By Kristyn Corrigan, Principal and Practice Lead at AMS
In my recent webinar facilitated by the Manufacturers Alliance Marketing Council, I shared a candid look into how manufacturers are discovering deep, solution-agnostic customer needs, leveraging AI-powered Voice of the Customer (VOC) to strategically guide product and service innovation. If you couldn’t join us live, the webinar is now available on demand.
We received some great questions during the Q&A: read on for key takeaways!
For manufacturing companies that might be sitting on unstructured, internal customer data but don’t have a formal VOC program, what would be the first step you recommend to start getting value from that data?
This is a challenge I’m hearing more frequently from teams in B2B, especially those that are operating in silos. I think it starts with getting the right people in the room and on board with a VOC program, outlining why we need to look at the data and what it could potentially contain.
From there, you’ll want to transfer the data you have into a centralized location so that your team can go through and assess where there are gaps that you may need to supplement with external sources or traditional research.
How do you see AI used in gathering or discovering insights in the case of B2B industrial products and applications where there are less available online data on specific products?
The reality of B2B industries and manufacturing is that there’s less conversation about your products – online reviews, social media posts, etc. – as they can be very niche. I would challenge you to look at sources I mention in the webinar to see what insights might exist there. Even if it’s not a complete picture, it’s a good starting point.
I have seen teams start with online sources to understand what information lives there and let that inspire a more pointed, efficient VOC program. For example, having access to existing online data could mean you might not need as many interviews or site visits.
What are the risks of relying on general purpose AI models versus something purpose-built for VOC?
General purpose AI models such as ChatGPT are easy to gravitate to. Beyond obvious concerns such as security and privacy issues, you must remember that your competitors are also using all of these tools: when everyone has access to the same insights, it’s not differentiated at all. Generic AI tools aren’t capable of surfacing the detail needed for VOC insights, and they often default to the target value of the solution, rather than a customer need.
Has AMS explored using AI in other parts of the VOC process?
We’ve used it in a few different areas, information synthesis being one of them. Between summarizing a wealth of materials from clients or desk research on a product category, it’s been a helpful tool in that sense. Additionally, when we’re doing traditional qualitative interviews, AI has been successful in summarizing interview themes, which can be helpful when a client is unable to attend a live interview with a prospective customer. However, the most exciting application is the needs identification piece and how it can help add dimension to brand opinions and customer sentiment. We are always seeking ways to help our researchers be more efficient, from client proposals to final reporting.
Interested in harnessing AI to accelerate and enhance your customer insights? View the full webinar – now available to watch on demand.
Have industry- or organization-specific questions about AMS’s AI model for VOC? I’d love to connect! Shoot me an email or put a quick meeting on my calendar to chat.
