, Nippon India Mutual Fund.
That’s exactly what the company did using the Google Cloud Platform.
Today, the company has over 10,000 interactions using the Google AI Platform.
“Today we see more consumer coming and doing more transactions with us, and we only see this going up as we make this experience razor-sharp,” says Saha.
AirAsia Turns to Google Cloud to refine Pricing, Increase Revenue, and Improve Customer Experience
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AirAsia needed a platform incorporating products that could capture, process, analyze, and report on data, while delivering value for money and meeting its speed and availability requirements. The airline also wanted to minimise infrastructure management and system administration demands on its technology team members.
The airline conducted a proof of concept and found Google Cloud Platform—including the Google BigQuery analytics data warehouse—was the best fit.
AirAsia was impressed by the ease and flexibility with which it could extract, transform, and load customer data from its systems, websites, and mobile applications into Google BigQuery for analysis. Reporting and dashboards were quickly and effectively delivered through Google Data Studio.
“With a minimal number of people involved, we can very quickly transform an idea or thought process into a deliverable. Prior to Google Cloud Platform, bringing those ideas to fruition would have been impossible,” says Nikunj Shanti, Chief Data and Digital Officer, AirAsia.
Conversational AI drives better customer experiences
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Conversational AI is opening up a new world of possibilities in areas like customer experience, user engagement, and access to content.
In Cloud AI, we’ve taken Google’s groundbreaking machine learning models in speech and natural language processing and applied them to the contact center space, radically improving the customer experience while also driving down operational costs. It’s shifting the focus of contact centers from the backroom, out of sight (and mind) to the boardroom and the strategic heart of the business.
Our solution, called Contact Center AI (CCAI), is an accelerator of digital transformation as organizations all over the world figure out how to support their customers during these challenging times. One customer that’s redefined the possibilities of AI-powered conversation using CCAI, is Verizon.
Whether through voice calls or chat, Verizon customers will no longer need to go through menu prompts or option trees; they simply say or type their question, and CCAI’s natural-language recognition feature finds the best way to assist them. No stilted speech or robot-like commands.
“These customer service enhancements, powered by the Verizon collaboration with Google Cloud, offer a faster and more personalized digital experience for our customers while empowering our customer support agents to provide a higher level of service,” said Shankar Arumugavelu, SVP & CIO at Verizon.
CCAI is also driving cost savings without cutting corners on customer service. In the past, to improve customer satisfaction (CSAT), you had to hire more agents, increasing operating costs. Conversely, if you reduced the number of agents, your CSAT scores went down. Contact Center AI (CCAI) lets you have both.
CCAI does this by expediting customer requests using virtual agents, assisting live agents, and providing insights on customer interactions to drive improvements back into the system. The result is a better experience for your customers—they get answers faster—and you get lower operating costs. It allows human agents to focus on higher value tasks in their customer interactions.
New products and features add richer customer experience
We are continuing to invest in CCAI and today we are excited to announce the following new capabilities:
Dialogflow CX is the latest version of Dialogflow, a development suite used by over a million developers for building natural, rich conversational experiences into mobile and web applications, smart devices, bots, interactive voice response systems, popular messaging platforms and more. This new version of Dialogflow is optimized for large contact centers that deal with complex (multi-turn) conversations and it is truly omnichannel – you build it once and deploy it everywhere – in your contact centers and digital channels. Dialogflow CX features a new visual builder to create, build and manage virtual agents. It’s available now, in beta.
Lukasz Rewerenda
Dialogflow CX brings conversation state management to a whole new level
Principal Solutions Architect, Randstad (Netherlands)
Agent Assist for Chat is a new module for Agent Assist that provides agents with continuous support over “chat” in addition to voice calls, by identifying intent and providing real-time, step-by-step assistance. Agent Assist enables agents to be more agile and efficient and spend more time on difficult conversations, giving both the customer and the agent a better experience. It transcribes calls in real time, identifies customer intent, provides real-time, step by step assistance (recommended articles, workflows, etc.), and automates call dispositions.
For customers in regulated industries, Agent Assist can remove the risk of agents providing inaccurate information (which can happen due to high agent turnover and limited training). Agent Assist can also surface the latest discount information, deals and special offers, which can be hard for agents to keep track of as this information changes frequently.
Custom Voice, available in beta, is a new capability for CCAI and our Text-to-Speech API that lets you create a unique voice to represent your brand across all your customer touchpoints, instead of using a common voice shared with other organizations. By taking advantage of the custom Text-to-Speech model created with Custom Voice, you can define and choose the voice profile that suits your business and adjust to changes without scheduling studio time with voice actors to record new phrases.
At Google Cloud, we are committed to ensuring that our products and features are in alignment with our AI Principles. If you are interested in Custom Voice, there is a review process to ensure that your use case is aligned with our AI principles. Learn more about Custom Voice and how to sign up here.
Bring Google’s groundbreaking speech models on-prem
For many customers, an all-in approach to public cloud is not an option, which is why we’re extending our AI capabilities to run on-prem. Last week we announced Speech-to-Text On-Prem, the first of our hybrid AI offerings, now generally available. Speech-to-Text On-Prem gives you full control over speech data and since it runs in your own data center, it’s easy to comply with your data residency and compliance requirements. At the same time, Speech-to-Text On-Prem uses state-of-the-art speech models from Google researchers that are more accurate, smaller, and require less computing resources to run than existing solutions.
Take a look at how businesses saved costs and improved customer experience with CCAI in the new commissioned study conducted by Forrester Consulting: “New Technology: The Projected Total Economic Impact™ Of Google Cloud Contact Center AI.” To learn more about CCAI and conversational AI, see how our customers are using the solution and check out these sessions at Next OnAir:
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How Apna is using data and AI to drive the gig economy in India
Nippon India Mutual Fund, formerly Reliance Mutual Funds is changing the way Indians purchase funds making it easier and faster, with the help of Google Cloud.
In India, only 3-4 percent of the population has invested in mutual funds. There’s a sizeable market to tap into for mutual fund houses–if they can find ways to make it easier for first-time investors to take the plunge.
As the leading retail asset management company in the country, Reliance Mutual Funds, decided to use voice to facilitate transactions.
“That would create a delightful experience for the investor,” says Arpan Saha, Head of Digital Business, Nippon India Mutual Fund.
That’s exactly what the company did using the Google Cloud Platform.
Today, the company has over 10,000 interactions using the Google AI Platform.
“Today we see more consumer coming and doing more transactions with us, and we only see this going up as we make this experience razor-sharp,” says Saha.
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How FLYR and Google Cloud Help Airlines Forecast Demand and Set Prices
FLYR Labs is an international team of industry experts and specialists in revenue management that works to bring in intelligence to the airlines companies. FLYR uses machine learning and AI to help predict demand and optimize price so that every airline is operating its complete capacity. Watch the video from Architecting with Google Cloud to deep-dive into a use case with FLYR involving the use of historic data, competitors data and future information to build model for outputting demand, set prices and optimize revenue. You can can even have a quick view of the FLYR ML platform!
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Everything You Need to Know About Google Cloud ML Engine 101
Machine learning is all around us today. But data scientists and IT teams tasked with creating models have a hard time bringing together the right mix of ingredients—from data, infrastructure, tools, and APIs—to do their jobs effectively.
Google’s Cloud ML Engine eases many of the challenges data scientists and IT teams face. It’s a managed service that allows businesses to build and deploy their own models using any type or any size of data.
With Google’s Cloud ML Engine, data scientists can create models for training and prediction. And it provides APIs for these two building blocks.
Nikhil Kothari, Senior Staff Software Engineer, Google Cloud, breaks down Google’s Cloud ML Engine. He shows you how to use it as a service, so that data scientists can focus on data and on building models instead of managing infrastructure.
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