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Conversational CX Provider Quiq Focuses on Digital CX and Automation

Quiq Offering Spans Digital Messaging and AI-Based Contact Center

Conversational CX Provider Quiq Focuses on Digital CX and Automation

Quiq is a conversational CX provider that offers a complete solution for serving customers on digital channels. The company has a strong focus on travel and hospitality, retail, and ecommerce and has a solid roster of clients such as IHG Hotels, Terminix, Calvin Klein, and Brinks.

The Futurum Group was able to chat with Quiq CEO Mike Myer as well as VP of Product Marketing Thomas Kuravilla to learn more about the platform and what makes it unique.

Digital First Customer Experience

Quiq offers solutions in two areas, helping to support the entire customer service journey—AI automation for digital messaging—and offering a workspace for agents that leverages AI. The company focuses on trying to help companies resolve tier 1 inquiries without a human, via AI-supported self-service. The assistants are engineered with tools built for large language models (LLMs), and Quiq offers a very human-like, conversational experience for the end user.

Conversational AI and AI automation are getting to be increasingly crowded fields and any company providing solutions in this area needs strong differentiators.

“Everyone is quickly building AI tools right now. But we I think we have a unique position as our goal is to support customer experience with a focus on conversational goals – such as, has this customer fulfilled what they were looking to do with this conversation? Our architecture is based on the identification of intent, help with prompts, and safety. We are not just focused on latency. We use industry best practices, and we have people on our side looking at all the in the outputs, and analyzing the conversations to ensure accuracy,” said Kuravilla.

Kuravilla pointed to helping to make it specific to clients by reviewing their data to see how their interactions were categorized (e.g., 5% related to missing items, 30% are shipping related, and so on), seeing whether there is an API available to solve those particular issues, and how that can best be automated and done in a way that is more human-like than normal assistants.

“In the past, when dealing with AI, you had to really stay exactly within the ‘lines’ and do what the AI guided to do in a very linear fashion. In this generation, our clients don’t have to stay in those lines, and they can interact with AI like they would with a human, even in the middle of an interaction,” said Kuravilla.

There is a lot going on behind the scenes to clarify intent and get to the correct answer. Quiq’s model works to determine customer attributes that will lead to the right answer quickly. The model takes factors into account such as what the actual question is, where the customer is located, if they are a prospect or a customer, etc. Once that background is identified, the answering process is done using only the company’s specific information.

According to Myer, “Often this information is on the other side of an API call, such as account information or a product catalog search, but we might also augment that information with the company’s knowledge base, policies, documentation, or an agent training model. Once gathered, the LLM generates a response, but we make sure the question is answered only with the information provided. We use a lot of prompt engineering to review exchanges to see if the answer got a bit off the rails or refers to non-given information or an offsite URL. This type of brand and safety check is a bit of our secret sauce that happens after we use the LM generated answer and results in a very personalized and concise response.”

Quiq recently shared a case study demonstrating how this approach has worked in practice. LOOP auto insurance partnered with Quiq to replace its previous generation bot. This bot was limited to pre-canned, static answers that were manually built. It was not providing great CX, and the company’s self-service rates were low. Quiq’s assistant was able to better understand human language and the nuances of different questions resulting in some impressive stats including:

  • 3x increase in self-service rates versus the first-gen chatbot previously being used with a 50% automated resolution
  • 75% CSAT score for the AI assistant
  • 55% reduction in email tickets

The Other Side of the Platform: AI Contact Center

In addition to the AI assistants that help support a digital CX, the platform has a mechanism for human in the loop escalation, allowing Quiq to help move a customer through the entire interaction, end to end.

“If you think about the customer journey, when an issue arises, that journey can be potentially solved by AI inside of the digital messaging channel, but if it can’t, that can be escalated to a human agent who is also working inside of the Quiq platform. So, we cover the entire journey inside of a customer’s interaction on a digital channel. We also recently developed the capability of covering the beginning of the voice journey with a voice AI assistant as well,” said Myer.

Conversational CX Provider Quiq Focuses on Digital CX and Automation
Image Source: Quiq

The AI contact center offers fairly robust reporting and insights that help to measure agent performance and can synch with other platforms such as Salesforce so that agents can see the prior interactions, conversations, and cases. An interesting feature is the ability to dynamically determine which customers are the most actively engaged, putting them at the top of the list so that agents can respond to them before other people who might not expect a response quickly. Managers can be invited to join a conversation if an agent needs an assist or have the interaction transferred to them.

Based on the demo we viewed and some independent testing of Loop’s bot, Quiq’s solution appears to be functioning as advertised. The bot was able to quickly retrieve relevant answers to basic questions, and when prompted with off-topic questions or prompts that may be designed to return toxic or inappropriate responses, a stock response indicating that the system wasn’t able to handle the request was returned. Few systems –including Loop’s—are able to respond in a fully natural and human tone. However, the system passed the most important test—ensuring that the bot did not engage with irrelevant or toxic requests—thereby protecting the brand from both operational and reputational harm.

Disclosure: The Futurum Group is a research and advisory firm that engages or has engaged in research, analysis, and advisory services with many technology companies, including those mentioned in this article. The author does not hold any equity positions with any company mentioned in this article.

Analysis and opinions expressed herein are specific to the analyst individually and data and other information that might have been provided for validation, not those of The Futurum Group as a whole.

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Author Information

Keith has over 25 years of experience in research, marketing, and consulting-based fields.

He has authored in-depth reports and market forecast studies covering artificial intelligence, biometrics, data analytics, robotics, high performance computing, and quantum computing, with a specific focus on the use of these technologies within large enterprise organizations and SMBs. He has also established strong working relationships with the international technology vendor community and is a frequent speaker at industry conferences and events.

In his career as a financial and technology journalist he has written for national and trade publications, including BusinessWeek, CNBC.com, Investment Dealers’ Digest, The Red Herring, The Communications of the ACM, and Mobile Computing & Communications, among others.

He is a member of the Association of Independent Information Professionals (AIIP).

Keith holds dual Bachelor of Arts degrees in Magazine Journalism and Sociology from Syracuse University.

As a detail-oriented researcher, Sherril is expert at discovering, gathering and compiling industry and market data to create clear, actionable market and competitive intelligence. With deep experience in market analysis and segmentation she is a consummate collaborator with strong communication skills adept at supporting and forming relationships with cross-functional teams in all levels of organizations.

She brings more than 20 years of experience in technology research and marketing; prior to her current role, she was a Research Analyst at Omdia, authoring market and ecosystem reports on Artificial Intelligence, Robotics, and User Interface technologies. Sherril was previously Manager of Market Research at Intrado Life and Safety, providing competitive analysis and intelligence, business development support, and analyst relations.

Sherril holds a Master of Business Administration in Marketing from University of Colorado, Boulder and a Bachelor of Arts in Psychology from Rutgers University.

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