By Donna Fluss
Contact centers contain a significant amount of customer data as most interactions with customers and prospects are captured and recorded by one or more of the department’s systems or applications. Structured inputs are saved in a system of record, such as a customer relationship management (CRM) solution or other customer tracking application. Unstructured inputs, including customer conversations (via voice or digital channels), freeform survey comments, etc., are collected in omnichannel recording systems and either transcribed or structured for analysis by conversation analytics applications. Information and insights contained in these inputs provide actionable findings that can help improve contact center and agent performance and identify customer needs and wants.
Despite its usefulness, most customer data captured by contact centers is generally not shared outside the department, even though the information surfaces business, procedural, operational, and systems issues and new sales opportunities throughout the organization. Although this data was overlooked in the past, this must change in the artificial intelligence (AI) era. Customer data collected in the contact center should be shared with other enterprise functions and business intelligence (BI) repositories to enhance the company’s bottom line and brand.
The fuel or enabler for AI initiatives is data. In many cases, the larger the repository, the better, particularly when the information is targeted for a specific use, like data from a company’s contact center or customer service organization. Another important information source for AI initiatives is the enterprise’s knowledge base or knowledge management system, which is typically the responsibility of the contact center. This solution usually contains detailed information about an organization’s products, services, policies, and procedures.
When it comes to generative AI (GenAI)-based solutions, the applications’ underlying training data is essential for their success. GenAI technologies utilized in contact center solutions are primarily trained using large language models (LLMs), which can contain generic training data (public LLMs), be specific to an industry or company (private LLMs), or have a combination of both. Initially, public LLMs were leveraged almost exclusively, including various iterations of OpenAI’s Generative Pre-trained Transformers (GPT-3, GPT-3.5, GPT-4), Google’s PaLM, Anthropic’s Claude, Meta’s Llama 2, Cohere, Hugging Face, and others. Today, a growing number of contact center software vendors are building customized private models trained on contact center data because their clients are showing a strong preference for domain-specific and verticalized LLMs. Vendors are also striving to improve their LLMs’ quality and value by fine-tuning public models with their own data to improve accuracy; supporting the simultaneous use of multiple LLMs; or leveraging different ones based on use case. In addition, some enterprises are beginning to create proprietary LLMs so they have complete control of the data leveraged in their organizations by GenAI technologies. Since GenAI creates its original content based on the provided training data, it’s imperative that any LLM used, public or private, contains targeted, tagged, curated, and maintained information appropriate for the task at hand, and includes effective guardrails to avoid hallucinations.
Companies need to determine how to share structured and unstructured customer data captured by the contact center, as well as information contained in their Knowledge Management solution, with other departments to enhance enterprise-wide AI initiatives. The contact center data is also very effective for helping companies train and improve the LLMs used to support GenAI-enabled solutions throughout the organization. This in turn, will reduce operating costs, increase revenue, and improve the customer and employee experience, which are the primary goals of applying AI in enterprises.
Donna Fluss, founder and president of DMG Consulting LLC, provides a unique and unparalleled understanding of the people, processes and technology that drive the strategic direction of the dynamic and rapidly transforming contact center and back-office markets. Donna can be reached at donna.fluss@dmgconsult.com.