Rule-based chatbots have become prevalent in customer service and e-commerce over the past decade, but they’ve not been as popular with consumers as the rate of deployment might suggest. Generative AI revolutionizes the consumer-chatbot experience and brings AI-driven “conversational commerce” to the fore.
Gartner figures from a survey of B2B and B2C customers between December 2022 and February 2023 found only 8% of customers used a chatbot in their most recent customer service experience, and only 25% would use the chatbot again. Gartner doesn’t say whether these are rule-based chatbots, but it’s reasonably safe to assume they were.
Experts from Boston Consulting Group (BCG) penned “The Chatbot Is Dead—Long Live the Chatbot,” explaining that “most consumers dislike chatbots—less than a third of online customers use them” based on the group’s latest research.
However, it’s conversational commerce, using generative AI, that is expected to reduce customer service costs by 30% and become a revenue-generating tool by delivering personalized customer experiences and augmented marketing and sales.
The Evolution of the Chatbot
Chatbots really began to evolve to serve customers with the development of SmarterChild, used by AOL and MSN in 2001. Then Apple’s Siri in 2010 and Microsoft’s Cortana and Amazon’s Alexa in 2014. Over the past few years, rule-based chatbots have, quite literally, begun to pop up on every website, offering to answer customer inquiries and product questions.
Rule-based chatbots rely on predefined if/then logic and decision tree functionality. They work reasonably well when asked common questions by consumers because they can be pre-programmed to answer FAQs in certain ways. However, they fall short when consumers ask questions that a rule-based chatbot doesn’t have the pre-programmed answers for and can’t answer complex multi-faceted queries or understand context.
Source: BCG
BCG isn’t wrong. We didn’t like rule-based chatbots all that much. A US Consumer Financial Protection Bureau (CFPB) study published in mid-2023 found all the top ten U.S. commercial banks had deployed chatbots, and by 2022, 37% of customers had interacted with a bot. CFPB’s review of customer complaints found:
“People experience significant negative outcomes due to the technical limitations of chatbots functionality. There are many kinds of negative outcomes for the customer, including wasted time, feeling stuck and frustrated, receiving inaccurate information, and paying more in junk fees. These issues are particularly pronounced when people are unable to obtain tailored support for their problems.”
Even so, the Bureau estimates that using chatbots instead of human agents saves $8 billion per annum or approximately $0.70 per customer interaction. It notes that the banking industry has begun to adopt generative AI chatbots. Goldman Sachs said it would start working on a large language model (LLM) in April 2023. Wells Fargo is using a chatbot “virtual assistant” using an LLM and Google Cloud. Morgan Stanley said it was testing a chatbot powered by ChatGPT-4 to generate scripts for financial advisors.
On the e-commerce front, 76% of online retailers have implemented or plan to implement chatbots into their customer service strategies, and 71% of Gen Z shop through bot interactions. Statistics are still emerging for generative AI chatbot adoption, but some point to around 57% of businesses already using the technology. New research from Deloitte suggests as many as 91% of companies plan to use generative AI in some way.
Will Consumers Actually Like GenAI Chatbots?
The statistics are still relatively sparse. But it is obvious that generative AI’s conversational and contextual understanding abilities, not to mention how easy custom GPT chatbots are to train on company data, mean they are being implemented at astounding rates as companies rush to take advantage of the benefits. The question is will consumers like generative AI chabots?
McKinsey’s Where is customer care in 2024? suggests:
“The transition from a care paradigm dominated by human agents to one steered by AI technologies may be the biggest disruption in the history of customer service.”
Customers, particularly younger generations, are becoming more accepting of using live chat or messaging services.
Source: McKinsey
Respondents in the McKinsey study reporting better-than-expected customer care performances have high levels of digital adoption. Businesses are already using AI tools, including chatbots and automated email response systems, for customer service. It cites the example of one firm that replaced its rule-based chatbot with a generative AI chatbot and discovered it was 20% more effective within seven weeks.
Making Conversational Commerce a Success
Conversational commerce, says BCG, can guide customers through a typical e-commerce funnel “from discovery to conversion.” However, to achieve this, companies must focus on data and technology as well as employee and process changes. BCG’s survey discovered that 66% of US consumers expressed strong interest in trying generative AI-powered conversational commerce. If companies manage risks and create use cases that appeal, they can reap the benefits.
A typical conversational commerce scenario can involve a consumer looking for suggestions from a chatbot, being supported through detailed product comparison, making a purchase, and then being presented with further purchase opportunities via the bot’s upselling.
Source: BCG
Generative AI’s “human-like, proactive and interactive” conversational style and, once trained, its understanding of customer needs powered by natural language processing (NLP) and data integration abilities will deliver a competitive advantage and immense revenue-generating opportunities at scale.
BCG says the rewards are substantial for early adopters harnessing evident consumer enthusiasm for conversational commerce driven by AI (at two to three times higher than interest in traditional conversational commerce).
Source: BCG
There are four aspects to effective generative AI or conversational commerce integration, according to this consultancy company. They are:
Strategy—First articulate the benefits to all stakeholders, then identify customer needs, plan for the future as well as invest in a data foundation.
Technology—Conduct a build vs. buy partner analysis to determine the route to conversational commerce. Aspects to consider include budget, expertise, and capabilities, as well as the language model, platform, customization, ecosystems, and integration. Then, for implementation, data, rules, training, risks, monitoring, and managing are all considerations.
People and processes—BCG says organizations should be “bold” in rethinking processes and functions and that solid change management is critical. Vital aspects are assessing operating models, skill gaps, hiring and training, and assigning new roles. Employees will be trained to manage and interact with technologies, and their roles will become “more strategic and value-adding.”
Governance – The all import risk and risk protection element of AI. BCG says a responsible AI framework begins with “ethical principles, risk taxonomy, and tolerance.” Then comes governance over processes, technology, tools and culture and alignment with company purpose as well as values.
Find out how AI impacts employee roles with What is Human-in-the-Loop (HITL), and Why Does it Matter? and Enhancing Customer Experience with Human-in-the-Loop Strategies in Generative AI to learn more about ensuring the best human-AI collaboration for customer service. Or discover The Future Unveiled: 5 AI-Driven Employment Opportunities Soon to Emerge
