Two hundred open-text responses are readable. You can sit down, work through them, and come away with a real sense of what people are saying. A few thousand? By the time a human has read enough to spot a pattern, the moment to act on it has usually passed.
This is the quiet problem with open-text feedback. It’s the richest data your team collects, full of the why behind every score, and it’s also the hardest to use at scale. So it piles up. The volume keeps climbing, too. As Ryan Tamminga, Alchemer’s Chief Customer Officer, described it in our recent webinar, “Whether you’re adding ratings and reviews data, social media data, more survey context, inferred data from your digital channels… the volume is getting huge.”
It’s no surprise, then, that 42% of teams in Alchemer’s March Brand Tracker Study named analyzing larger datasets faster as one of the things they most want from AI.
In this post, we discuss exactly how Alchemer’s AI features help teams analyze feedback in a fraction of the time.
The following post references Alchemer’s new guide, Alchemer AI: Turning Customer Feedback into Faster, Smarter Action, and new webinar, From Hype to Impact: How Teams Use Alchemer’s AI Capabilities to Turn Feedback into Action.
Purpose-built AI turns piles of feedback into something actionable
This is the moment, as Tamminga put it, “where that giant pool of really valuable data starts to sift itself out into usable buckets.” Alchemer Pulse reads across thousands of comments and surfaces the trends, risks, anomalies, and outliers automatically. The unstructured pile becomes consistent, structured insight, and it does it the same way every time, which means you can trust a comparison from one month to the next.
The sorting itself has come a long way. “I remember when we started, there waswere word clouds,” Tamminga said. “Now we actually have it by observations, themes, by function and or department within a business, because it automatically knows what people talk about with products.” It separates the data into usable chunks you can actually build action on.
There’s a detail here worth pausing on. These aren’t generic, off-the-shelf models. Alchemer’s models are fine-tuned for feedback, which means they understand the context of how customers talk about specific industries.
Tamminga shared a favorite example from a customer who runs surveys for auto manufacturers. A respondent wrote about “the seat in the SEAT.” A general model sees a typo. Because Alchemer’s model understands automotive context, it “knows that SEAT is a brand and seat is also what you sit on,” so it correctly logs a positive comment about seat comfort against the right brand. That precision is the difference between an insight you can act on and noise.
Reviews and social feedback
For reviews and social feedback, AI Review Signals watches for shifts in sentiment before they escalate. As Tamminga explained, the tool helps you “predict whether this is a single location problem, or if it’s starting to become more of a regional issue, or it’s a national issue,” so “you’re actually on top of it before it becomes an issue.” It benchmarks your performance against competitors at the same time, so you see your position in context, not in a vacuum.
What this looks AI feedback analysis like in practice
Washburn & McGoldrick, a consulting firm specializing in philanthropic fundraising for higher education, adopted Alchemer Pulse to analyze open-text alumni survey feedback at scale. By automatically categorizing comments into themes like Student Life and Leadership, they cut analysis time by more than 50%, improved consistency, and eliminated bias—delivering twice the value in half the time for their clients.
Malwarebytes had been manually coding open-text feedback into the seven customer segments they assumed they had. With Alchemer Pulse, they uncovered 15 distinct segments. Those insights informed four new product launches and contributed to double-digit revenue growth in one of tech’s most competitive, mature markets. What Tamminga loves most about that story is what it gave the team back: “turning manual weekend work into a task that AI can do for you,” so people spend their personal time with their families instead of coding comments.
Continue learning
In our webinar, From hype to impact: how teams use Alchemer’s AI to turn feedback into action, Ryan Tamminga demos Alchemer Pulse and AI Review Signals on real feedback data, including the Pulse observations and highlights that turn thousands of comments into clear themes in seconds. Watch the webinar →
Our guide to purpose-built AI for customer feedback goes deeper on how Alchemer turns feedback into clear insight across surveys, reviews, and social channels. Read the e-guide →