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Tag: natural language processing

  • Companies use NLP-based sentiment evaluation to supply intelligence

    NEW DELHI: Pandemic-led issues are prompting corporations throughout sectors to make use of synthetic intelligence (AI)-powered sentiment evaluation.

    For occasion, after the Reserve Bank of India (RBI) lifted a moratorium on mortgage repayments, banks and non-banking monetary corporations (NBFCs) needed to cope with a backlog of pending loans. To speed up the method, many NBFCs and banks started to undertake pure language processing (NLP)-based options to evaluate a borrower’s sentiment from their conversations.

    A living proof is Credgenics’ sentiment evaluation device that makes use of speech recognition and evaluation of chats over automated voice bots and WhatsApp bots to generate insights about debtors. These have allowed NBFCs and banks to establish the issues that debtors are dealing with in paying loans. Credgenics claims that over 60 lending establishments together with ICICI Bank, Axis Bank and IDFC First Bank are utilizing its sentiment analysis-based software-as-a-service (SaaS) platform.

    “This has allowed them to plan the communication technique and channel for the lenders for optimum outcomes,” said Anand Agrawal, co-founder and chief technology officer at Credgenics. He said sentiment analysis helps extract subjective meaning from text to determine a borrower’s sentiment. It is an ideal tool for reviewing unstructured content about a particular borrower’s digital communication for insights.

    According to Agrawal, sentiment analysis has enabled lending institutions to improve their debt collection rates by 15-20% and recover 70-95% of their bad debts.

    Sentiment analysis is also helping companies stem attrition. Firms are using these tools to identify employees who might leave, and retain them with perks, salary hikes, and a better working environment. “We have seen customers able to retain 85% of their top talent (using sentiment analysis),” stated Tanmaya Jain, founder and chief govt of inFeedo, a SaaS agency that gives sentiment evaluation instruments to over 200 corporations, together with Samsung, Airtel, Xiaomi and Lenovo in India.

    Jain stated one of many prospects in India, a big unnamed enterprise with over 3,000 workers, was struggling to retain workers after a big merger with one other firm. After deploying a sentiment evaluation chatbot, the corporate was in a greater place to estimate worker sentiment and managed to extend its retention price by over 10%.

    InFeedo’s AI chatbot presents insights primarily based on its interplay with workers. The bot makes use of NLP to know the context and establish workers who appear disengaged and usually tend to go away.

    The use of sentiment evaluation isn’t completely new. Earlier, using NLP for sentiment evaluation was restricted to tech giants corresponding to Google and Amazon, which had extra information and AI and ML engineers to experiment with it.

    Among Indian corporations, e-commerce companies corresponding to Flipkart had been the primary to undertake it to know buyer sentiment by analysing consumer critiques utilizing NLP.

    NLP, a subset of AI, permits a bit of software program to learn, perceive and derive context in textual content and spoken phrases similar to people. It can be utilized in any area the place human dialog is concerned. Before NLP, most AI-based chatbots operated and responded inside a set boundary of mounted set of questions and solutions.

    Sentiment evaluation has been round for years, however the curiosity in it’s rising amongst many companies now because the underlying NLP expertise has develop into much more mature. “What has modified is that now the NLP and sentiment evaluation is turning into much more mature by way of accuracy, readiness,” said Jayanth Kolla, co-founder of market researcher Convergence Catalyst.

    He added that the talent pool of people working on it has increased in the recent past, which in turn has led to more adoption.

    According to Kolla, demand for sentiment analysis has grown since the pandemic. He noted that a lot of HR tech firms are using sentiment analysis to read the chatter on platforms like LinkedIn and Glassdoor to rank companies.

    For inFeedo, the demand has grown 3x since the pandemic. “Earlier, with employees being on premises, it was easier to understand employee sentiment, but with hybrid and remote work, and with video conference fatigue, it is difficult for leaders to gauge their employee’s sentiment,” stated Jain.

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  • NLP-powered sensible bots enhance transaction charges amongst clients

    These bots use pure language processing (NLP), a mix of synthetic intelligence (AI) and machine studying (ML) applied sciences, to know pure language in spoken or written kinds.

    “One of the biggest insurance coverage corporations noticed its workforce decreased to 10% of peak capability on the onset of the covid-19 pandemic, whereas buyer question quantity elevated to five instances. In phrases of dealing with transactions, their chatbot may efficiently conclude nearly 78% of their transactions,” says Shekar Murthy, senior vice chairman of options {and professional} providers at Yellow.ai.

    It’s not simply area of interest companies which are benefiting from bots contributing to precise purchases from clients. Gaurav Singh, founder and CEO of automated chat platform Verloop.io, says, “With Nykaa, we deal with nearly 68% of all buyer conversations with none human interference. A majority of buyer requests akin to including or changing objects, altering supply addresses and altering fee strategies are totally automated at present.”

    For one other of Singh’s shoppers, the Abu Dhabi Islamic Bank (ADIB), Verloop.io claims to be efficiently automating 88% of all buyer conversations, “together with acquisition, help, engagement and retention.”

    This degree of automation, corporations declare, helps companies ease transactions and efficiently convert queries into purchases. Talking in regards to the ease of transactions, Beerud Sheth, co-founder and CEO of unicorn startup Gupshup, says, “CreditWise Capital has at present used automation to scale back two-wheeler mortgage processing instances at dealerships all the way down to as little as three minutes – as a substitute of a number of days. It integrates coordination with credit score bureaus akin to Experian to just accept buyer purposes through WhatsApp, to provide them mortgage buy approvals inside minutes.”

    Yellow.ai backs up the variety of corporations which are immediately gaining transactions via chatbots. 

    For Bharat Petroleum, Murthy stated, the voicebot processed over 500,000 LPG cylinder bookings in simply 4 weeks, and even acknowledges totally different dialects.

    “The Madhya Pradesh Electricity Board makes use of an NLP-enabled voice bot that deploys 5 dialects of Hindi to know related phrases when spoken by totally different customers in their very own methods. The accuracy in voice queries in Hindi is within the decrease 90s. For languages past Hindi, our bots are able to performing at above 80% understanding accuracy,” Murthy provides.

    Voice automation, curiously, is an space the place chatbot suppliers see development potential when it comes to precise transactions. “The old style was chat, however now the entire argument is that it must be one AI throughout many channels — whether or not it’s a phone line bot, chatbots or different issues. While chat utilization has gone up in India, it nonetheless lags behind world international locations. That is primarily as a result of actual India doesn’t like to talk in English,” stated Ganesh Gopalan, CEO and co-founder of Gnani.ai. He stated that voice interfaces on an app or perhaps a phone line dialog has allowed the corporate to deal with a number of languages.

    Yellow.ai CEO, Raghu Ravinutala, stated compared to nearly zero voice automation minutes processed simply over a 12 months in the past, his firm’s providers at present course of over 10 million voice automation minutes each month.

    Talking about what it claims to be the “world’s largest insurer”, Yellow.ai says that its multilingual voice bot automation is, in fact, delivering 12% higher efficiency in terms of successfully converting user transactions – as against live, human agents. This is an area that has the potential to tap into India’s “next billion”, as consultants see. 

    Gopalan stated that an insurance coverage consumer who was engaged in one-use case earlier has expanded to 27 use-cases now.

    Gargi Dasgupta, director of IBM Research India and CTO of IBM India-SA, says, “IBM Research India is working with IIT Bombay’s Center for Indian Language Technology (C-FLIT) to allow Watson to know Indian languages natively past translation. Today, Watson is provided to know Hindi utterances in Devanagari, sentence construction, grammar and different nuances and work is on for Watson to know different Indian languages – each spoken and written.”

    What everybody appears to agree upon is that the way forward for automated conversations is just not both voice or textual content, however each. Until the effectivity of voice automation catches up, corporations at present are taking advantage of elevated chatbot effectivity due to pure language processing, to extend precise transactions from clients.

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