Companies are increasingly willing to delegate core tasks to AI agents. For online shops that answer hundreds of repetitive questions every day – about delivery status, returns or technical details of products – such solutions are an obvious time and resource saver. But this is only one side of the coin. The other, less obvious, is the strategic potential of the data generated during these interactions.
Chat data is not just logs
Most AI agents not only conduct conversations, but also record and structure them in real time. As a result, every question, complaint or suggestion becomes a data point – often much more authentic than classic customer satisfaction surveys. It’s a natural, unobtrusive way of gaining information about what customers actually want, what annoys them or what they lack.
The problem is that many companies are not using this data strategically. They focus on solving the problem quickly, rather than on what can be learned from these repetitive interactions. The result? A missed opportunity to understand deeper customer needs and optimise products, marketing or internal processes.
From insights to product innovation
One area that can particularly benefit from the analysis of chat data is product management. If there are repeated questions about the size of a particular model, unclear fitting instructions or incompatibility with other accessories – this is no coincidence. It’s a signal that something in the description, design or the offer itself is not working as it should.
The data collected can help detect design problems, feature mismatches or simply information gaps. What’s more, for companies operating on a direct-to-consumer (DTC) model, chat analytics can de facto act as a ‘continuous market research study’ – much cheaper and more up-to-date than classic focus studies.
Marketing is the language of the customer, not the brand
The second clear beneficiary of AI agent analytics is marketing. Traditional advertising content often uses technical language that is consistent with brand identity, but not necessarily understandable or appealing to the end customer. Chat data shows how people really talk about a product, what terms they use, what associations they have.
This allows marketing teams to better tailor content – both for SEO and campaign effectiveness. It’s also a great tool for identifying language niches that are not yet addressed in a communications strategy, but which can increase brand visibility and message effectiveness.
Sales: new needs that no one has yet managed
Customer chats are also a goldmine of knowledge about what… isn’t there. Questions about unavailable products, missing colour options or features that customers assume should be available give direct insight into demand that remains unmet.
In the hands of sales and logistics departments, this is an opportunity to optimise inventory, expand offerings or better forecast trends. Data from AI agents can therefore support decisions about new product lines or even partnerships with manufacturers whose offers address untapped customer needs.
Customer service as a source of process innovation
There are also not insignificant lessons for the customer service teams themselves. Frequently repeated questions are indicative of weaknesses in existing self-service channels. If users are asking about things that should theoretically be available in the FAQs, it means that the content is invisible, unintuitive or simply does not address a real need.
Analysing chat logs makes it possible to identify such gaps and design more effective help paths, improve first-line chatbots or improve the structure of support portals. The result? Fewer requests, greater user satisfaction and improved operational efficiency.
From tactics to strategy
The biggest barrier to realising the potential of AI agent data is not technology, but mindset. Companies need to stop treating chatbots as an incident resolution tool and start seeing them as a constant, dynamic source of business insights. Especially since most modern customer service platforms – such as Zendesk, Intercom, Freshdesk or Salesforce Service Cloud – already offer built-in analytics tools and integrations with CRM or ERP systems.
This makes the cost of implementing analytics virtually non-existent – what matters is the decision to actually use this data. And this is what can determine who will build a sustainable advantage in digital commerce and who will merely automate the status quo.
