As an intern at an AI startup, I was tasked with collecting, storing, and organizing data for an after-sales (SAV) agent project in the banking and insurance industry. My focus was on three banks and an insurance company, but I quickly realized that scraping their websites wasn’t yielding the useful data I needed.
The websites were often outdated, with little product or service information. Most of the content was news, press releases, and conferences, which didn’t seem relevant for an after-sales agent. Their social media was also dominated by marketing and event announcements.
This left me with a small and incomplete dataset, which didn’t seem sufficient for training a useful customer support AI. My supervisor suggested scraping everything, but I wasn’t convinced that this was valuable for a customer-facing SAV agent.
So, I started wondering: what kinds of data do people usually collect to build an AI agent for after-sales service in banking and insurance? How is this data typically organized and divided? And where else can I find useful, domain-specific data that actually helps the AI answer real customer questions?
## The Importance of Relevant Data
Collecting the right data is crucial for building an effective AI after-sales agent. The data should be relevant, accurate, and comprehensive. It should cover various aspects of the banking and insurance services, including products, features, and customer issues.
## Typical Data Sources
Some common data sources for building an AI after-sales agent in banking and insurance include:
– **Customer feedback and reviews**: This provides valuable insights into customer pain points and expectations.
– **Product descriptions and features**: This helps the AI understand the services and products offered by the banks and insurance companies.
– **FAQs and knowledge bases**: This provides a wealth of information on common customer queries and issues.
– **Industry reports and research studies**: This offers insights into industry trends, customer behavior, and market analysis.
– **Social media and forums**: This provides a platform for understanding customer concerns, opinions, and experiences.
## Organizing and Structuring Data
Organizing and structuring the data is critical for building an effective AI after-sales agent. The data can be divided into categories such as:
– **FAQs and knowledge bases**: This includes information on common customer queries, product features, and service details.
– **Workflows and escalation cases**: This includes information on customer issue resolution, workflows, and escalation procedures.
– **Product and service information**: This includes information on banking and insurance products, features, and services.
## Beyond Official Sites
Looking beyond official websites can provide valuable insights and data for building an AI after-sales agent. Some sources to consider include:
– **Industry forums and discussion boards**: This provides a platform for understanding customer concerns, opinions, and experiences.
– **Social media and online reviews**: This offers insights into customer feedback, opinions, and expectations.
– **Industry reports and research studies**: This provides insights into industry trends, customer behavior, and market analysis.
By collecting and structuring the right data, and looking beyond official websites, you can build a comprehensive and effective AI after-sales agent that provides valuable support to customers in the banking and insurance industry.