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Tiger Global-backed conversational AI platform, Gupshup.io, has introduced new updates to its conversation engagement platform, aiming to streamline and automate various aspects of customer interaction, including acquisition, qualification, personalised engagement, re-marketing, and customer service.
Some notable additions include audience categorisation, automated retargeting for leads from click-to-chat ads, and the integration of Gupshup ACE LLM for better interactions in AI-driven chat and voice bots.
Valued at $1.4 billion, the unicorn assists clients on diverse channels such as WhatsApp, Instagram, RCS, GBM, and others. Beyond personalised one-way messages, users engage in interactive experiences, ranging from quizzes and contests to completing processes like KYC.
“Our platform integrates with core banking, marketing, e-commerce, and payment systems, alongside APIs for credit scores and identity verification,” said Gaurav Kachhawa, chief product officer at Gupshup, in an exclusive interview with AIM.
Major brands in various sectors, including BFSI and retail, like HSBC, Kotak Mahindra Bank, and Flipkart, as well as Unilever, too use Gupshup.io.
The platform’s Bot Studio and visual journey builder allow business users to create omnichannel bot flows, while the AI-powered Agent Assist dashboard helps agents with consultative selling based on customer interactions for improved resolution and conversion rates.
“The conversational phenomenon has seen a massive boost with the rise of LLMs, and we observe businesses transitioning to two-way interactions, whether for advertising, growth marketing, commerce, or support,” Kachhawa added.
Key Updates
The Campaign Manager facilitates the conversion of messages into two-way interactions through linked journeys and templates, allowing for instant template previews and improved campaign customisation. The platform also features AI-driven tools like Agent Assist, including the beta version of AI Summarise, which generates concise chat summaries to streamline customer support by reducing agent time spent on lengthy chat histories.
Additional features such as Rephrase and Expand help agents craft more professional responses. Click-to-chat ads enable lead acquisition and qualification through chatbot conversations, reducing friction associated with form fills. The Conversational Ads Manager supports building a first-party database and boosts conversions through remarketing within the chat window. Marketers can send retargeting messages up to 72 hours.
“Unlike conventional ads with 1-5% conversion rates, click-to-chat ads redirect users to a brand’s chatbot, capturing their info for personalised interactions,” Kachhawa said. He further added that for ecommerce, it’s about swift re-engagement, be it exclusive deals, cart reminders, or interactive gamification within 72 hours — all done in the same chat window.
The inclusion of Full Funnel Analytics goes beyond traditional metrics, providing brands with insights into chatbot funnel performance and the effectiveness of retargeting campaigns. Metrics cover various aspects, including conversations, messages, users, returning users, and identifying typical drop-off points for optimisation.
The release introduces ACE LLM for Natural Conversations, integrating AI into the no-code journey builder.
Riding the Generative AI Wave
Two months ago, the company launched ACE LLM, a series of domain-specific LLMs tailored for functions like marketing, commerce, support, HR & IT, and industries such as banking, retail, and utilities. These models, based on foundation models like Meta’s Llama 2, OpenAI GPT-3.5 Turbo, and others, are finely tuned for specific industries with enterprise-grade safety controls.
“When domain-specific LLMs are used in an industry or function-specific context, they are far more adept at enabling an enterprise’s chatbot to give answers and insights that are clear, accurate, and devoid of noisy data,” said Kachhawa.
With sizes ranging from seven to 70 billion parameters, ACE LLM supports text generation in over 100 languages. According to Kachhawa, a fine-tuned and customised LLM, such as ACE LLM, addresses various enterprise requirements like ensuring compliance with local data residency regulations by storing data demographic wise, enabling better control over LLM output to prevent hallucinations.
