From Reviews to Revenue:​ – How to Understand What Your Customers Really Care About

0:06
All right.

0:07
Hello, everybody.

0:09
I’m watching the participants trickle in.

0:10
This is exciting.

0:11
Thanks for making time for us today.

0:13
I’m going to give it maybe just one more minute before I really get started on intros.

0:16
Not a whole minute, by the way.

0:17
A fake minute.

0:18
A corporate minute.

0:19
I’ll give it 30 seconds.

0:20
Let’s call it that.

0:34
All right, thank you all so much for hopping in here.

0:38
I’m sure there’s all sorts of exciting buzz about chat meter and Elkamer, and what in the world are we going to talk about today?

0:43
So thank you for making time for us on a Wednesday of all days.

0:46
Oh, my goodness, you’re brave.

0:48
I’m Ashley.

0:49
I am a strategic client success manager here at Get This Elkamer at chat meter at Alchemer, whatever you want to call us right now.

0:57
And Morrissey is here with me too.

0:59
I’ll let him say hey, but you’ll hear more from him later.

1:01
Hey everybody, I’m Morrissey Balsamitis.

1:03
I’m our senior data and AI product manager here at Alchemer.

1:06
Really looking forward to walking you through some of the functionality that we have.

1:11
And yes, he’s going to stay that good and that smooth when he’s presenting later.

1:14
So no pressure.

1:17
So today we’ll be covering from reviews to revenue, how to understand what customers, what your customers really care about.

1:25
Particularly exciting because isn’t that what we all want to know?

1:28
So before we happen, just really quickly, I want to let you know that I want your questions and I want them badly.

1:33
So please leave them in the chat section of this webinar.

1:38
Whenever you are ready to ask a question, just pop it in there.

1:41
We’re going to answer them all at the end.

1:42
So if we can’t get to you, we’re maybe going to collect those and send you an e-mail later with your answer.

1:48
OK, all right, let’s move on.

1:51
So as you could imagine, we’ll be talking quite a bit about qualitative data collecting all of that open text.

1:57
Maybe you’re an Alchemer customer originally and you’re looking at all this open text from your surveys.

2:02
Or maybe you’re a chat leader customer originally and you’re looking at open text from your surveys, from your social media comments, from your reviews.

2:10
Oh my.

2:11
And that stuff is everywhere.

2:12
And it’s really difficult to really track that information.

2:15
Let’s consider multi locations.

2:19
If you’re originally a chat meter customer, it’s because you’re a multi location customer.

2:22
You have so many locations inside of your business and on top of each of those locations, they’re each getting reviews, they’re each getting social media comments, and it’s all happening on all of these different platforms.

2:34
That’s a lot of logos in a pile right there.

2:36
So the idea is that customers are sharing everything and they’re sharing it everywhere for each of your locations.

2:43
That is a wide net to cast.

2:46
How do we even start with that?

2:49
So really multi location businesses, like I mentioned, you’re not able to really listen to about 80% of that feedback that you’ve gotten.

2:56
Let’s consider all of the feedback that you’re missing from there.

2:59
I say Geo, that’s been a buzz lately.

3:02
If you’re in the SEO world, you’ve certainly heard about generative engine optimization and what that means for maybe ChatGPT.

3:10
We all have a friend who uses ChatGPT for anything and everything, regardless of how inappropriate it is to ask.

3:16
It could be a really simple question.

3:18
They’re using it, though, and they’re also using it about your business.

3:21
Let’s be real.

3:21
Hey, what’s a good restaurant near me?

3:23
I’m in DC for the very first time.

3:24
Where do I eat?

3:26
Aren’t you concerned with how you’re showing up in those?

3:28
Or what do people think about this restaurant in DC?

3:32
If you’re showing up, you surely hope that Chaji PT is going to be giving maybe some favorable results or at the very least, results that your marketing team predicts can control.

3:41
You kind of know where they’re pulling that information from.

3:44
And unfortunately, Chaji PT does not pull from all of those logos we saw in the previous slide necessarily.

3:50
They’re not really pulling from Google, right?

3:51
They’re pulling from BBB.

3:53
They’re pulling from Yelp.

3:54
If you’re at home listening right now, you probably are thinking to yourselves, we do not have BBB in like our plan on how we’re going to be successful.

4:01
I fear it might be a good idea to add it in there.

4:05
Yelp, maybe that’s not your plan if you’re not a restaurant.

4:08
I have a lot of clients who say, oh, well, I’m not a restaurant.

4:10
I don’t need Yelp.

4:11
I, I fear you do because it actually is not just for restaurants and also they’re really expanding and they’re making partnerships with things like Chai GPT, right?

4:20
So that’s a lot of data.

4:21
And we want to have an idea on how we can funnel this all into one platform.

4:25
And let’s consider maybe that all of it lives in separate areas and we need it in one place.

4:32
And also what happens if it stays that way?

4:36
You have your reviews in one area, you have your social media posts in another area, you have images in a different area, you have your qualitative comments and your surveys as long text box in a different area.

4:47
We want to put a lot of them all in one place.

4:49
We hear you, Ashley.

4:50
It all should go in one place.

4:52
But what happens if it stays that way and it doesn’t all go in one place?

4:56
You might have something like this Urban Outfitters, we may have heard this scandal that they kind of had one tweet from one employee one time.

5:07
I worked at Urban Outfitters, the same company that owns Anthropology and Free People for three years.

5:13
I worked at the Georgetown location in DC, and we would use the terms Nick or Nicole to specify somebody who was shoplifting.

5:19
And unfortunately, they said that they used it mainly for black people.

5:23
Whoa, dude, that’s a pretty big deal.

5:25
Don’t we want to know about that risk?

5:27
If there’s a tool that can capture which there is risk like this when comments are being left, that would be really helpful to see before it snowballs.

5:36
And guess what?

5:36
It did, This brand did not see this and it did snowball into something much bigger and into a larger conversation.

5:42
Now it’s affecting their other brands like Anthropology and Free People.

5:45
It didn’t just stay with urban people can Google people know that you’re related to these other brands.

5:50
So we have to get ahead of it.

5:51
There needs to be a tool to hear all this feedback.

5:53
And you can see that they had a little unfortunate headline down there.

5:57
Our Urban Outfitters owner scraps the policy that allegedly led to racial profiling of shoppers.

6:03
Woof.

6:04
Now, if you didn’t know about it, you definitely do.

6:05
Now we’re on the other hand, maybe you’re a brand who is listening.

6:10
This is one of our customers, the kebab shop.

6:12
Hey, Wally, how you doing?

6:13
Yes, I’m still here.

6:16
The kebab shop listens.

6:17
They listen deeply.

6:19
They actually really genuinely care, while he really is a very caring person about what his customers are saying.

6:25
So consider you can have all of your review feedback, your social feedback, see this image, see the social post, see these reviews.

6:32
It can go all in one place.

6:34
You’ll be able to study it, you’ll be able to get ahead of things.

6:37
You can be alerted when things are going a little bit South in a particular area and get ahead of it before it snowballs like Urban.

6:43
So it doesn’t have to be that way.

6:45
It can be really easy.

6:46
It should be really easy and you actually really should be listening.

6:49
You want to be more of a Wally at kebab shop and you want to be the folks at Urban Outfitters.

6:56
All right, I’m gonna pass it over to Morrissey so he can show you what I’ve been gabbing about.

7:00
Morrissey, please take it away.

7:01
Perfect.

7:02
Thank you so much.

7:03
So Ashley has done a great job outlining some of the challenges that business are facing in the modern era, right.

7:09
She’s talked about this level of unstructured data, and we don’t want it to appear scary.

7:13
Unstructured data is actually a gold mine of information and a gold mine of insights that you can take to operationalize your business, but you need the right tools to be able to extract that information.

7:25
Luckily, not everything is doom and gloom and there is a solution to the challenges that businesses are facing.

7:31
This solution is the new frontier that is powered by AI, specifically Alchemist’s Pulse AI.

7:38
That is the tool that allows you to extract from your goldmine and really turn your business into the most successful version of itself.

7:46
Before I jump into how things have shaped in the modern world with AI, I do want to set the stage a little bit kind of a timeline for where we came from to where we are now in terms of analyzing all of that unstructured data.

7:59
So the era I want to 1st talk about is what I like to call the pre AI era.

8:03
And during the pre AI era, established business goals needed to be manually validated.

8:09
So what does that mean?

8:10
Well, really, businesses only had two ways to go about going through all of their unstructured data to analyze exactly what customers are saying about their business, whether it be positive or negative.

8:22
The first way, and probably the most obvious way, was people were actually manually reading through all of their reviews, all their social media comments, all of their survey responses, looking at all of these images.

8:34
As you can imagine, there are a couple of challenges with this approach, and there’s really two fundamental issues with manually reading through all of this data.

8:43
The first one is an issue of scale.

8:45
If you are getting a couple of reviews, a couple of social comments per month, sure, it’s possible to read through all of that data.

8:53
But in reality, most businesses are receiving magnitudes more of that information on a daily basis.

8:59
So it’s impossible for one individual or even a team of individuals to truly read through all of that data and actually internalize and understand the insights.

9:09
So as a business scales, as a business grows, the amount of data that comes in grows, and it makes it impossible to actually read through every piece of data with the actual intention to understand it.

9:21
The second item, which is a little bit more obtuse but is incredibly important, is the natural bias that us humans have when reading through data.

9:29
As you’re reading through this data, you’re looking towards your key business objectives and you’re going to have a tendency to focus on the ones that achieve your business objectives instead of detract from it.

9:39
And so this human bias can allow you to make conclusions that aren’t actually a representation of the real world, right?

9:46
There’s a little bit of bias applied to it.

9:48
We all suffer from this.

9:49
This is just part of the human condition.

9:51
It’s nothing negative with what people do, but it is something that is worth pointing out as a fundamental issue so we can see the challenges with manually reading through all of the data.

10:00
The next layer up that people went from manually reading all of the data was to utilize basic keyword matching to quantify the data.

10:09
So what do I mean by this?

10:10
People would search through, you can think of it like a control F through the data to find specific words like burger, fries, hospital, bank.

10:20
And while that can give you some information about the number of times a specific word is mentioned in a review, there are again two key fundamental issues with this approach.

10:31
The first one is when you’re doing keyword matching, you’re looking for that exact word.

10:36
So let’s take a scenario here.

10:37
A business, maybe in the fast food space, has decided that they want to increase the overall perception from customers of their customer service.

10:46
Well, when using keyword matching to track customer service, you need to come up with every single word somebody could use in a review to reference the concept of customer service.

10:57
So you need to track employee and employee, host, Hostess, cashier, waiter, waitress, so on and so forth.

11:04
Because we have such an extensive vocabulary, it is impossible to come up with every single phrasing that somebody is going to do when referencing the theme of customer service.

11:14
And so as a result, when using basic keyword matching, you’re only getting a small part of the picture, which is actually pretty damaging.

11:21
There’s a common saying in in data analytics that incorrect data is often times worse than no data.

11:28
So seeing just a piece of the puzzle and making a decision on it with the best intentions can actually result in negative implications for your business.

11:36
The second fundamental issue with utilizing baser keyword matching is that when you are looking at a keyword in a review, you don’t actually know the intent of what somebody has expressed.

11:50
So sure, we know that the word burger has appeared in a review 10 times, but you don’t know the emotion somebody felt when mentioning that word burger, right?

11:59
They could say that the burger was incredible.

12:01
They could say that the burger was terrible.

12:03
You can’t actually quantify the opinion, the emotion somebody is saying with just basic keyword matching.

12:09
The way people went around this was they would attribute the star rating of the review to the keyword, right?

12:16
So if it’s a five star review and the word burger is mentioned in the review, they’re going to say that it’s a positive impression.

12:22
But the challenges, most reviews are complex.

12:24
They may say something positive in one sentence and something negative later on in the sentence.

12:29
A really good example.

12:30
The customer service was amazing, but the burger was terrible.

12:33
Five stars.

12:34
This happens all of the time and now you don’t know which word is positive, which word is negative.

12:39
Thus again giving you an incorrect part of the picture.

12:42
As a result of these two fundamental challenges, business was were acting reactively instead of proactively.

12:49
And so there’s a time delay between their analysis.

12:52
If customer service is having issues for months, you’re doing that analysis months later because it takes months to do and taking action before it’s too late, before damage has been done to your business.

13:03
Luckily, as I alluded to, we are no longer in that pre AI era.

13:07
We are now in the AI era where those issues are no longer a problem.

13:12
With Alchemer’s Pulse AI, for example, we can automate business objective validation in seconds with all the functionality of our AI insights offering Pulse AI.

13:23
Couple of quick examples here.

13:25
You can leverage our tool signals to ask any question you would like about all of your unstructured data and receive a response.

13:31
You can use our risk monitoring tool to immediately receive alerts when our AI detects a potential risk for your business within a review.

13:38
And you can leverage our competitive intelligence product to do a very similar analysis to understand how you are performing as compared to your competitors in the industry, in the marketplace and where are those competitive advantages and where are those blind spots.

13:52
One thing that I always like to say is I don’t want to just talk about things that we’re able to accomplish here.

13:57
I want to actually show you them.

13:59
So I am going to transition over here into a demo of our actual dashboard to set the stage a little bit.

14:06
Right now we’re looking at about 1000 locations for the brand Target, a retail environment.

14:13
The first thing that we’re going to do is show off the Signals offering here.

14:16
So to navigate to that, you’re going to go to our Pulse AI section.

14:20
You’re going to click on that and you’ll default to Signals.

14:22
Pulse AI is the umbrella branding for all of the AI insights tools offered by Alchemer.

14:28
Signals, when you look at it, is the ability once again to ask any question you would like.

14:34
When you ask this question, it is going to read through all of your reviews, all of your social media data and all of your images, analyze it, and then generate an answer to your question.

14:45
One thing I want to be super clear about here is we talked about keyword matching before.

14:50
This does not use keyword matching whatsoever.

14:53
The Pulse AI model is able to analyze thematically and conceptually what the question contains and then find attributing data that relates to that concept or theme.

15:04
So if you ask about customer service, we’re not looking for the word employee employees.

15:08
We’re looking for the concept of customer service by default.

15:12
When you navigate to signals for the first time in your session, we’re going to ask a default question, what are people saying about my business?

15:18
So it just gives you a high level overview about the personal opinions of people who have left reviews and social media comments.

15:25
For the purpose of today’s demo, we’re going to focus on reviews.

15:27
So I’ll transition specifically to that tab.

15:30
And what we can see here overall is for Target.

15:33
In the last three months, people have had varying experiences at the Target locations, with many customers praising the cleanliness and organization of the stores, while some expressed frustration over poor customer service and long wait times.

15:46
So instantly, in just one sentence, we can see the most prevalent themes, the most prevalent concepts in online reviews over the past three months.

15:54
People love the cleanliness and organization of the store, but there are some issues with customer service and long wait times.

16:01
Beyond that single answer to the question, we expand upon this with additional bullet points to give you even more information about what the conclusion was.

16:10
So we can see that many reviewers appreciate the cleanliness and organization of the stores, noting that it creates a pleasant shopping environment, perfect to see when you’re shopping.

16:19
Several customers highlight helpful staff members who provide good service, making their visits enjoyable.

16:24
However, on the other hand, and more severely, there are frequent mentions of rude or unhelpful employees, particularly in customer service and checkout areas.

16:34
So people are having some positive experiences, but the negative ones specifically around the checkout areas are dominating the overall perception of customer service.

16:43
And finally, tangentially related to that, long wait times at checkout, often due to limited staff availability, receive consistent complaints from shoppers.

16:51
So individuals appreciate the organization of the store and we’re able to understand that in seconds.

16:56
But what they’re struggling with is the checkout process.

16:59
Primarily, there are long wait times when checking out and the customer service at these checkout areas is an issue for them, negatively impacting their experience.

17:10
Once we have this insight, our AI model will automatically suggest follow up questions that allow you to dive a little bit deeper and understand exactly what is going on as it relates to the insight generated.

17:23
So based on this, I’m really concerned about Target’s checkout experience.

17:28
So I’m going to click on this question here.

17:30
Do customers mention any ways the staff for checkout experience could be improved in their reviews?

17:35
Are there any suggestions made?

17:37
Once we click on that, we’re going to go ahead and do that same analysis and have an insight created here.

17:42
And what we see is the focus on self checkout seems to frustrate shoppers who value personal interaction, leading to frustration over not receiving timely support in what should be an efficient transaction process.

17:55
Improvements in staffing at checkout and enhancing the customer service training of employees could greatly enhance the shopping experience for Target’s customers.

18:03
So what we’re seeing here is that the most important part in the shoppers buying process, the actual purchase section, the actual section where they’re going to buy something is the most negative experience they’re having primarily because of those self checkout machines.

18:17
And so now we can easily understand the biggest pain point for customers of Target and some potential resolutions there as it relates to the self checkout machines.

18:26
So we understand that overall people are complaining about these issues.

18:30
And while this is negatively impacting the star rating, we want to be able to see if that issue is going to take things further and pose a potential risk for the business overall.

18:40
This is where the risk monitoring tool comes into play.

18:43
Risk monitoring analyzes all reviews that come into the Alchemer platform in real time and determines whether a review is risky or not.

18:52
A risky review is a review that has the potential to lead to a lawsuit, social media nightmare, PR disaster, that negative TikTok moment we’ve all seen in 2026.

19:02
And once it understands if a review is risky or not, if it determines that it is risky, it’ll place it into one of the categories that we support here, including discrimination, harassment, customer safety, employee safety, unfair business practices, theft.

19:14
And then we have some industry specific categories, food safety and improper care.

19:19
But the overall point here is that our AI model is able to alert you of any reviews that could lead to those huge consequences based on the risk profile for your business.

19:30
What I want to show here is how we can connect from signals to risk monitoring to see that customer service if it’s really having a negative impact on the business.

19:38
So we’re going to focus on the middle section here.

19:41
Each of these colors for the category indicates a trend in your risk profile.

19:45
If it’s red, this is your fire alarm system.

19:48
You have more risky reviews for this category as compared to previously.

19:51
If it’s yellow, it’s about the same.

19:53
If it’s green, it’s decreasing or zero.

19:56
So the red is the most important part.

19:57
You have a trend that is spiraling out of control that could lead to the urban outfitter situation that we saw previously.

20:03
One thing that I thought was really interesting here is the unfair business practices risk.

20:08
This could easily be connected to a poor customer service experience.

20:12
So let’s take a look at these actual reviews that were categorized as unfair business practices.

20:17
You can click the count of risky reviews here and you’ll be taken to our reputation reviews page where you can see the actual review text of what contributed to this risk profile.

20:27
And we’ll see here that 90% of clothing items in the store have no price at all and on a shelf with no price listed.

20:34
So they can charge whatever they want when you go to the register.

20:38
Put an end to retail fraud, right?

20:40
This is a serious accusation by this customer and can absolutely lead to those social media nightmares that we’ve seen previously.

20:47
But the real take away here is people do not see the prices.

20:52
That’s issue #1 right?

20:54
Labeling the prices is an operational change that needs to happen.

20:56
But what I think is more interesting is if the customer service was improved, if there were more people that these customers could talk to, there could easily be a reconciliation of the disconnect between what the price is and what it should be, right?

21:09
Training these employees can show them to articulate well why it’s not labeled what the price actually is instead of leading to these risk reviews that could lead to a lawsuit, social media nightmare, or PR disaster.

21:22
Finally, to wrap it up for Pulse AI, here today in an abbreviated demo session, I want to talk about competitive intelligence.

21:29
I’ve shown you how we can analyze your business’s reviews, but with Pulse AI, we’re capable of doing the same analysis not just on your reviews, but also on competitors that you care about.

21:40
With Competitive Intelligence by Alchemer, you have the ability to specify the brands you care about.

21:45
We’ll collect the reviews for those brands with locations around yours in order to determine and generate insights and an analysis for you.

21:53
Competitive Intelligence is quite dense in its offering.

21:56
There are 4 distinct sections that I want to overview here.

21:59
The first section is on our overview tab under the benchmark section.

22:03
Consider the benchmark section your industry overview.

22:06
What are you doing well and poorly?

22:08
What are your competitors doing well and poorly in the overall industry?

22:12
And as we look at Target here, denoted by the black border as the customer, we can see that one of their key strengths is the drive up and online ordering options are convenient and efficient for many customers.

22:24
As you all are likely aware, there has been a huge push for drive up and online ordering, particularly after the COVID era.

22:31
What’s compelling here is that not a single one of their competitors has this as a strength.

22:36
So Target can know one of their competitive advantages is that they are outperforming as it pertains to this drive up and online ordering concept, showing that they succeed quite well in that area.

22:50
On the overview tab, we also have our regional insights section, which is going to show where Target is doing better as compared to the competitors or better as compared to itself or worse in a geographic area.

23:01
So are they performing better in a particular city, in a particular state, in a particular region, the West Coast, the East Coast?

23:08
Are AI models able to interpret the customer reviews as well as the where those reviews are located geographically to give that information?

23:15
Outperforming is going to be the positive.

23:17
Underperforming is going to be the negative.

23:19
This map is fully interactive, allowing you to zoom in and see the points in which you are having the most activity and the highest level of differentiation from yourself or the competition.

23:30
If we look at a -1 here for Target, we can see specifically in Minneapolis that pricing in stock disparities in low income areas are an issue.

23:39
Shoppers report prices and fewer sales on essential items in lower income neighborhoods at Target with basic goods often out of stock, highlighting inconsistently with the brand.

23:49
So there is a perception by customers that the pricing for low income areas specifically in Minneapolis is not accurate as compared to the rest of the brand.

23:58
This is something that an operational manager can go in and resolve with improved training and pricing.

24:04
Final thing I want to know on competitive intelligence here is you do have the ability to do a one to one comparison.

24:10
So let’s say Target is really honed in on Kohl’s as a competitor.

24:13
They can click this compare button, it’ll take you to the brand’s comparison tab and you can do a specific analysis just on what Target and Kohl’s are doing differently.

24:23
This will be our advantages and disadvantages section and we can see some really compelling things.

24:27
The store layout is much more positive at Target as compared to Kohl’s, but what we can see is the phone answering.

24:34
Many reviews mentioned the difficulty of reaching Target stores by phone is a negative as compared to Kohl’s.

24:41
So again, this one to one analysis really allows businesses to identify the competitive advantages and disadvantages as compared to a competitor.

24:49
Final, final thing I’ll note on competitive intelligence is that signals tool that I mentioned previously.

24:54
You can also use it here to ask a question about your business, a competitor’s business, a comparison between the two, or you can even switch this to do a comparison between two competitors, allowing it to be a true market research tool.

25:08
I can go on forever demoing Pulse.

25:09
AII wanted to show you just the highlights here.

25:12
There’s plenty more capabilities offered.

25:18
You know, the one thing that I want to state here as it relates to the Pulse AI functionality is that we are continuously improving it and we’re entering a new era, the agentic era.

25:28
This agentic era is going to completely redefine how customers interact with businesses, how businesses understand customers and the overall perception and actions that can be taken.

25:39
With our Pulse AI agentic functionality coming out later this year, these agents are going to be able to operate continuously adapting to new data with out manual action.

25:49
So we will be automatically surfacing these insights to you about what you specifically care about, knowing that information with our agents and delivering the highest quality, most targeted recommendations without you having to even ask a question to signals.

26:02
We’ll be able to identify a new product release without your input on that being a product release, identify that it is a new product release and immediately deliver insights exactly on how people are perceiving that product.

26:15
And this goes across all industries, whether it’s a new product, a new service, a new offering.

26:19
That’s just one example of the capability of these autonomous AI agents being able to understand and compare new data to historic data, identify those emerging trends, inform you before they take hold.

26:31
With that, I know we’re running out of time here so I’m going to pass it over to Ashley to see if there are any questions that I may be able to assist with.

26:39
There are we have about 3 minutes left so if we can crank out one minute per question, it looks like we have three they all are definitely for you so how does this AI tool compared to pasting customer reviews into ChatGPT?

26:55
That’s a great question I could go on for 30 minutes, but I’ll make it succinct here with something like ChatGPT, you’re limited by two key problems.

27:03
The first one is you have a limited amount of information that you can feed into ChatGPT.

27:09
So with Pulse AI, we’re looking at all reviews, we’re looking at all social media content based on our proprietary infrastructure that feeds into models.

27:17
With something like ChatGPT, you’re only able to fit in maybe 10 percent, 20% of your data.

27:22
And as I mentioned previously, inaccurate data based on a small subset of data is oftentimes worse than no data.

27:29
So it’s gonna give you worse insights if you do something like that as compared to Pulse AI.

27:33
The second issue as it relates to just pasting things into ChatGPT is it doesn’t understand the data structure.

27:41
When you paste something in, there is important context for reviews.

27:44
There is important context for social media posts, the way they’re orchestrated, the way that a threat occurs.

27:50
Pulse AI takes all of that into account to understand the full context connected to the themes to deliver you the most accurate insight.

27:58
Awesome.

27:58
Thank you.

27:59
Plus, like, Oh my gosh, how manual exactly?

28:02
Even just the automation piece is a huge efficiency gain.

28:04
You’re absolutely right, Ashley.

28:06
All right, I think you’ve got time for another one.

28:11
Let’s do this one here.

28:12
Does Signals adapt and learn the context of my business over time?

28:17
Yeah, that’s a that’s a fantastic question.

28:19
Absolutely.

28:20
The entire point of Signals is obviously to deliver insights that you care about with the questions you ask or are suggested questions because again, you can manually type in any question you want.

28:30
But every time we’re generating a response, every time we’re analyzing that data, the AI model is understanding more and more what is important to you for the business and what is important for the customers to the business and how does your business operate.

28:43
So it’s continuously learning, it’s continuously evolving and making more, even more targeted insights based specifically on what you care about.

28:52
The fundamental thing is the more you use signals, the better it’s going to get.

28:55
It’s how it works with every AI product that actually has a continuous learning model.

29:00
Awesome, thank you.

29:02
All right, that last question, we’re going to e-mail you your answer.

29:05
OK.

29:05
We’re going to get everybody out of here on time.

29:07
We’re building trust with this webinar series.

29:09
OK.

29:10
So thank you all so much for hopping in here.

29:13
You’re still all mostly here, which is wild.

29:15
Please go ahead and share your feedback on this session.

29:17
We’re really big on feedback.

29:18
And if you’re a chat meter customer that’s getting to know Elkamer, you’re really going to get to know that we love feedback around here.

29:24
So please scan that QR code, let us know how we did mainly give Morrissey his flowers on that awesome demo.

29:32
And please feel free to reach out to our support team with any further questions, and we’ll see you on the next one.

29:38
Have a great week, everybody.

29:39
Thank you, everybody.

29:41
Bye.