In this article, Palak Dalal Bhatia, CEO and Founder of IrisAgent, explains how AI-powered automated tagging can provide a reliable source of customer feedback for Product Managers.
Product Managers are primarily responsible for identifying customer needs and the larger business objectives that their product or feature will fulfill. They articulate what success looks like for their product, and lead cross-functional teams to turn that vision into a reality. Driving success for their product starts by ensuring that they fully understand customer needs and ensuring that they take continuous feedback from customers for their roadmaps. In fact, product-led growth (PLG) companies adopt a customer-centric approach to product management and engineering – wherein active customer feedback is the primary input to the roadmap.
Product managers employ various techniques to solicit product feedback – ranging from customer advisory boards, surveys, informal interviews as well as BETA programs. Each of these approaches has its merits – however, these are prone to analysis bias and represent feedback as an ad-hoc event rather than a continuum or active feedback loop. These approaches thus often fail to capture the full spectrum of feedback and do not deliver real-time, reliable, and actionable insights.
Fortunately, Product Managers now can tap resources and technology readily available to build a closed-loop feedback mechanism with customers by tapping into support data and the power of AI-enabled automated tagging.
- Importance of tapping into support data – Customers are at their authentic best when seeking support. They provide unfiltered feedback on what is not working for them and also what they like or want to see in the product. Tapping into this feedback gives product managers insights into what is working well and what is not and where they need to focus on their roadmap. Often it may not be the next cool feature or the “shiny object”, rather it may be usability, service quality, or an enhancement that they may have overlooked or prioritized lower.
- Automating the feedback loop with AI-powered automated tagging – While tapping into support data makes logical sense, it is not possible to do that at scale and with consistency and accuracy. Manual tagging is time-consuming and prone to human error. This is where AI-powered tagging and insights derived from tagging data come to the rescue. AI-powered tagging and triaging helps product managers quickly view the top categories for customer issues, areas of improvement, and overall customer sentiment. These are critical inputs to roadmaps.
Thus by using AI-powered automated tagging with support data, product managers now can have a reliable source of customer feedback. This approach can in fact replace some of the time-consuming and often unreliable techniques mentioned above. This frees up time, delivering a more data-driven and objective customer feedback loop and eventually helps product managers drive effective product and business decisions.
Book a short call to get a free trial and get started with IrisAgent’s automated tagging and reporting within 24 hours: https://irisagent.com/get-a-demo.
About the Author
Palak Dalal Bhatia is CEO and Founder of IrisAgent. She has built predictive customer support and machine learning solutions. Palak is a technologist with a career spanning a decade as an entrepreneur, product manager, venture capitalist, and engineer. At Google, she led product management for stateful container applications and launched Google’s first Kubernetes marketplace. As a venture capital investor, she led diligence and invested in early-stage technology startups in the US and India in the enterprise SaaS space. Palak holds a BS in Computer Science from IIT Bombay and an MBA from Harvard.
