Bank & Credit Union Online Reviews with George Swetlitz of RightResponse AI
Host Bio
Brent Feldman is a cofounder of Matchbox Design Group, the St. Louis-based digital agency he helped launch in 2006 after beginning his career as a Marketing Associate at Ameriwood Industries. At Matchbox, Brent oversees both the Design and SEO teams, connecting brand, UX, and search visibility to help organizations earn trust online. His background blends technical and creative disciplines—starting in management information systems, then shifting into advertising and marketing, with formal training in web design and development. Brent has led award-winning work across a wide range of industries, including healthcare, higher education, architecture, packaged goods, retail, technology, and more, with a focus on anticipating client needs and delivering practical strategy (not just deliverables). He’s also the host of the Mix & Matchbox podcast, where he interviews marketers, operators, and partners about the tools and decisions that shape customer experience and reputation in a digital-first marketplace.
Summary
Brent Feldman frames this episode through a trust-and-decision lens—especially for banks and credit unions—where reputation isn’t a “nice to have,” it’s a daily proof point. He explicitly points to where the stakes are rising, noting that “reviews responses are incredibly important” as search behavior evolves, and he keeps returning to the idea that online reviews sit right at the moment of choice. In his words, “people are at the bottom of the funnel when they’re reading reviews,” and that’s why this topic matters.
George Swetlitz opens with the operating context behind RightResponse AI. Brent asks about George’s path from consulting to leadership roles, and George describes two “strains” in his career—consulting and operating—starting at McKinsey, then moving into an operator track that included running a Sara Lee factory, leading a startup division in Central Europe around 1990 (“a year, year and a half after the Berlin Wall fell”), and later returning to consulting work for financial services boards and CEOs. The pivotal operating chapter comes when he becomes CEO of a private equity-backed rollup in audiology and “bringing together about 220 audiology clinics.” That experience becomes the origin story: how do you drive growth across many locations when marketing channels are noisy and expensive, and the real win is when “people pick up the phone and call you”?
From there, the conversation turns into a clear reframing of “reputation management” as marketing leverage. George states it cleanly: “Review management is not a task, it’s an opportunity.” Brent reacts positively to that framing and ties it to his own agency experience—he calls out the operational reality that managing “all those profiles” and “all the reviews” is a stretch whether marketing is centralized or decentralized. But the point of the episode is that “saving time” isn’t the real headline. George argues the main goal is to “win customers” and “win in your market,” and Brent leans into that by describing how review responses become even more important as AI-driven discovery grows.
A key theme is that reviews and responses serve different audiences—and different purposes. George explains: “The, the review is for Google,” and that organizations collect reviews because volume and consistency are a “proxy for popularity,” which connects to visibility and “the map pack.” Brent affirms this and adds broader context about how platforms use ratings and review volume as signals. But George draws a sharp line on the response side: “Responses are for the people reading the reviews.” Not the reviewer, not the algorithm—the next potential customer who is scanning social proof and deciding who to trust. He summarizes the risk as a missed conversion moment: if you’re absent when someone is reading, “it’s a huge miss… it’s an unforced error.”
This sets up the episode’s core tension: AI skepticism versus AI used in a way that feels more real, not less. Brent asks directly how “AI driven review strategies” can build trust (especially in industries where trust is everything), instead of undermining it. George’s answer is that AI undermines trust when it produces generic, decontextualized language—essentially “it spits back some generic answer.” The solution he describes is not “make it sound human” at any cost; it’s make it relevant by grounding responses in business truth. He draws the contrast in a memorable way: “AI on its own can make you sound terrible,” but “AI that’s trained on your brand makes you sound great.”
What does “trained on your brand” mean in practical terms? George describes building “a set of facts about the business” that reflect brand positioning, products, services, and how the organization wants to communicate. Those facts then shape how AI responds to specific themes in a review. Instead of parroting back the review (the behavior George criticizes as “silly AI responders that just parrot back exactly what’s in the review”), the response can add useful, business-specific information—at scale—without requiring every human responder to invent or remember the right message each time. Brent validates the strategic advantage of this approach: personalization becomes less about targeting individuals and more about delivering the specific details that make a response feel real.
The episode’s most concrete illustration of “personalization at scale” is location context. George explains that some facts are “global” across an organization, while others are “hyperlocal” to a branch—facts the local manager knows best. He gives a detailed example: if a branch has repeated feedback about parking, the response can explain exactly where to park and how to access it. Brent highlights why this matters: the “automatic inclination” is to assume personalization means one-to-one targeting, but the real unlock is relevance—“the things that are relevant to them that makes it feel more personal.”
The conversation then escalates the importance of this channel with a blunt comparison: “More people read reviews than visit websites.” The implication is operational and marketing-driven: if your website contains the best articulation of your value, you can’t assume people will see it there. You have to “bring the website to them” in the places they’re actually evaluating you. Brent repeatedly returns to that bottom-of-funnel idea as the episode’s organizing principle: reviews aren’t a top-of-funnel awareness tactic; they’re the “conversation” happening right before the call.
On the analytics side, Brent pushes on an operator’s question: ratings alone don’t tell the story. He says, “ratings alone, just they don’t tell the full story necessarily,” and asks what “hidden insights” leaders should pull from sentiment data. George’s answer is a Voice-of-Customer approach that disaggregates review text into topics and sub-sentiments. Reviews can be mixed (“The teller was great, but the office was dirty”), and the star rating becomes an “average of sub ratings.” Since platforms don’t provide sub-ratings, George describes analyzing reviews phrase-by-phrase to determine (1) whether each phrase is relevant to the business, (2) which topic it maps to, and (3) whether sentiment is positive or negative (and how strong). Rolled up across many reviews, leaders can see performance by topic—e.g., strong “products” sentiment but weaker “service level”—and compare locations that share the same overall rating but have completely different underlying drivers. The point is operational: once structured, review data can be “operationalize[d]” by leaders responsible for branch performance.
Brent also probes the future: how CMOs might leverage this as AI gets better over the next five years. George’s roadmap points toward making insights more actionable and reducing setup burden: “AI onboarding” that asks questions and configures settings automatically, and output that delivers trends and peer comparisons without requiring managers to “go into our system.” He notes a practical constraint: adoption depends on model quality and cost.
Two additional threads round out the episode:
- Competitive context: George explains why local competitive environments can make two similar locations perform very differently. He describes a “competitor analysis” capability where organizations can rank in-market, analyze competitor sentiment, and understand what competitors are excelling at. Brent calls this “amazing,” and George notes agencies sometimes use this before pitches to walk in with a sharper view of the competitive environment than the prospect has.
- Compliance and aggregation: In regulated environments, George argues for a review layer before publishing. He cites a healthcare example—a “PHI tracker” that flags protected health information and prevents it from appearing in responses—and suggests similar mechanisms can apply for banks and credit unions with personal information. He also sees the future in aggregating sources: reviews from platforms, survey verbatims, Better Business Bureau, Trustpilot, and even chat logs (“We… analyze chats for clients”), bringing the “whole universe” into one place.
The closing advice is intentionally simple. Asked what he’d tell a CEO about reputation in the AI era, George says: “take it seriously, because it could be your biggest source of growth.” Brent reinforces the long-game economics of organic trust, emphasizing that organic growth is “critical,” especially compared to “paying for clicks.”
Q&A
- I run a multi-location business—should review management be treated like a checkbox task or a growth channel?
Treat it like an opportunity, not a chore. Review visibility can influence whether you show up, and review responses shape whether readers trust you enough to choose you. - Do Google reviews really affect map rankings, or is that overblown?
Review volume is treated as a proxy for popularity, and that visibility can matter for where you appear in the map pack. Consistent review collection is positioned as part of competing locally. - Who are review responses actually for—the reviewer or everyone else?
Responses are positioned as being primarily for the people reading the reviews later, not just the person who wrote the review. - If someone is reading reviews, how close are they to buying?
Reading reviews is framed as bottom-of-funnel behavior: the person has already researched and is deciding who to call next. - I’m worried AI replies will feel fake. How can AI help without undermining trust?
AI undermines trust when it produces generic, non-specific language. The approach emphasized here is grounding AI responses in business-specific facts and brand messaging, then keeping a human review step. - What’s the difference between generic AI replies and business-specific AI replies?
Generic AI tends to echo the review in vague language. Business-specific AI is guided by a curated set of facts about your organization and can add relevant details that reflect how you want to position your brand. - What does personalization at scale actually mean for a bank or credit union with many branches?
Personalization can be based on shared “global” facts across the organization plus “hyperlocal” facts for a specific branch—details like local logistics or branch-specific context. - Do I need to personalize down to the individual customer for it to work?
Not directly addressed here as an objective. The emphasis is that relevance is what makes it feel personal—local context and useful specifics can matter more than one-to-one targeting. - Should we respond to every review, even five-star ratings with no written text?
Some reviews can be posted with less scrutiny, especially when there’s no text. For most responses, a skim-and-review layer is described as common before publishing. - How do I stop replies from sounding like templates?
Avoid repeating the same response structure and focus on adding useful, business-specific information tied to what the review mentions, instead of generic “thanks” language. - How can I get operational insights from short, messy review text?
Break reviews into topics and assess sentiment phrase-by-phrase for relevance and positivity/negativity. Rolled up, this can surface topic-level strengths and weaknesses that a single star rating hides. - Two branches both have a 4.7—how do I know what’s actually driving that rating at each location?
Disaggregate the rating into topic-level sentiment so you can see whether each branch is strong or weak for different reasons (e.g., service vs. products), then address the specific drivers. - How can competitor reviews help me improve, instead of just making me feel worse?
Use competitor analysis to understand local rank and what competitors are excelling at, including sentiment patterns in their reviews, so you can diagnose why performance differs by market. - What’s the best way for an agency to position review response work to clients?
Position it as a marketing and growth lever, not just a time-saving task. The emphasis is that review readers are making decisions, and responses influence trust and conversion. - How do regulated industries balance compliance with authentic communication in review responses?
Keep a human review step before publishing and use safeguards that prevent sensitive information from being included in responses. - Is it useful to combine reviews with other feedback sources like surveys or chat logs?
Yes. The approach described is aggregating multiple sources—reviews, survey text, and chat logs—so leaders can compare issues across channels and understand the full picture. - Are Yelp and Google reviews basically the same audience?
They can be different. Different platforms attract different behaviors and complaints, so understanding the context of the platform matters when interpreting feedback. - If I’m a CEO, what’s the single most important mindset shift on reputation right now?
Take it seriously as a major source of growth, especially because review visibility and bottom-of-funnel trust can drive organic demand.
Transcript
[00:00:00] Brent Feldman: Hi, I am Brent Feldman, and we are back with another episode of Mix & Matchbox. Today I am George, joined by George Swetlitz. He is the co-founder of RightResponse AI. Hi George. How are you doing?
[00:00:12] George Swetlitz: How you doing, Brent? Great to be here.
[00:00:14] Brent Feldman: Yeah, definitely. Great to have you. Um, thank you so much for, for, uh, joining us and as usual, uh, I have a first question, which is really kind of based on your background and experience and kind of how you got to where you’re at.
[00:00:27] Brent Feldman: But, um, it, it’s interesting you started your career at McKinsey. You were the president of Sarah Lee in Central Europe, and then you have some tech and healthcare leadership positions that sprinkled in, uh, right before getting into RightResponse AI. So how did all of this kind of come together?
[00:00:46] George Swetlitz: Sure, sure.
[00:00:47] George Swetlitz: Great question. So, as you said, I’ve had a long career and it’s really been broken down into two. Broad strains. One is around consulting and the other is around operating. And I’ve rotated back and forth between them. So as you said, I started at McKinsey and really enjoyed the consulting environment, uh, being.
[00:01:08] George Swetlitz: In front of different companies all the time is a real learning experience because you’re operating from first principles and you have to decide how you apply those to every company. I’ve done a lot of consulting over the years, both with large firms and on my own. But then I also, after business school, I decided I wanted to be an operator and started, uh, joined Sara Lee and ran a factory and, uh, then ended up going to Europe.
[00:01:34] George Swetlitz: And, uh, was asked to lead the startup of a division in central Europe. And so this was in, uh, this will date me, but this was in, uh, 1990, which was a year, year and a half after the Berlin fall, Berlin Wall fell.
[00:01:49] Brent Feldman: I,
[00:01:50] George Swetlitz: I was thinking
[00:01:50] Brent Feldman: of a dynamic time for Central Europe, that’s for sure.
[00:01:53] George Swetlitz: Yeah. Yeah. So it was really a crazy experience being out in Poland, actually living in.
[00:01:58] George Swetlitz: In, in Warsaw during that time and, you know, helping people understand what is what, what is the transition from socialism to capitalism look like for them personally. Yeah. And, uh, and, and that was a, a tremendous experience. And then I left Sarah Lee came back and went back into consulting. And then at that time did a lot of consulting for financial services.
[00:02:22] George Swetlitz: Probably about six, seven institutions in the area of broad. Overall performance improvement, working for boards and CEOs. Um, and, and then after doing that for a while, decided, you know, I, time to get back on the operating side. And so ended up, uh, becoming the CEO of a private equity backed roll rollup in audiology.
[00:02:48] Brent Feldman: Ah,
[00:02:48] George Swetlitz: and in that business ended up, uh, bringing together about 220 audiology clinics. Uh, and, and it was really, that’s the origin story of RightResponse AI in the sense that as the CEO of this large clinic group, how do you grow? And, you know, you can grow through Facebook, social ads, paid social, and you know, Google paid ads, but you really wanna grow because people pick up the phone and call you.
[00:03:24] Brent Feldman: Yeah.
[00:03:25] George Swetlitz: So understanding how that happens, the dynamics of why people choose the companies they choose took us down this, this road of review management, the ecosystem of review and reputation. And at that time, we really weren’t able to find a company that did the things that we wanted to do.
[00:03:47] Brent Feldman: Mm-hmm.
[00:03:48] George Swetlitz: Never really found it.
[00:03:49] George Swetlitz: You know, there are the. Typical players, the BirdEye and the podiums and the review trackers and all those guys. And we talked to all of them and we used them, but they never really did what we wanted. And we ended up exiting in 2022. Uh, and then chat, GPT came out 23 and uh, I got my team together and said, you know, I think maybe with AI we could solve this problem.
[00:04:16] Brent Feldman: Mm-hmm.
[00:04:17] George Swetlitz: And we did. And we built the company that we wish we would’ve had when we were running this rollup. And so that’s, that’s the story.
[00:04:28] Brent Feldman: Yeah. You’re a, as a CEO I’m sure you know, like that, that does make you kind of perfectly poised to be able to, you know, see some of the gaps in the marketplace and, and then, uh, that, that is fun to be able to say, oh, I wish I had this tool, and then go build it.
[00:04:42] Brent Feldman: And, uh, you know, sort of realize that that dream. Well, actually you touched on alpaca audiology and that, uh, did, as you mentioned, grew to two or were or included 220 plus locations. Correct. That’s, uh, uh, very significant. Um, so what kinda leadership insight from that experience kinda shaped most how you think about scaling reputation management?
[00:05:07] Brent Feldman: Because dealing with all those profiles, all the reviews has to be a, you know. And I know this personally as a, you know, working in a marketing firm. It is a challenge Like that is a, it’s a stretch whether you’ve got centralized marketing or decentralized marketing, but yeah, what did you kinda learn from it?
[00:05:23] George Swetlitz: So I think that the key, the key insight, the key thing that a leader of a multi-location group needs to understand or realize in integrate is this notion that it’s not a task. Review management is not a task, it’s an opportunity.
[00:05:45] Brent Feldman: Hmm.
[00:05:45] George Swetlitz: It’s potentially your most important marketing, direct marketing tool.
[00:05:53] George Swetlitz: And, and when you think about it that way, when you say, this is an opportunity for me to grow organically, then it sets a whole series of things into motion, a whole set of things. But most reputation management. Firms talk about it in terms of saving time.
[00:06:16] Brent Feldman: Hmm.
[00:06:17] George Swetlitz: Right? As if it’s a task that you need to save time.
[00:06:21] George Swetlitz: Of course, everyone wants to save time, but that’s not the most important thing. The most important thing is to win customers to win in your market. And I spend a lot of time talking to agency leaders. As they onboard, as an agency, we do. We do white label agency and so I talk to a lot of agency leaders and getting them to understand that this is a real value added opportunity for them to bring something new and different to their, to their clients is really one of the key insights.
[00:06:54] Brent Feldman: Yeah, it, it’s, it’s also, it’s very interesting that as, uh, I, I think you’re totally right that this is an opportunity in interact with your audience and, and create, you know, kind of better content. But especially as AI search develops, uh, a reviews responses. Are incredibly important. Um, and that I, it seeming like you’re, you’re fitting in at a point in the marketplace that actually is, is very necessary.
[00:07:20] Brent Feldman: Uh, and it’s gonna become even more so in the very near future too.
[00:07:24] George Swetlitz: Right. Right. You know, there’s no one closer to the bottom of the funnel than someone who is actually reading your reviews.
[00:07:34] Brent Feldman: Hmm.
[00:07:35] George Swetlitz: They’re reading your reviews because they’re at the bottom of the funnel. They did some research. They found you.
[00:07:40] George Swetlitz: They’re trying to decide who am I going to call? And so if you are not engaged in that conversation, when that perspective client is there, it’s a huge miss. It’s, it’s just, it’s, you know, it’s an unforced error.
[00:07:59] Brent Feldman: Yeah.
[00:08:00] George Swetlitz: And, and that’s kind of, that’s what we try to teach our, you know, our customers.
[00:08:07] Brent Feldman: Yeah, definitely.
[00:08:08] Brent Feldman: I, I, and it’s interesting, not that he invented ratings or reviews, but I think, you know, Bezos, uh, knew within Amazon that ratings and reviews, you know, were even critical. And there was so much, uh, data that came out of that, you know, from understanding that even things with. Lower reviews versus no reviews actually perform better on the platform.
[00:08:28] Brent Feldman: And so, uh, I, I think you’re totally right. And, and I, I love that actual piece of, uh, you know, uh, kind of advice or insight that essentially people are at the bottom of the funnel when they’re reading reviews. ‘cause I think that is so true. That is right before the, the, the decision, the, you know, kind of making that purchase, uh, choice that, uh, they are, they’re basically that they, they are, they’re, they’re the closest they could be to the bottom of the funnel.
[00:08:52] George Swetlitz: So I think what happens is people think about review response as just something that I have to do because somebody wrote a review, and what, the way I think about the way we think about it and RightResponse AI, the review and the response are for two completely different audiences.
[00:09:12] Brent Feldman: Mm-hmm.
[00:09:13] George Swetlitz: Right. The, the review is for Google.
[00:09:17] George Swetlitz: Right. That’s why you collect reviews. You collect reviews for Google because the fact that you have reviews and a lot of reviews is a proxy for popularity. Mm-hmm. So you, you, if you wanna be high on the map pack, you have to have reviews and we have a lot of tools that help you understand what your competitors are doing and what do you need to do in order to win versus your competitors, all of those things.
[00:09:41] George Swetlitz: But that’s why you collect reviews. Responses are not for Google. Responses are for the people reading the reviews. Mm-hmm. They’re not even for the person who wrote the review. They’re for everybody else who’s reading about your business from somebody else’s perspective, and you wanna talk to them as if you were there.
[00:10:02] Brent Feldman: Yeah.
[00:10:02] George Swetlitz: And that’s the purpose of the review response. So every piece of this is for a different element. And that’s what I was saying before about. But once you start to think about this not as a task, but as an opportunity, then you start saying, well, how do I take advantage of that? What? What are the different components and how do I think about engaging in that review ecosystem?
[00:10:25] Brent Feldman: Definitely. Yeah. Cool. Well, uh, sorry, I’ll try to get back to the questions. I, I can’t help but dig deeper here. But, um, one of the things that, you know, we’re focusing on in terms of like, you know, industry is definitely banks and credit unions, and I think that, um, as a whole, they are usually, you know, they live and die by trust.
[00:10:44] Brent Feldman: Um, how can AI driven review strategies actually build trust rather than potentially undermining it?
[00:10:54] George Swetlitz: So the first question is, why does AI undermine trust? And so AI undermines trust because in the absence of personalizing information, it spits back some generic answer based on what everyone else has written, you know, in the last whatever period of time.
[00:11:21] George Swetlitz: And so AI by itself is not necessarily helpful. AI is helpful if you are able to extend your brand, right? So if, so, if so, think about if, if, if you’re a banker, credit union and there are all sorts of things that you think make your credit union or bank special. It’s products, it’s services, it’s the way you engage with customers.
[00:11:49] George Swetlitz: It’s, it’s whatever. Whatever you decided as a marketing organization makes you special. Okay, so you, you’ve determined that. Now the question is how do you, it’s scale. Get the word out. So if you think about a review and you’re responding to these reviews manually. You have a room full of people who are typing incessantly, trying to like write these reviews, but now you have to teach them about your brand and you have to teach them about all these advantages and you have to teach them what to say.
[00:12:27] George Swetlitz: Mm-hmm. Because they don’t know. And that’s difficult. That’s what we try to do at Alpaca when we were free at 2, 220 locations. How do we get the word out? Well, it was a mess because you train people, they don’t say the right thing, they say the wrong thing. They don’t understand, you know, it’s, it’s, it’s humans.
[00:12:46] Brent Feldman: Mm-hmm.
[00:12:48] George Swetlitz: AI is really good at understanding relevance, and so when we talk at, at RightResponse AI, and we’re different than everybody else at RightResponse AI, when we talk about highly customized review responses, what do we mean? We, we mean that you, and we help through ai, but we build. A set of facts about the business that reflect your brand, that reflect your marketing approach.
[00:13:13] George Swetlitz: And so if somebody says something that’s relevant to the way that you’re treated by a commercial banking rep, then you could say, if somebody talks about this, add that we do this. Right. So it’s very relevant to your brand, it you’re, you’re telling AI how to respond to that type of question at scale.
[00:13:35] Brent Feldman: Yeah.
[00:13:36] George Swetlitz: Now you still wanna have a human reviewing it, but they don’t have to think about what, what are all the points that were made in that review and how do I respond to each one? AI is great at that.
[00:13:48] Brent Feldman: Mm-hmm.
[00:13:48] George Swetlitz: So AI helps you scale your brand voice, your marketing voice, and that’s the difference. AI on its own can make you sound terrible.
[00:14:03] George Swetlitz: AI that’s trained on your brand makes you sound great. And that’s, that’s what we do at RightResponse AI is we fuse those things together.
[00:14:15] Brent Feldman: That’s awesome. Well, you’ve actually, you’ve talked about another, uh, element too, which is interesting, which is personalization and, uh, you know, and, and as those being, uh, a, a, you know, kind of strategic advantage, um, making sure that they’re actually really, you know, tailored messages.
[00:14:32] Brent Feldman: Um, what would personalization at scale actually look like for, you know, let’s say the, a regional or national financial institution?
[00:14:43] George Swetlitz: So there are certain que there are certain things that people say that are applicable to the entire organization, right? The way you know the, the way the products you have and why they’re special or whatever, it’s then there are certain things that are local.
[00:15:00] George Swetlitz: So you have a branch that’s in a downtown area in a town, and it’s difficult to get parking out front. There’s a lot of reviews that say, I had a hard time getting parking, and that was very frustrating ‘cause I needed to get a deposit, blah, blah, blah, blah, blah. All these things. So you can create facts about your business that are global, that apply to all locations, and you can create facts that are hyperlocal to a single branch and who knows that.
[00:15:30] George Swetlitz: But the branch manager, the branch manager knows those things. And can create those facts for that branch. So when somebody says, I had no time, hard time finding parking, AI says, Hey, next time you come, that’s, I’m sorry about that, but next time you come, we have additional parking in the back of the building and you enter off a beach street and there are 47 spaces and two accessible spaces, and sorry about what happened.
[00:15:54] George Swetlitz: That is a response, right? That’s personalization at scale.
[00:16:02] Brent Feldman: That’s cool. I, I, I think that’s really helpful for contextualizing personalization for people because I, I feel like the, uh, the automatic, um, inclination, I think is that it’s going to be personalization down to the individual and it doesn’t have to get there, you know, and you’re right to like, you know, center on like locality.
[00:16:22] Brent Feldman: Uh, that, that could be helpful. There’s lots of different relevant points that, uh, could occur around that individual that don’t necessarily mean you’re targeting, you know, X person Exactly. But it’s the things that are relevant to them that makes it feel more personal, not just, you know, oh, uh, thank you.
[00:16:38] Brent Feldman: We generally have parking at our locations, you know, and, uh, and that can be really helpful for just people to picture what personal personalization actually looks like.
[00:16:47] George Swetlitz: Right. More people read reviews than visit websites. So if you’ve, you know, every bank, every company spends a fortune on their website, but more people are going to the, and reading reviews than going to the website.
[00:17:05] George Swetlitz: And so what that means is that you have to bring the website to them, all the things that you talked about on your website about why you’re the best. You have to bring that to them when they’re engaging. And so. Remember we talked about before about how the response is not for the person, it’s for the, it’s for everybody.
[00:17:28] George Swetlitz: So you have a elderly couple that reads about the credit units. Oh, but I can’t get parking there, and so I’m not gonna go there because I can’t, I can’t walk. Well, but when it says the parking’s in the back and there’s accessible spaces, they’re like, oh, there is parking. I can go there. And, and more than that, they’re actually engaging with those reviews.
[00:17:55] George Swetlitz: This sounds like a friendly place. I’m gonna go there.
[00:17:58] Brent Feldman: Mm-hmm.
[00:17:59] George Swetlitz: And it’s not fake. And there’s still, you know, you, most of our clients. Like skim the responses.
[00:18:07] Brent Feldman: Mm-hmm.
[00:18:08] George Swetlitz: They have the ability to automate some of them, and they don’t necessarily have five star review. Do you really have to look at it a re you know, a, a response for a review that doesn’t have any text?
[00:18:17] George Swetlitz: Do you really have to look at it? So some things you can just post, but, but most of our clients will review or read those responses before they post them to make sure, so you’re not, it’s not fake. It’s very real and it’s actually better. It’s a better way of bringing your brand to customers than these silly AI responders that just parrot back exactly what’s in the review.
[00:18:45] Brent Feldman: Mm-hmm.
[00:18:45] George Swetlitz: You know, it used to be templates, right? It used to be you’d create a response template.
[00:18:50] Brent Feldman: Yeah.
[00:18:50] George Swetlitz: And so every third. Response looked exactly the same.
[00:18:54] Brent Feldman: Thank you for reaching out. You know,
[00:18:57] George Swetlitz: and, and most honestly, most banks and credit unions still do that.
[00:19:01] Brent Feldman: Yeah.
[00:19:01] George Swetlitz: It’s templates. Um, they have this negative thing about ai, which, you know, it, generic AI isn’t necessarily, I think, a benefit to anybody.
[00:19:11] George Swetlitz: Um, but, but, but they’re not at the cutting edge, you know, they’re not really engaging in the opportunity.
[00:19:20] Brent Feldman: Absolutely. Wow. Well, you know, in general, I, I, I don’t know if you agree with this sentiment, but I, I, I, I think you might, but ratings alone, just they don’t tell the full story necessarily. And, uh, what would you say in your opinion are, are maybe some of the more overlooked, hidden insights inside of that customer sentiment data?
[00:19:42] Brent Feldman: And how should, um, you know, these leaders. Act upon that information they receive from this platform. ‘cause it’s not just about, you know, the sort of the five star review with no text. Uh, people are, you know, putting in deep thoughts and feelings sometimes into these reviews. So, so what, what is the subtext that can be gleaned from all of that?
[00:20:01] George Swetlitz: Yeah, it, it’s a very, it’s a very interesting problem because reviews are so complicated. They’re so short, but they’re so complicated. The teller was great, but the office was dirty. You know, I mean, it is just, is this a positive review or a negative review? And, and so when you look at a rating, the rating is really an average of sub ratings if you think about it.
[00:20:28] George Swetlitz: But Google doesn’t do sub ratings, right? You, you give a, a root a star on the overall. And so what we do is we. We create topics so when someone signs up, we create a set of topics. This topic might be for a bank, it, you know, it might be products and services, it might be, uh, service level, you know, how you engaged.
[00:20:57] George Swetlitz: It might hours, and you know, ease of use. Things like that might be kind of categories. And then within that, there are specific topics. And every large organization, banks, credit unions, they all have scorecards. They all, they all have a way of thinking about their business. They, they, they measure themselves against certain things and they can take those exact things and they can put them into our topic framework.
[00:21:24] George Swetlitz: And then what happens is when a review comes in, we essentially look at that review phrase by phrase and we say, is this review relevant? To this business. ‘cause sometimes someone might say, I was down the street at fifth Third and it was horrible. Well, that’s not relevant. You know, that’s not a negative sentiment around this business.
[00:21:46] George Swetlitz: So we look at that and say, no, that doesn’t have anything to do with, and we throw it out. And so we look at every phrase and we say, is this relevant to the business? If it’s relevant to the business, what topic is it associated with? And then we assess whether it’s positive or negative. And the strength.
[00:22:03] George Swetlitz: Now you get a lot of reviews. You roll all of that up. And you can then say, well, you might have had a 4.7, but it’s really a five on products, but a four on service level.
[00:22:18] Brent Feldman: Hmm.
[00:22:20] George Swetlitz: And so you can visually see that, and then not only can you see that, but you can compare locations. So you might have two different branches that are both 4.7.
[00:22:33] George Swetlitz: They could be 4.7 for very different reasons. And you could just go to that branch manager, say you’re a four seven, you should be five. But you’re better off going to the branch manager saying you’re a four seven because you’ve got an issue with your tellers.
[00:22:48] Brent Feldman: Mm-hmm.
[00:22:49] George Swetlitz: And that’s, that’s, those are the insights, right?
[00:22:53] George Swetlitz: So when we talked before about every element of the review, ecosystem has a different purpose. The purpose of analyzing the unstructured data and reviews is so that you can operationalize it. You have someone who’s responsible for the operations of the branch.
[00:23:08] Brent Feldman: Yeah,
[00:23:08] George Swetlitz: that’s a job, and they can use the assistance of understanding where every particular location is excelling and where every location could use some help.
[00:23:20] Brent Feldman: Yeah, definitely. Um, that’s cool. And I, I guess probably the primary way people may be getting this feedback where the people see the forum to engage with the brand is really the review section. Sometimes, you know, they’re, they’re not always being served a survey or if it does come to their inboxes, it just another thing that gets, you know, deleted pretty quickly.
[00:23:40] Brent Feldman: But, uh, you know, reviews get engaged with so quick, so it’s, it’s. I, I guess not just important, but imperative that they start peeling out this data and sussing out what is exactly, you know, uh, going right and going wrong with the organization based on some of these things that, that can, can be real insights.
[00:23:58] Brent Feldman: Yeah.
[00:23:58] George Swetlitz: Right, right. Well, there’s nothing better than. Disaggregating real written reviews that people wrote and making sense of it. You send a survey, you’ve teed up the categories, you’ve teed up the questions. People you know, they don’t necessarily, you know, they start doing it. They didn’t realize there were 20 questions.
[00:24:23] George Swetlitz: They thought maybe there’d be three. They’re zooming through to get done. So there’s some, you know, there’s some potential issues with surveys. But there’s fewer issues with reviews. People wrote what they wanted to write and you’re just structuring that unstructured data. Yeah. And gleaning the insights.
[00:24:42] George Swetlitz: And it’s a very, you know, it’s, it’s a, there’s a lot of truth in those reviews.
[00:24:47] Brent Feldman: Absolutely. Ooh. Well, actually, as you touched on, you know, disaggregating, uh, ratings, uh, you know, I think, uh, I’ll, I’ll jump to this one, which is like a, a as AI is kind of getting, you know, smarter at disaggregating, you know, the ratings and surfacing more, kind of like that nuance sentiment.
[00:25:05] Brent Feldman: How do you see CMOs. Leveraging this information, not now, but in five years from now. Because, you know, we’re at a point where, I know you mentioned, you know, like chat, GPT came out and you’re finally able to realize, you know, making this product. But as you see AI developing, because I’m sure you have, even from when you started this product where it is now, what, what do you see either the information, um, you know, being, uh, you know, of, of, of higher quality, of, you know, more sort of like nuanced.
[00:25:35] Brent Feldman: You know, sort of like information coming to the service or, uh, creating some sort of like different insight altogether and how will it be potentially leveraged?
[00:25:44] George Swetlitz: Yeah, I think, I think the, and where we, you know, we’re a small company, there’s only so many things we can do at one time.
[00:25:51] Brent Feldman: Sure.
[00:25:51] George Swetlitz: But in our roadmap of where we want to go, it, it’s really taking those insights that we develop that are now in tabular format and.
[00:26:05] George Swetlitz: Providing it back to a manager of a location in a very simple way to say, Hey, this, these are the, this is what’s going on. These are the trends. This is where you stand against your peers, you know, making it very actionable.
[00:26:21] Brent Feldman: Cool.
[00:26:22] George Swetlitz: So that nobody has to ever, our, our objective eventually is that nobody ever has to go into our system and set it up.
[00:26:31] George Swetlitz: We’re working on AI onboarding so that when you sign up to our platform, it just, AI asks you questions, has a conversation with you, and then sets all the settings in the background. The same thing would be on the output. You, you’d never have to go in to analyze it, does all of that for you. And, uh, and gets it to you and, and, and, and the problem is it’s all, you know, it’s all around cost.
[00:27:03] George Swetlitz: It’s, it’s the quality of the LLM, the cost of the LLM. And as prices continue to go down, you reach a certain point at which somebody says, you know what? I’m willing to pay for that. And so, we’ll, you know, that, that’s what I see is that over time. Uh, as costs hopefully continue to decline and quality continues to increase, that you reach a point at which people say the quality of these outputs is such that I’m willing to pay for them.
[00:27:30] Brent Feldman: Yeah. Yeah, that, that’s great. Alright, well, and you actually already kind of, um, answered one of the, the future questions here too, which is like of, you know, by your roadmap, it does seem like, you know, analyzing some of these, you know, larger trends. But actually what it just made me realize maybe what it can kind of attack on there too, or wonder if this is like, you know, maybe in the forecast or, or an idea of future o opportunity and possibility is, what is that?
[00:27:55] Brent Feldman: Data mean on a more macro level to the market in general. This is on a organization by organization level that you’re able to provide trends, insights, but theoretically the more that you concentrate within an industry that can give you an even bigger, broader picture of like, oh, where are people running into challenges with like, you know, local organizations or, you know, how are they, you know, um, in sort of responding to these things en mass.
[00:28:21] Brent Feldman: But, um, do you see that as being, you know, trends forecasting that you’ll be able to do in the future as well?
[00:28:26] George Swetlitz: Yeah, so one way that I like to think about that is, and, and this go again, this goes back to my alpaca days. It, it, every business exists in its own competitive environment, right? And so I could have two clinics that on paper looked exactly the same.
[00:28:48] George Swetlitz: One was excelling and the other was failing. And the one that was excelling was because they were the best in their market doing what they were doing. And the other one had competitors that were just doing a better job than we were. So on our internal measurements, they were the same. But performance wise, they were very different because of the local competitive environment.
[00:29:09] George Swetlitz: And the same thing is true. Banks and credit unions. You might be in a market where there’s a lot of credit union competitors and they’re really good, and so it’s a harder competitive environment from a different branch somewhere else that’s in a market where there’s not as many or they’re not as good, and so and so we have a section called competitor analysis where you can understand you, you can understand where you rank, and then you can.
[00:29:41] George Swetlitz: You can learn about your competitors and what are they excelling? You can do the sentiment analysis on their reviews so you can learn what people are saying. So you can start to understand why is it that we’re performing this way in this market?
[00:29:55] Brent Feldman: Mm-hmm.
[00:29:56] George Swetlitz: And that’s a different angle. It’s, it’s, you know, com, it’s competitor awareness that is a, you know, a second level, you know, derivative of.
[00:30:10] George Swetlitz: Reputation management. Yeah. And it’s kind of the next level. And again, I, we, we, we have some customers that use it most dope because it takes, it takes, it’s, you still have to work at it and think about it. And one of the things I’ve learned building this business is that people don’t wanna do that. You know, people want things done for them.
[00:30:29] George Swetlitz: And so that will never, that won’t really take off until we do all the heavy, you know, we, we do a lot of heavy lifting, but not enough. Yeah. So we need to do more heavy lifting, but to, for. Uh, we have a lot of agencies, for example, that before they go on a pitch, they’ll go in and they’ll use those tools to understand the client that they’re pitching and the competitors, and they’ll go in there with a better understanding of the competitive environment than the company has.
[00:30:56] George Swetlitz: Using these tools, just using the information that’s out there, people’s reviews, it’s really, it’s amazing.
[00:31:03] Brent Feldman: Yeah. That’s amazing. That is cool. Well, actually, alright, just really quick, gonna hop back into, you know, sort of banks and credit unions only. Sure. As I feel like there’s, uh, there’s a topic that I’d really, you know, love your idea or insight into, especially because of, you know, you, you had mentioned, you know, kind of like these AI responses and potentially how they’re, they’re getting to people, but, uh, in, in, you know.
[00:31:24] Brent Feldman: In definitely industries where maybe regulators more, you know, more heavily scrutinized communication. How can organizations, you know, balance both the compliance need as well as the need for authenticity in their communication with customers via the response?
[00:31:44] George Swetlitz: That’s, that’s why I think it’s important that, uh.
[00:31:48] George Swetlitz: Especially in the regulatory environments that people review before publishing. So it’s really, it’s, it’s really a way of, of, uh, you know, extending the brand through the response, but then making sure what you’re writing is actually
[00:32:09] Brent Feldman: true.
[00:32:12] George Swetlitz: Yeah. And so it’s, to me it’s, you know, the, in these highly regulated environments, you just have, you know, you’re, you’re able to actually spend less time and get better content.
[00:32:22] George Swetlitz: But there, but there needs to be a review layer.
[00:32:25] Brent Feldman: Yeah.
[00:32:25] George Swetlitz: So, for example, in the medical side, we have a PHI tracker. Mm-hmm. And they can turn that on. And what it does is it, it reads the review and has a definition of what PHI is. And then it identifies all the instances of PHI alerts you that there’s PHI in that review, and then makes sure that none of that PHI shows up in the response.
[00:32:48] George Swetlitz: So there’s a series of AI agents working in the background to do that for you. And similar things could be done on the banking side around personal information and things like that. And so, you know, there are always ways to, you know, use ai. Around regulation, you know, around compliance to help you. You know, do things in the right way and not get yourself in trouble.
[00:33:15] Brent Feldman: Yeah, definitely. Uh, well, you did mention earlier about how more people want it, more things done for them, and uh, and I do, I totally, I know that to be the case as well. Um, because. You know, the developments in ai, hopefully maybe at a point in time, I don’t know if you see this as being possible, but it being explicitly trained on all the compliance, regulation, necessities, you know, for whatever particular industry.
[00:33:42] Brent Feldman: And then being able to apply those facets to its response in general. I, I, I am completely with you on the human response or at least review being, you know, sort of best before publishing, but, uh, you know, do you see that as a, a future potential?
[00:33:59] George Swetlitz: I, I think it’s just a question of having a client that wants to spend the time to build that compliance component.
[00:34:09] Brent Feldman: Yeah.
[00:34:09] George Swetlitz: That’s what we did on the, I mean, my background was on the medical side anyway, so when we, we built it, we knew we would do a lot of healthcare related things and so we built the PHI kind of in a very rigorous way. Um. Uh, we would just need the same level of intensity of thought in that area to, to build it for banks and credit unions.
[00:34:30] Brent Feldman: Cool. Well that’s, that’s helpful. Thank you. Uh, alright. Uh, I guess, um, knowing that banks and credit unions are also leveraging, you know, maybe internal satisfaction surveys as well, you know, potentially NPS net Promoter score, uh, data. Um, is this a. Something that you feel like in the future, those things will always, theoretically hold importance or value.
[00:34:56] Brent Feldman: Do you see a point in the future where reviews that they’re getting through other resources or mediums may actually be more important than what they see through those survey? Or do you see a point where actually putting that data all together in a central resource of, Hey, we’re seeing this on internal surveys, net promoter scores, here’s the review data.
[00:35:20] Brent Feldman: Let’s put it all together and see what the bigger picture is. Do you see, um, you know, either one out weighing the other or the, the larger aggregation of all of this data, creating a more thorough picture of what people could, um, take data away from?
[00:35:35] George Swetlitz: Yeah, so I, I think it’s the aggregation. I think that, so we, we do some of that today.
[00:35:41] George Swetlitz: So for example, if, if, uh, if, if someone has surveys with unstructured text, we can incorporate that and it’s just another source. You know, Google’s a source. Yelp’s a source better. Business Bureau is a source. Trustpilot is a source. Your own survey is a source. A chat log is a source. We, we analyze chats for clients.
[00:36:11] George Swetlitz: You know, they, they, they have, someone goes on the website, does a chat that’s all recorded, and then we get a copy of that and we analyze it. Lot of great data. What’s going wrong, what’s going right. So it’s really, it’s, it’s, I think the future is bringing all of these together and trying to understand.
[00:36:35] George Swetlitz: Where am I? Why am I seeing certain issues in this source versus another source? What’s understanding that whole universe of customer data or customer information, that’s, to me, that’s, that’s the future. It’s bringing it together into one platform.
[00:36:53] Brent Feldman: That’s great. Yeah. Do you think that it’s just Yelp people are unhappy in general, and I’m kidding?
[00:37:00] George Swetlitz: Well, you know, there’s a certain kind of person that. Signs up to the Yelp experience.
[00:37:07] Brent Feldman: Uhhuh.
[00:37:08] George Swetlitz: Right. And you know it’s true. We did a, we did a, we analyzed, it’s on our website, we analyzed a hundred thousand restaurant reviews and. And we looked at Google versus Yelp versus DoorDash versus, you know, and, and it’s just very different what people talk about.
[00:37:28] Brent Feldman: Mm-hmm.
[00:37:29] George Swetlitz: You know, the DoorDash people are all about what was the food like when it got to my house? Right. So it’s just they’re all a little different.
[00:37:37] Brent Feldman: Yeah.
[00:37:38] George Swetlitz: And you have to understand that. When you’re thinking about your, your business,
[00:37:43] Brent Feldman: uh, that’s actually great insight for people also to concentrate not only maybe like their energy on, you know, it’s getting reviews from a, a particular platform, but also then curating what response is actually, you know, necessary for particular platform too.
[00:37:58] Brent Feldman: Because that could mean a lot to that, you know, that business at the end of the day.
[00:38:02] George Swetlitz: Right? Right.
[00:38:02] Brent Feldman: Yeah,
[00:38:03] George Swetlitz: a hundred percent.
[00:38:04] Brent Feldman: Cool. Well, all right. Last question for you. Uh, uh, you’ve worked in, uh, consumer goods, healthcare and now tech. Uh, if you were advising, um, you know, A-A-C-E-O on on, on reputation in the AI area, uh, uh, era, what, um, what would, what would be your single piece of advice?
[00:38:26] George Swetlitz: My single piece of advice would be take it seriously, because it could be your biggest source of growth.
[00:38:35] Brent Feldman: Cool. Great.
[00:38:36] George Swetlitz: That’s it. You know, you get yourself to the top of the map pack and you engage with those customers that are reading the reviews. Your organic growth is gonna, you know, skyrocket.
[00:38:49] Brent Feldman: That’s amazing.
[00:38:49] Brent Feldman: And the, and the, the organic growth is so important. You know, I know that ads matter. Other things matter out there, but I, I do, I, you know, I’m a firm believer in that organic growth that is just, uh, that, that it is, it’s, it’s critical.
[00:39:03] George Swetlitz: Yeah. It really is. It’s, it’s in the long run. It’s so cheap compared to paying for clicks.
[00:39:11] Brent Feldman: Mm-hmm. Absolutely. That’s Well, that’s great. Uh, I, I will say, George, you’ve had some fantastic information on here. I, I not only appreciate that, you know, it sounds like you’ve got a great platform and, uh, I, I really appreciate your thoughts and your insights though, as well. Um, actually, where could people either, you know, learn more about you, the, the platform in general?
[00:39:32] Brent Feldman: Um, where, where would you send people to go?
[00:39:35] George Swetlitz: Yeah. Uh, the, the easiest place to go is rightresponseai.com. Um, everything is self-serve on our platform for larger customers. They, you have the ability to book a call and, and, and we talk to you and help people understand, you know, larger implications. But, but our, our large customers self schedule meetings and our small customers just sign up, so it’s a really easy, easy.
[00:40:01] George Swetlitz: Uh, process. We try to make it easy. At least that’s what our reviews tell us.
[00:40:06] Brent Feldman: There you go. Yeah. And exactly. And if you weren’t reading your reviews, that would be a little confusing. Uh, oh my gosh. Well that’s amazing. Uh, again, thank you so much, uh, for being on the podcast and uh, yeah, ire, I appreciate all these great responses.
[00:40:21] George Swetlitz: Yeah, great. It was a lot of fun. Nice talking to you.
[00:40:23] Brent Feldman: Thanks. Well, this has been another episode of Mix & Matchbox. I am your host, Brent Feldman. We’ll be back soon with more content. Please like and subscribe. Thanks.