Stay ahead with Yellow AI automation chatbot. Yellow AI Chatbot stands alone in customer service automation in 2024. It is also revolutionizing email support through it generative AI powered email automation. It has recently introduced updates that bring greater intelligence and efficiency to your contact center, ensuring faster, smarter support and happier customers.
Yellow AI analyzes elevating customer service with advanced Bot. Customer service leaders strive to improve efficiency, reduce ticket volumes, and enhance customer satisfaction. While traditional bot metrics offer some insights, they often fall short in addressing critical questions about bot performance and user experience as claimed by yellow ai product and marketing executive Pritika Ramani.
Key Features of Yellow AI
1. Self Learning Loopback for Continuous Improvement
One of the standout features of Yellow AI Analyze is its self learning loopback mechanism. When a bot encounters a query it cannot resolve, the conversation is escalated to a human agent. But the process doesn’t stop there.
The platform has in house LLM (large language model) to analyze how the human agent resolves the issue, then generates a corresponding knowledge base article. This article is later reviewed and approved by a human agent, ensuring accuracy before it is used to train the bot.
Over the time, this creates a continuous cycle of learning and improvement, where the bot becomes smarter and more capable of handling similar inquiries on its own.
In essence, every interaction whether handled by a bot or a human becomes a learning opportunity for the bot, reducing the need for future escalations.
2. AI Powered Topic Clustering for Strategic Insights
Understanding the vast number of conversations that bots handle can be overwhelming. Yellow AI Analyze makes this easier by offering AI generated topic clusters, which group conversations based on the subjects being discussed.
For example, if a large portion of your customers are inquiring about product shipping, Analyze will generate a cluster around this topic. Each cluster is accompanied by valuable insights such as:
Customer sentiment: Was the overall tone positive, neutral, or negative?
Knowledge base recommendations: Are there gaps in the bot’s knowledge that need to be filled?
Conversation share: What percentage of total conversations does this topic represent?
By analyzing these clusters, businesses can identify common pain points, track trends over time, and proactively enhance their bots to better serve customers.
3. Comprehensive Conversation Evaluation
Topic: What issue or question was the user raising?
User Query Summary: A breakdown of what the customer asked.
Resolution Summary: A summary of how the issue was addressed.
Resolution Status: Was the issue fully resolved by the bot, or did it require human intervention?
Containment Rate: What percentage of the conversation was handled entirely by the bot without escalating to a human agent?
Conversation Share: How often does this type of issue appear compared to other inquiries?
4. Sentiment Analysis for Better Customer Experiences
One of the key factors in customer satisfaction is understanding how users feel about their interactions. With sentiment analysis, cloud Yellow ai Analyze categorizes conversations as positive, negative, or neutral, giving businesses a clear picture of how customers are reacting to bot responses.
Moreover, sentiment tags can be applied to entire topic clusters, providing even deeper insights into areas where customer satisfaction is high and where it might be lacking. This feedback loop allows companies to continuously refine their bots to ensure they’re not just answering questions but doing so in a way that leaves customers feeling valued and understood.
HDFC Bank, one of the largest private sector banks in India was facing challenges in managing the high volume of customer inquiries. The traditional methods of handling customer queries were slow, and human agents were overwhelmed with repetitive requests, resulting in delays and lower customer satisfaction.
HDFC Bank implemented Yellow AI’s Conversational AI platform to automate a significant portion of its customer service operations. It deployed a smart chatbot to handle common customer queries like account balance inquiries, loan information, transaction details, and more.
As a result, 30% of queries were resolved by the bot, reducing the need for human intervention. The bank saw a 40% reduction in response times for basic queries. Customer satisfaction rates increased as users were able to get answers in real-time without having to wait for human support. The chatbot also became a valuable tool for marketing and cross-selling, sending personalized loan offers and credit card promotions to customers based on their interactions.
There are ample of example big companies that are using it like Sephora, Domino’s Pizza, Airtel, Hyatt Hotels.
People behind the AI
Yellow ai founder are three friends Raghu Ravinutala, Rashid Khan & Aya Kishore Gollareddy. They are the main co-founders. These three friends started Yellow Messenger in 2016. Letter it turned to yellow ai company. Their first Chatbot gone live in 2017.
Yellow AI funding was raised $4 million in series A. After raising $78 million in Series C funding in 2021, it was valued at around $400 million. The funding round was led by investors such as WestBridge Capital, Sapphire Ventures, and Salesforce Ventures. funding for their global presence. Yellow ai office is now located at United States, India, Malaysia, Singapore, UAE & Indonesia.
This company remains a private company and has not yet gone public. The future of this company is bright. Once it gone public Yellow AI share price will be attractive to buy.
Yellow AI, a leader in conversational AI solutions, has achieved remarkable growth and is transforming customer interactions across industries. The company’s estimated revenue is around $218 million per year, with over 1,000 employees globally.
Yellow AI has been reviewed 4.4 stars. There are similar service providers in the market also. The prominent among them are;
