How To Leverage Customer Review Data
Host Bio
Jake Sanders is Content Marketing Manager at NinjaCat and host/producer of What Gets Measured, the company’s award-winning podcast on marketing performance, data, and strategy. He brings more than a decade of marketing experience across multiple roles, with work centered on helping marketers turn complex data into clearer decisions and stronger execution. At NinjaCat, his remit spans content and conversation design: translating analytics, AI, and operational topics into practical takeaways for teams that need both strategic clarity and usable measurement. Sanders also brings a creative background as a professional musician and composer, which informs a careful, listener-focused interview style rather than a purely technical one. That combination of marketing experience, content leadership, and disciplined storytelling makes him especially well suited to conversations about customer experience, feedback, and reputation—areas where numbers matter, but context and human judgment still determine what to do next.
Summary
Jake Sanders treats this topic as important because he does not trust surface metrics on their own. Early on, he pushes on the gap between measurement and meaning, and later he makes the bigger case that reviews are not admin work at all: “This is marketing,” Jake says.
That lens shapes the whole episode. Jake keeps validating ideas that move beyond averages, canned replies, and generic AI. He wants to know what a leader should actually do with the signal customers are already sending, and he repeatedly pulls the conversation back to context, action, and real human response.
George Swetlitz begins with the operating problem that led him here. In a prior audiology business, he helped scale to 220 locations through organic growth and acquisitions. At that size, reputation was not abstract. Two sites in what looked like similar demographic environments could perform very differently, and the reasons were not obvious. Some of the gap might come from competition, some from reputation, some from operations. The problem was that the underlying review data was hard to extract from the major platforms. George describes those systems as “closed gardens or walled gardens,” so even an analytics-heavy company struggled to get the granularity it needed.
That sets up the episode’s first major argument: a star average is too blunt to manage from. George’s example is memorable. Two restaurants can both have a 4.7, but one may be strong on food and weak on service, while the other has the opposite pattern. “They both average to 4.7, but for very different reasons,” George says. Jake immediately recognizes why that matters. When the conversation turns to NPS, he notes that many businesses are “living and dying off of that number,” then cuts straight to the real issue: “you have to go where these conversations are happening” and “listen for context.” His joke—“Seven. I just wanna get outta your form.”—lands because it captures how thin those scores can be.
From there, George defines the layer beneath ratings: voice of customer analysis. Instead of stopping at a score, businesses should look at the topics people mention, whether sentiment on those topics is positive or negative, and which phrases triggered the classification. That turns reviews into operational data. George contrasts the standard management response—tell someone to fix their 4.5—with a more useful one. A leader should be able to say exactly what is wrong, such as problems with waiting-room efficiency or reception flow, and then coach to that issue. In his framing, customers are “freely giving” the business this information. The mistake is failing to use it.
Jake is especially effective when he connects that analytical point to practice. He tells a story from hospitality, where one of his early jobs was responding to reviews. Instead of treating that as throwaway work, he “talked to them like they were people.” He worked through a backlog, responded seriously, and found that even upset reviewers often changed tone when the business showed up thoughtfully. That experience changed how he thought about marketing. “This is marketing,” he says again—not a billboard, but public relationship-building. He also notes the danger of businesses arguing with reviewers in public, especially when the response starts revealing too much instead of building trust.
That story leads into the episode’s strongest shared insight: “the review and the response is a marketing opportunity,” as George puts it. Jake clearly agrees. Reviews are not a box to check; they are a conversation between customers and everyone else deciding what to do. George’s standard is sharp: no response is not participation, template language is not participation, and generic AI is not participation either.
This is where the AI tension gets interesting. George is not anti-AI—he explicitly says AI opened up the possibility of solving the problems he had experienced at scale. But he is strongly against empty AI. If a customer says the steak was great and the fries were bad, a reply that simply mirrors that back is “meaningless.” George calls generic AI replies “a net negative,” and Jake reinforces the point from the audience side: “we are them.” People know what a canned response feels like.
The better use of AI, in this conversation, is not to sound vaguely human. It is to say something real. George’s examples are concrete: bring relevant facts from the business into the reply. Mention the farm a restaurant has worked with for years. Point a hotel guest to the adult pool by Building C. Use AI to surface the details a good operator or marketer would want included anyway, so the response gives the next reader useful information right when they are near a decision. George says nobody reads reviews for fun; they read them because they are trying to choose.
The final section broadens the idea of response beyond the literal reply. George describes the tension between simplicity and rich data, then points to tools like a ratings grid, granular topic-level exports, and competitor analysis built around the same KPIs used on the business itself. Jake summarizes the whole philosophy with a question he clearly likes: “What’s the response that’s required?” He says that framing “feels empowering.” George agrees and widens the meaning of response to include operational fixes, employee coaching, and competitive positioning.
By the end, Jake has guided the discussion toward a simple but durable takeaway: better review data should lead to better judgment. For location-based businesses especially, the review ecosystem is not busywork. It is an underused source of marketing insight, operational diagnosis, and smarter action.
Q&A
- I run multiple locations with similar demographics, but one site performs much worse than another. Where should I look first?
Look past the averages. Compare the review topics and sentiment for each location, then weigh those patterns against the local competitive environment so you can see whether the gap is tied to product, service, waiting efficiency, or local pressure. - Is a 4.7 average rating enough to manage from?
No. A 4.7 can hide very different underlying problems. One location may be winning on food and losing on service while another shows the reverse, so the rating is only the surface signal. - Why can two businesses with the same star rating need completely different fixes?
Because the same rating can be produced by different combinations of positive and negative customer experiences. What matters is which topics are driving the score up or down. - Is NPS enough to tell me what to improve?
No. NPS may tell you whether customers broadly like you, but it does not tell you why. The more useful guidance comes from the actual comments, context, and themes inside customer reviews. - How should I coach a location manager when reviews are slipping?
Do not stop at go fix it. Identify the specific issue customers keep mentioning—such as waiting-room efficiency or reception flow—then coach the manager on that problem instead of handing them a vague target. - Should I respond to every review, or just the bad ones?
A hard rule on every single review is not directly addressed here. The clearer guidance is to participate in the conversation rather than defaulting to silence, templates, or generic AI. - Why should I think of review responses as marketing instead of admin?
Because reviews are public conversations happening in front of people who are deciding what to do next. A response is not just for the original reviewer; it also shapes how future customers interpret your business. - Are template replies better than no reply at all?
Not by much. A stock thank-you does not really participate in the conversation, and it misses the chance to say something useful about the business. - Do generic AI review replies help or hurt?
They hurt when they only restate the review in polished language. That kind of response feels meaningless, signals automation, and can work against trust. - How do I make AI-assisted review replies feel real?
Use AI to insert relevant business-specific information, not generic empathy. The reply should add facts a real operator would want the next customer to know, such as sourcing details or a helpful property feature. - Why do review readers matter so much?
Because they are often close to a decision. People usually read reviews when they are choosing where to buy, visit, or book, so the response sits near the bottom of the funnel. - I don’t have time to hand-write every response. Is automation still worth it?
Yes, when automation removes repetitive work without flattening the message. The goal is to help the business say the right specific thing faster, not to publish empty, interchangeable replies. - What if a reviewer is unfair or gets facts wrong?
Do not turn the response into a public argument or start exposing details to win the exchange. Stay in the conversation without becoming defensive or revealing information that should stay private. - What kind of sentiment analysis is actually useful for operations?
Topic-level analysis is the useful layer. Break reviews into the things customers mention, measure whether sentiment on those topics is positive or negative, and keep the exact phrases that triggered the signal. - Can I use the same review analysis on competitors?
Yes. The same framework you use on your own reviews can also be applied to competitors so you can see their strengths, weaknesses, and possible positioning gaps. - How can RightResponse AI help me act on review data instead of just collecting it?
The approach here is to turn reviews into usable signals: topics, sentiment strength, trigger phrases, location comparisons, and competitor comparisons. That gives leaders something more actionable than a single rating. - How can RightResponse AI help me avoid canned responses?
By cataloging business details and pulling the relevant ones into the reply. That lets the response say something concrete and helpful instead of repeating the review back to the customer.
Transcript
[00:00:28] George Swetlitz, welcome to What Gets Measured. How are you, sir?
[00:00:50] George: Great. Great to be here.
[00:00:51] George: Thank you.
[00:00:52] Jake: Excellent. So RightResponse AI. This isn't, this is your current company, but you, you had a previous job where I says, here you scaled to 220 locations, and I'm just thinking that's amazing to keep one business going is, is a lot to keep 220 locations going. I, I just want to, you know, speak to you as, as you know, you're coming from this operations mindset.
[00:01:21] Jake: What, what's the pain right there? I mean, the, the data measurement at that scale, what, what made you think like, there might be a better way to handle this? Or is the scale too much? Just talk about that. How did you go from zero to huge?
[00:01:36] George: Yeah. Yeah. So. So growing, you know, this was an audiology business, so think hearing aids and, and we were, you know, scaling that business through a combination of organic growth, opening locations around established businesses that we have, but then also acquiring businesses and, uh, very strong businesses and bringing them into our footprint.
[00:02:01] George: And, and as we did that. As you can imagine, there's lots of issues around scaling these kinds of businesses and, and part of those issues were around reputation and the impact that reputation has on your ability to generate organic growth. And so, for example, a a common problem would be that you, we would have two locations in what seemed to be for us similar.
[00:02:31] George: Demographic environments
[00:02:33] there
[00:02:33] Jake: is, right?
[00:02:33] George: But one would perform very well and the other one wouldn't. And so why? Why would that be? And so as we did more research, we found that in some cases that might have to do with the competitive environment. In other cases, that might have something to do with the reputation of that business.
[00:02:56] George: Then we started thinking about, well, how do we measure that? How do we understand it? How do we collect data? And it was very difficult to do that. So there are platforms out there that are reputation management platforms, but very infrequently can you get data out. You know, many applications are. You know, closed gardens or walled gardens, you can do a lot within it, but you can't take data out.
[00:03:28] George: And so we had a hard time getting the granularity of the data that we were very analytics heavy as a company.
[00:03:34] Jake: And George, is this like Yelp? Are you talking about those type of platforms or,
[00:03:38] George: yeah, exactly. So Google, Yelp, you know, any kind of review management source,
[00:03:45] Jake: right.
[00:03:45] George: For you. Go location-based businesses.
[00:03:47] Jake: Okay.
[00:03:48] George: Exactly. And so, you know, we had, you know, we had data engineers in our company. We had a lot of sophistication around that, but we had difficult time getting the data. So fast forward couple years, we ended up, it was private equity backed, so we ended up selling the company and then chat G PT came out.
[00:04:07] George: And so we thought that AI could help in a lot of the elements of that. The, the problems that we had.
[00:04:15] Jake: So, so, I mean, when you aligned on reputation management, was that the big takeaway from, uh, the, the, the former audiology company? You were like, man, reputation was a big ding. Um, is, I mean, it seems like most people don't start a company based on this.
[00:04:32] Jake: If they didn't think that was. The main issue here. Wait, one question. Reputation versus, um, workforce management. You just do, was it reputation that helped you or was it better management? Like, you know what I mean? Like if you just had better managers in that store, the reputation would be better. Is that a thought?
[00:04:53] George: Yeah, but you know that, that is like you have to manage all of it. Sometimes, right?
[00:04:58] Jake: That's all your responsibility, right?
[00:05:00] George: You have to manage everything. And so, you know, making sure, but how do you know that you have a problem? How do you know?
[00:05:06] Jake: Well, that's my question. I, this next one is like, you're, you're looking at disaggregation, five stars, one star, whatever.
[00:05:12] Jake: You can't go through looking every review, you can't track the sentiment shift. And so, so speak more about this, this, this AI pivot that could help you kind of look at aggregate data and make sense of it.
[00:05:27] George: So, so you're exactly right. What, what most platforms give you is a view of your ratings.
[00:05:35] Jake: Hmm,
[00:05:36] George: 4.5.
[00:05:37] George: Your average is 4.7, but it's when you start to peel the onion, it's much more complicated than that. You could have two restaurant locations, both have a 4.7, but one could be good at food and bad at service, and the other could be good at service and bad at food. They both average to 4.7, but for very different reasons.
[00:05:58] George: So in order to understand what the problem is, order what people are talking about, you have to do more. And so that's voice of customer sentiment analysis. And now you're starting to generate lots of data. So you've got a review that's got a paragraph. But now you have, well, there's all of these things that people are talking about, and that sentiment's either positive or negative, and there are certain phrases that caused.
[00:06:23] George: The, you know, that to trigger. And so we didn't have any of that data back then. Oh my God. We, we didn't have it. You couldn't, you couldn't get it. The idea was right. The idea was start something, build something where that, where we create that data, we, we use and leverage that data and that's essentially what we, what, what the idea was behind.
[00:06:43] George: RightResponse AI.
[00:06:44] Jake: That's so cool. And so you're, you're hitting on something that a couple previous guests have talked about is causation correlation. These are not the same things. So, you know, when you're wondering what caused something, that's a heavy, heavy question. Um, sentiment analysis, you know, it's like correlation, statistics.
[00:07:04] Jake: How do you tune, how do you tune the ai so it, it isn't making correlation mistakes and it's getting you close to causal relationships.
[00:07:15] George: Most large organizations have a set of KPIs. They know how they think about the business.
[00:07:21] Jake: Hmm.
[00:07:22] George: I've got products that I sell and people tend to evaluate the products in this way, or I offer these services.
[00:07:29] George: And people tend to think about that in a certain way or
[00:07:31] Jake: like, uh, NPS is almost our internal review system, you know?
[00:07:37] George: Right. So think about NPS, I mean, that's a different NPS to me is. It's almost, you know, it's, it's kind of interesting, but it's unusable, right? It doesn't tell you anything.
[00:07:51] Jake: It sure
[00:07:52] George: doesn't, you know, you get a it tell, well, it tell you, yeah.
[00:07:55] George: Customers like me or they don't, and I can compare that to my competitors. Then I'm like, well, why? Like, why don't they like me?
[00:08:02] Jake: It's crazy because a lot of people are dying off of that number. They're living and dying off of that and, and whenever I'm asked to ask, you know, one through 10, how'd we do?
[00:08:12] Jake: Seven. I just wanna get outta your form. You know, are they really giving you the. T No, that's, that's why I love you have to go where the place where these conversations are happening, you have to have the wherewithal to go in and listen for context. You have to know what's happening that might cause these things.
[00:08:31] Jake: So, you know, how, how do you disaggregate sentiment data? You know, like, like what is it, what, what is the disaggregated sentiments? Give you that, those star ratings, obviously, like you just said, they can, but kind of like how does that change, how does now an operator who's looking, you know, through your interface being like, ah, this helps.
[00:08:54] George: So that's, that's exactly, that's exactly the question because the reality is. You have somebody that has a 4.5 average rating in their business. So what do you do? You just go yell at them. You say, no, it's four point five's not good enough. I want you to get to 4.7, right? Because that's, I don't information to give you, right?
[00:09:11] George: I don't have any other information to give you. If you wanna go figure it out, go read the reviews and figure it out for yourself. But just as the leader I'm saying, go fix it. So that's what happens in most companies. But really what you need to do. Yeah, that's what I'm saying. What really you need to do is you need to analyze the information that customers are freely giving you.
[00:09:34] George: They're, they're giving you this information. It's like gift, right? You know, I don't have to go do survey. I don't have to pay anybody. They're like freely giving me this information and then I can go back and say, you know, Mary, you have a 4.5. There's one thing you need to focus on. People aren't satisfied with the way you handle the waiting, the reception and waiting and the efficiency of that.
[00:10:00] George: That's what people complain about. So fix that and here's our SOPs and here's whatever it's, but fix that and you will have a higher rating. Like that's what our real company does, right? That's what a company that leverages data will do.
[00:10:19] Jake: It's just a very interesting thing because you said Go fix it.
[00:10:23] Jake: That's, that's like the, the modus operandi. Just go fix it. And I worked in, uh, hotels. I worked, um, law firms. You want to talk about people who are getting ratings and reviews. Um, but one of my jobs at the hotel was to respond to every review and. They were like, just, just write whatever was what I was told.
[00:10:47] Jake: And I, I, I kind of took it, it was one of my early jobs as a marketer, and I took it seriously. I talked to them like they were people. I made sure that I stood up for, you know, the rights of the, the hotel. Like if you got, you know, if you got stuck in the bathtub, is that the bathtub's fault? You know, I, how can I say that diplomatically?
[00:11:08] Jake: But after a while, the hotel was like. Everyone loves the responses you're giving. Here, two, four. They hadn't said anything to any of 'em. One star, four star, no response. But within a month, the hotel was like cheering me on because it was just a place where they didn't want to, like people are kind of saying good things or bad things, but I just don't even want to go in there.
[00:11:36] Jake: And I just did a backlog and worked forward and by the time I got done, all I did was show up as a human to a conversation. I made like friends with, you know, some of the, like, the people that were complaining were like, I thank you so much. It was just like, and it blew my mind 'cause I was like. This is marketing.
[00:11:56] Jake: I, I'm out here thinking advertising is marketing. That's right. Marketing is a billboard, marketing is a, you know, it's this, it's, it's being seen, seeing people showing up to relationships. And I love that you said they freely give it. Um, but one thing that I had to stop these fools from constantly doing is arguing with the reviewers and respondents online.
[00:12:17] Jake: Like you have a lawyer talking about. You know, client attorney, confidential stuff in the thing saying, no, this client doesn't, I was like, stop, don't do it. So what's your response to people who are like, no response to the reviews, or respond to the reviews? Or how do you engage with, you have the review, you have the thing, how do you speak back to that person?
[00:12:41] Jake: How do you keep that, you know, nice freely given, you know, wheelhouse going?
[00:12:48] George: Yeah. So the first thing that. Companies need to recognize is that the review and the response is a marketing opportunity. It it's a marketing opportunity. It's, it's not a task that has to get done. It's not, you know, it's, it's none of that.
[00:13:07] George: It's a marketing opportunity. It's the ability to participate in a conversation. So the review space is a conversation between your customers and the rest of the world. That's, that's what it is. And the question is, are you going to participate in that conversation or not? So how do you not participate in the conversation?
[00:13:29] George: You don't participate in the conversation. If you don't respond, you're, you're not participating in the conversation. If you use a template response, thank you, we appreciate your, you know, whatever. Right,
[00:13:41] Jake: right.
[00:13:41] George: Standard response again and again and again. You're not participating in the conversation if you use a generic AI response.
[00:13:49] George: So that's the big thing right now. So someone says, I love the steaks, but I hated the fries. And the response says, oh, we're glad you love the steaks to, you know, we're disappointed that you didn't like the fries. It's a meaningless response,
[00:14:05] Jake: right?
[00:14:05] George: Everybody knows it's a meaningless response. All you're doing is taking up space.
[00:14:12] George: And so I think that it's a net negative. Generic AI responses are a net negative. They work against you, they hurt you. Right? We've done surveys with customers, kind of random things, where they tell us that they, they know now what a generic AI response is, and they don't like it.
[00:14:33] Jake: An audience. Let's just be, we are them.
[00:14:35] Jake: You know what it's like to get a canned response from somebody? Are, are you like, hooray? No.
[00:14:41] George: Right? And so. Our approach to that was to say, if it's a marketing opportunity and it's an opportunity to talk to all sorts of people that are in the process of making a decision. You don't go read. You don't, you don't go read reviews for fun.
[00:14:59] George: You read reviews because you're trying to make a decision. You, there's no one closer to the bottom of the sales funnel than someone reading a review. They're making a decision.
[00:15:12] Jake: Totally.
[00:15:12] George: So if that's the case, then don't you want to take all that great content that you spent lots of money developing on your website and bring that content to your response?
[00:15:28] George: Right? So if on the website you bri, your restaurant and you know, all your steak comes from this farm in Colorado where it's grass fed, beet, whatever it is. You put that you're proud of that and you put that on your website. If someone writes a review that says, oh man, that was the best steak I've had in Tallahassee, then don't you wanna say, yeah, we think it's, we think it's great steak.
[00:15:55] George: We've been working with this farm in Colorado for 22 years and we buy all of, it's all organic wrapped. Don't you wanna say that? So that when someone's reading it, they go, man, I wanna eat, I wanna try that thing. That's what people don't. That's what they're missing, right? Is that you have, and so that's what our platform does.
[00:16:14] George: You kind of catalog all of these things. What you said before when you were writing them by hand, right?
[00:16:24] Jake: I
[00:16:24] George: was doing it by hand. You were writing them by hand. Right? If you could have said to a system, Hey, if somebody makes a comment about, I wish we had. You know, if somebody writes a a com writes a review, complaining about the fact that you know they had to be in a pool with all sorts of toddlers in the response, write that there's an adult pool by building C.
[00:16:51] George: Next time, visit the adult pool by building C. That's what you would've said. So wouldn't it be great if, if we could automatically do that so you don't have to think about it all the time?
[00:17:03] Jake: Yeah. Well, and, but it, it's nice to have somebody who has experience on the frontline with folks giving you a way to make sense of the data.
[00:17:14] Jake: That's why I, I wanna move into the data conversation because subject matter expertise is important. If you are getting all of these, you know, multi-location brands, they have mountains of review data, sentiments. You know, competitive benchmarks. Um, at some point somebody has to make a right response to all that data, you know, so I'm, I'm kind of wondering how, how, just in general, not, not in regards to RightResponse AI or maybe in the way that you've built your engine, how do you get people.
[00:17:47] Jake: The data that they need in a way that they can use it and then into a place where they can act on it, transmit it, you know, just the general, a approach to, from data to trust.
[00:18:02] George: Right. Well, as, as you, as you know, building a system yourself or having a system yourself, there's, there's this kind of tension between simplicity.
[00:18:15] George: Everyone wants something that's simple and easy to use and richness of data. You know, all of those things, right? There's this trade off, and so in our platform we, we provide certain, certain screens and dashboards that, that are different than most people. They're rich. So for example, we have something that's called the ratings grid.
[00:18:43] George: And so what we do is we look at what are the topics, both positive and negative, that people talk about in a five star review versus a four star review versus a three star review versus the two. And so when you look, when you just visually look at it, you can see what, what starts to shift as the rating goes down.
[00:19:06] George: So it's kind of an interesting way to think about. Kind of the connection between rating and KPI. Context. Context. So we do things like that, right? It's context. Yeah. So we do things like that. But then for our larger clients, they wanna take all the data. They wanna, they maybe wanna build a model, they wanna do a lot of things.
[00:19:26] George: So we. We allow our larger clients to take this granular data, which would be the topic, the strength of the sentiment, the exact phrase that triggered that topic. And you can imagine for every review there might be 20 of those things, and now you have all these reviews. So it's a, it's a, it's a large amount of data that they can extract and use it.
[00:19:54] George: How they won. The same thing is true of competitor data. So what people don't necessarily think about is that anything that you know about yourself, you can also know about your competitors, right? Why are they successful, right? What are, what are their problems? What does that mean in terms of how I position myself from a marketing perspective against my competitors?
[00:20:24] George: So we can look at all of the, you can come into our system and you can identify who your competitors are, and we can look at that, and then we can, we can develop the sentiment analysis using your KPIs for your competitors, and we can compare them.
[00:20:41] Jake: So it's, I'm, I'm just thinking like, but the takeaway from all of this is if you have data, you have to do something with it.
[00:20:48] Jake: And I like what your whole game is, is to figure out how to respond, you know, to, to something. And, and I, I think that's, it's one of the big problems is, is I have the stuff, I'm not sure not how to act. How to respond. And I think there's something about the way you are just approaching data in general is just like, what's the response that's required?
[00:21:12] Jake: It's, it's, it feels empowering.
[00:21:16] George: Yeah. You know, and it's exactly what you just said. When we say response, we're not talking about the physical response to the review. Right. We're talking about how do you respond to the sentiment? What's the right response? How do you. How do you help your employees do a better job?
[00:21:32] George: How do you understand, how do you respond to the competitive environment that you're in? So it's all of these responses that drive your business forward. So when we say right response, if it's this broader sense of like, how do you respond in general to the data that's available through this review ecosystem?
[00:21:51] Jake: And, and this is, maybe we can just, uh, end generally, I'm gonna ask you one more, but reaction, responding. I think I've been, you know, a little overwhelmed as you know, it happens. A lot of technical changes happening, ceaseless change, and then you kind of wonder how can I balance those things, you know? Do you have any advice, um, you know, in regards just in general, like how reaction and response.
[00:22:23] Jake: Um, do, do you think of them differently? Do you have any advice on people? Because it feels like you're making a really compelling case, but I think a lot of people are reacting mostly and they would love to respond. Is, is there maybe something general you could give us in that regard?
[00:22:40] George: Yeah, I mean, it's, it's a management philosophy.
[00:22:45] George: Right. So it's do you run your business reacting or do you try to develop a hypothesis about what you need to do to be successful and then drive in that direction until data tells you that you're not right, or maybe you made a mistake, or maybe you need to steer in a different direction. And that's always been the approach that I've taken in organizations that I've been in.
[00:23:08] George: And the core of that is data. The core of that is generating as much data as you can in the areas that you think are important, and then developing a hypothesis and testing that as you go forward. So I would say, you know, that's how I would respond to that.
[00:23:24] Jake: Great response. He didn't feel too reactive when he gave that one.
[00:23:27] Jake: He had it in his pocket. Um, so you've given us a lot to think about the importance of reviews. I just thinking about sentiment analysis for competitive, uh, research. That is kind of cool. You, you can go and do that. You can do it for yourself, know yourself, know your audience, know your competitors. Be stronger.
[00:23:46] Jake: If there's one way. One piece of advice that you wanna leave us with? What do you think it is, George?
[00:23:55] George: I would say to companies, you know, location-based businesses or businesses that get lots of reviews, to really think critically about how to leverage that review ecosystem to drive their business forward.
[00:24:07] George: I think it's a, a huge missed opportunity for many, many businesses. Yeah.
[00:24:13] Jake: Yeah. And it's not just a task to check off people, get with George, um, and follow him. How can we connect, um, and learn more about you online?
[00:24:23] George: So RightResponse AI.com, that's our site. People can reach out to me there. We have the ability to schedule appointments and things like that.
[00:24:30] George: We love talking to people about their businesses. And I'm on link LinkedIn, RightResponse AI and George Swetlitz. Were on LinkedIn, so happy to connect with people there as well.
[00:24:39] Jake: Okay, I'm not gonna let you go until we play a game called cheese or chocolate, where I ask you two questions and you gotta choose one.
[00:24:45] Jake: Uh, are you ready?
[00:24:48] George: Sure.
[00:24:49] Jake: Cheese or chocolate?
[00:24:52] George: Chocolate. 100%.
[00:24:54] Jake: Whoa. What time is it? It's probably chocolate o'clock where you are. It's okay. Um, treadmill or trail?
[00:25:05] George: Trail. I'm not much of a treadmill guy. I I just get bored if I'm on, if I'm on a trail, I can, I can appreciate where I am and what I'm doing.
[00:25:15] Jake: That's
[00:25:16] George: treadmill's just for me.
[00:25:18] Jake: That's kind of what I was wondering if, if somebody was like all treadmill, I would've been like, yeah. Consistency trail's the right option anyway. Um, box lunch or bag lunch.
[00:25:33] George: I don't even know what the difference is.
[00:25:36] Jake: I guess a, a lunch. Well just imagine Then you have any, there's a lunch in a box and a lunch in a bag.
[00:25:43] Jake: Which one are you gonna take?
[00:25:45] George: Uh, probably the bag. Well, no, actually I would say if it's a reusable box, I would go with the box.
[00:25:54] Jake: Good cop caveat. No, I love that. And that made sense. I was like, man, but there is no right answer in this game. Um, okay, here we go. Books. Are you one at a time or are you slowly going through a stack
[00:26:07] George: back?
[00:26:09] Jake: Yeah. You like it? A
[00:26:09] George: stack
[00:26:10] Jake: Touch a bit. Yeah, touch a bit.
[00:26:12] George: There's just, there's just too many books. There's a lot of books and you know, you don't always make it through everyone, so you gotta have a lot of them going.
[00:26:22] Jake: Well, I, I've gotten to that point where you don't feel bad, like if you've gotten 80 pages in and it's not grabbing, like, I'm, I'm okay to, to move on.
[00:26:30] Jake: Life is too short. Okay. Yeah. Yeah. Okay. Good. All right. We're hitting. Okay. Two more. Flying or invisibility
[00:26:39] George: flying. I'm a pilot, so I gotta say flying.
[00:26:42] Jake: Are you piloting? Where are, are you, you got your license and everything?
[00:26:46] George: Yeah. Yeah.
[00:26:49] Jake: Well, go ahead. Um, fine. Well, as you were, sir. Okay. Ooh, this is the last one.
[00:26:56] Jake: And I know it's contentious, so I saved it for the end band of bagpipes, or a sideshow of slide whistles.
[00:27:08] George: That's an, I mean, I know what a bagpipe is, but I don't know what a, what's a, what was that other thing?
[00:27:13] Jake: You know, the woo, you know.
[00:27:17] George: Oh.
[00:27:17] Jake: Would you wanna acquire?
[00:27:18] George: I like, I, I like the way, I like the way bagpipes sound.
[00:27:23] Jake: It's the safer option. Both a little jarring, but no wrong answers. On today's show, you win the prize.