The rise of artificial intelligence (AI) promises to revolutionize customer service, enabling instantaneous support and readily available information for customers in need. Yet, the hope for a seamless experience often remains unfulfilled, leaving many customers feeling frustrated as they navigate automated systems that seem designed more to deflect them away from human interaction than to provide real solutions.
For customers seeking assistance, it can appear that automation aims to contain inquiries, funneling them through long FAQs instead of addressing their specific concerns. Such unsatisfactory experiences can count as successes for operators, creating a cycle of repeat contact, customer churn, and in the gambling sector, overlooked signs of financial distress and responsible gaming issues, which pose regulatory and protective risks.
Raphie, an AI platform based in New Jersey and founded as Conduet in 2019, focuses on delivering real resolutions, emphasizing accountable automation following over one million real industry interactions to enhance its capabilities.
Justin Heath, co-founder and CEO of Raphie, states, “A big premise on which we have built Raphie is to completely avoid that situation where it feels as though you are talking to a robot that does not know what you are asking.” This commitment has paid off; one of Raphie’s U.S. sportsbook clients has seen customer satisfaction scores exceed 90% with its fully automated service. This success, bolstered by Raphie's industry experience and cost-efficiency potential, has motivated a strategic expansion into European markets.
Critically, Raphie emphasizes that generic AI solutions are ill-equipped to address specific inquiries typical in iGaming. The company highlights that around 90% of incoming contacts in iGaming require information tied to individual accounts. For instance, a customer inquiring about the status of a withdrawal might instead receive a generic response about payment policies instead of a detailed status update specific to their account.
Further complicating matters, generic AI often lacks the necessary industry-specific insights that would allow it to explain the reasoning behind bet settlements and bonuses properly.
Raphie overcomes these limitations with what it terms a "context layer," which integrates with various systems like player account management and customer service. This features allows the AI to tap into relevant data and context, ensuring that responses align with operator protocols.
Raphie's approach is not about selecting between full automation and human agents. Their system features three distinct modes that can function independently or in concert. Full Automation utilizes the Raphie AI Agent for complete interactions while Co-Pilot assists human staff with suggested responses. They also provide specialized human agents from locations like Jersey City and the Philippines for customer, risk, and payment support.
The Arkansas-based sportsbook BetSaracen illustrates the efficacy of Raphie’s AI configurations. After adopting Co-Pilot over a year ago and more recently implementing Full Automation, BetSaracen achieved a 77% autonomous resolution rate in just eight weeks, alongside a 55% reduction in response times, all while maintaining customer satisfaction at over 90%.
Unlike typical evaluation metrics based solely on conversation completions, Raphie evaluates success by measuring customer satisfaction, any need for human agent involvement, and follow-up contacts about the same issue within 24 hours. They perform extensive quality assurance to track unresolved conversations, acknowledging the importance of thorough resolutions.
Heath explains, “An abandoned conversation is a very negative outcome… We assess where a customer has dropped off before the conversation has fully run its course rather than treating that as resolved.” Heath's co-founder, Alex Jones, adds another indicator of success: some users engage so smoothly with the AI that they do not realize it’s an automation tool.
Raphie suggests a controlled four-stage rollout for operators implementing automation. This includes analyzing existing inquiries and workflows, testing responses in shadow mode, gradually introducing automation category by category, and continually optimizing processes as products and player behaviors evolve.
As Raphie designs their system for the European market, they acknowledge the unique regulatory environments across the continent. While their foundational knowledge of payment protocols, bonus structures, and player protection measures remains relevant, the approach must be customized for each market. Operators in Europe face intense cost pressures, prompting them to seek efficiencies, and Raphie asserts it can reduce customer support costs significantly, by more than 60%, making their services appealing even to large operators.
Heath concludes, "European markets are under far more cost pressure, so the desire to move quickly with AI seems to be there. We are seeing interest from small operators right through to some of the biggest globally." As Raphie expands its services, players can expect a definitive shift, with fewer repetitive exchanges leading to effective resolutions and alleviating frustrations in customer support interactions.
