Customer journey optimisation using large language models: Best practices and pitfalls in generative AI
Abstract
Today's business environment is moving faster than ever, and the expressive and adaptive capabilities of generative AI (GenAI) and large language models (LLMs) are redefining the enterprise rails of tomorrow. Given the abundance of industry hype, investor expectations and leadership pressure, the initial impulse is to ‘get in the game’. But how does one implement initiatives that drive business outcomes within ethical parameters while avoiding technical pitfalls? Marketers need practical guidance to navigate through these changes. In this paper, the authors examine multiple considerations for deployment of GenAI in marketing and customer experience. How does the marketer decide on which initiatives and opportunities to begin with? Which use cases will drive value as the organisation adapts to deploying these new capabilities? Once a marketer has identified the opportunities to capitalise on through GenAI, how is the capability deployed? There are a variety of approaches that can be considered given the level of organisational capability with AI and resource levels to be applied. As with any cutting-edge capability, there are potential missteps that must be avoided to ensure success. This paper provides some insight based on practical experiences to date that cover ethical, technical and process concerns. The paper presents thoughtful approaches to the deployment of LLMs and GenAI that can result in concrete ROI and reduced risk even in this early stage of adoption. With this information, marketers can be prepared to confidently begin their journey using GenAI to transform their customer experience and drive enterprise value for their organisations.
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Author's Biography
Vaikunth Thukral Vaikunth is a data science and engineering specialist with a background in experimental particle physics, leading to his passion for AI, research and systems that deal with large and complex data. He has worked on developing new and easy-to-use data analytics technologies used by many data science practitioners around the world and is an advocate for responsible use of data and AI. With experience in both academia and industry, he brings innovation and leadership to creating new methods of democratising data. Most recently, he has been continuing his work in the generative AI space to bring the next generation of enhancements to the field. Prior to Teradata, he was a researcher at Stanford, and he holds a master's from Texas A&M University. Vaikunth is currently Lead AI/ML Engineer at Salesforce.
Lawrence Latvala is the Americas Financial Services Industry Practice Leader at Teradata. His remit encompasses such domains as customer experience, financial crime, chief financial officer analytics, open banking, pricing agility in insurance, risk analytics and payments. Prior to Teradata, Lawrence was Chief Revenue Officer for a FinTech firm. His experience also includes working in Financial Services at Capgemini, as well as for Deloitte, E&Y and the US Defense Department. He holds degrees from the University of Chicago and the University of Rochester.
Mark Swenson is the Director of the CX Practice for North America at Teradata. Mark works with enterprise clients to align CX objectives with best-in-class customer data, analytics and solutions. His 23+ years with Teradata have covered customer experience, digital marketing, campaign management, segmentation, loyalty, optimisation, data warehousing, customer analytics and artificial intelligence, in opportunities all around the globe.
Jeff Horn is an Industry Consultant at Teradata, with a focus on the domain of customer experience within the financial services industry. His experience implementing customer experience solutions includes loyalty, campaign management, mobile applications and customer experience analytics. Prior to his current position, Jeff oversaw the implementation and customer success teams at Loyalty Lab and led customer data strategy and implementation teams for Accenture.
Citation
Thukral, Vaikunth, Latvala, Lawrence, Swenson, Mark and Horn, Jeff (2023, December 1). Customer journey optimisation using large language models: Best practices and pitfalls in generative AI. In the Applied Marketing Analytics: The Peer-Reviewed Journal, Volume 9, Issue 3. https://doi.org/10.69554/DMIV5161.
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