Lessons Learned from 100 Conversational AI Implementations


We discuss the most important lessons learned after 100 conversational AI implementations for voice self-service.

What You Will Learn:

  • Key thresholds for change
  • What are guardrails and why do they matter
  • How data is fueling advanced AI interactions
  • 8 required CX functions to properly architect and manage self-service applications

Watch This


Mark Landry

VP of Product,


Brian Morin

Chief Marketing Officer,


Marilyn Cassedy

Director of Customer Success,

Lessons Learned from 100 Conversational AI Implementations

On-Demand Webinar

Brian Morin: We’re calling it a virtual happy hour. I think we are officially in happy hour mode. We are talking about lessons learned from 100 conversational AI implementation. So there definitely has to be drinking involved when you’re talking about sins of the past. In fact, we’ve been doing so many of these virtual webinars lately, I’m beginning to think that alcohol is requirement.

So since we are talking about a lesson learned webinar, in fact we’re talking about four big lessons learned, I don’t know if anybody’s like me, you don’t really learn anything until you’ve suffered some pain in doing something the wrong way. So I think this webinar really is more about hey, here’s about everything that we did wrong, so that we can learn what we did right.

We might even have to apologize for the first few customers while we were still learning and getting things right. And believe it or not those first few customers are actually still with us and they hung in there with us for whatever reason, at least until we got it right.

So hope everyone here is settling in and certainly hope that you had your drink delivered from your favorite restaurant. We’re joining in with you, I’ve got my Corona Seltzer. We were just prior to kicking off, I was decrying the fact that Corona decided that they were going to dial this back to 90 calories but then give up a point five percent alcohol. So, Mark, Marilyn, what are you drinking?

Mark Landry: I’ve got the Distiller’s Select Woodford Reserve thanks to Marilyn and the Dr. Pepper. So I’ve got my little cocktail,

Marilyn Cassedy: I’m drinking an All Day IPA so easy drinking for the afternoon here.

Brian Morin: Okay, well, so I hope everyone is dialed in and we’d like this to be as interactive as possible. I think this will be a little bit more interactive after we all get a drink down. You just chime in any comment, question as we go along in the Q&A box or chat box as we go along. And let’s just try to make this fun and as interactive as possible. Those who are joined in that may not have any familiarity with SmartAction, this is just to give you the super quick 50,000 foot, we deliver AI powered virtual agents as a service. And what that means is that we deliver the full conversational AI technology stack. And we bundle it together with end to end CX services. And that means everything the design, the build, the ongoing operations.

So as much as a large part of that conversational AI stack, is some of our own IP. We’re not a technology company in the sense that we’re just selling software licenses or receipts and throwing them over the fence and wish you good luck on your journey. We actually step in more as a partner that are working as an extension of your team, that makes us responsible for the ROI that was promised and the CX that was promised. And we would like to think our approach is working for us. We operate the AI powered CX for more than 100 brands at this point. And we are also the top rated solution Gartner peer insight as rated by customer reviews. So if you’d like a little more street cred on us, you can certainly look up those reviews and get some firsthand information.

So let’s just go ahead and jump in and say we had four big lessons that we have learned after doing as many conversational AI implementations as we’ve done. The first big one is really identifying the threshold for change. I’m not going to go through each of these on screen but essentially what we are talking about is the absolute bleeding edge of what AI is capable of in the area of self service, essentially all the tools that are required to mimic live agent behavior. We do a level of complexity for some customers that if we get into would probably surprise you. It is omnichannel, a lot of the focus on this conversation today will be voice. All of the customers that we operate the AI powered CX for, we do voice for all of them. We do voice and chat for some of them and we do voice and chat and SMS for others.

So it does go across all channels. And of course the two big AI engine stack one on top of the other. One is the recognition piece, converting waveforms to objects to recognize words. We don’t use Google or Amazon at least not yet. We have found that our own domain specific supervised machine learning approach delivers better accuracy in use cases where there are limited grammars and use case with most customer service use cases, it is a case of limited grammars. And then you have the cognition piece, which is the ability to parse sentences, extrapolate words, associate them and understand intent.

So at least you understand the tool set that we’re working from. If you haven’t implemented any kind of AI, powered self-service over voice or chat up to this point, your contact center likely looks something like this. The limitations of your touch tone IVR, or simple chat bot holds you back from really being able to reach into those simple and complex interactions and offload them from your live agents who really should be only handling what only a human should handle.

So on screen in front of you our time on this is too short really to dig into the complexities on how to identify the threshold for change across every interaction in your contact center. That takes a little bit more in depth call study. But the key really is just about finding the first two or three low hanging fruit interactions that represent the best opportunity for change, every contact center has them. And if you’re seeing at least 25,000 minutes a month that could be automated well then it’s likely worth the effort to the scope, design and build and start your journey.

So the screen in front of you, this is what the goal is. Some folks tend to think there is a clear line between what separates a virtual agent from a live agent, your virtual agent handles all of these call types or chat types, humans handle all of these ones, when in fact it really isn’t so cut and dried.

Much of the time, what we really see is this emergence of a symbiotic relationship between AI and agents where the AI agent and the human agent can actually both participate in the same interaction in the sense the AI will handle a portion of the call or the widest path of an interaction, while the human handles the rest or just the exclusions that go outside the happy path that the virtual agent is hand holding the customer. Marilyn, are you able to describe just a little bit to what I’m referring to maybe in a more real world example?

Marilyn Cassedy: Yeah, absolutely. I think that Choice Hotels is a really good example from one of our partners that’s doing this very effectively. And this is a client who we initially just wanted to sort of build out an infrastructure where we’d be able to capture an intent from the customer, authenticate if it was required.

And then we have a couple of self-serve options. But there’s a number of functions that don’t make sense for them to self-serve, things like sales functions. We just want to gather information from the customer and seamlessly hand them off to a live agent who can kind of finish closing the sale.

Brian Morin: Got it. So let me just give you an example, a customer of ours, Designer Shoe Warehouse. They started with us. A lot of customers will start with just doing one self-service application and then expand from there. In this case, they started with, excuse me, this is what happens when I have the Seltzer. Maybe I should be doing what Will I Am is doing and that’s he’s got a black label that he’s working with. I’ll think of that for next time. So DSW in fact, we’ll have a number of customers who will start this way.

They’ll do what we call intelligent front door, where at the very front end of the interaction instead of a press one, you’re getting a natural language greeting, how can I help you today? It’s a natural language, intent capture. And then we are authenticating that customer and then we are routing them to the appropriate live agent. Or if the intent is something that is intended for AI, they will just stay in automation.

And so after rolling out that front door, then you can begin to identify what are the other low hanging fruit opportunities for automation. In their case, it was things that fell under order management or account management but all started with that intelligent front door. I do have an example we can share on what that actually sounds like. We may be a little pressed for time.

Mark Landry: Say what? Damn! That’s awesome. See you.

Brian Morin: So, what I’m going to do is, I’m going to hold on to this example until we get to Q&A, if we have time during Q&A, we’ll go ahead and play that example. Hey Mark, we can hear you bud.

Mark Landry: So sorry.

Brian Morin: Mark how much have you had to drink over there before we got started?

Mark Landry: Just like five ,six-

Brian Morin: We’re only 10 minutes into this.

Mark Landry: Guys, the boss came in I couldn’t like not say anything. Sorry guys, I thought I was on mute.

Brian Morin: All right.

Mark Landry: Happy hour is a fun time to have a webinar, that’s for sure.

Brian Morin: It’s officially just got started. So this is our second rule once you have identified thresholds for change this, this rule has to do with guardrails. Now, we talk internally about guardrails a lot. This is probably the first time that you’re hearing about guardrails. When we spec a project, we only place a virtual agent in a swim lane where we know it will outperform a live agent.

And we also know that would be impossible to deliver on that promise if we didn’t have the ability to put business rules in place to act as guardrails to keep the virtual agent in its lane, where it will outperform a live agent. And so if the call goes outside one of those guardrails well then in those cases, we will transfer to a live agent. So, Marilyn, I’ve kind of explained that at a high level maybe you can just help us understand where that rubber meets the road.

Marilyn Cassedy: Yeah, absolutely. A client example that comes to mind for me is the home warranty company, who you might call if you had a new home or a warranty on your home and for example, your air conditioning went out.

I don’t know about where all you are, but out here in Los Angeles it’s been really hot the last couple weeks. And so the guardrails that we put in place for that application actually allow us to understand who is the person that’s calling?

And do they have any special needs or circumstances that might mean this is something we really need to expedite and get to a live agent because there’s someone elderly in the home or someone with an illness? Or is this someone who yeah, they’re going to be a little uncomfortable and hot but they’re going to be able to be okay until we can get a service technician out to them in a few days.

So I think that the idea behind that air conditioning repair is really good in terms of illustrating the guardrails and making sure that we know, there’s exceptions to some of these rules, where do we need to build those rails to keep a lot of conversations on track and change the tone of the conversation if we need to?

Brian Morin: So Mark, I know that you do some of the same work with AAA both on the emergency roadside assistance side and on the insurance side. And the fact is, we couldn’t do what we do for them if we didn’t use guardrails.

Mark Landry: That’s right. So we try to use the 80-20 rule as well and sort of designed for the majority of conversations are going to go through this happy path. But as you can see on this slide, if we can identify and authenticate the member, mostly yeah, we’ll be able to do that. Some of them we won’t, they won’t have an account or something.

And it’ll have lapsed and the business rules are that needs to go to an agent to upsell that person or to get a sale in. If we get down the tree and they’ve had all their toes taken out already, they don’t have any more left, we have to transfer that to an agent. And then if their intent is eligible for a self-service, we can obviously help them but if not, we’ll pass those to an agent.

But as you can see, the further down you get in the tree, the more information we’re collecting about that client and about their situation, and we pass all that to an agent. So even at the end when the funnel gets small that’s still a partial ticket that we’re creating for AAA, and those agents are getting all that information so they don’t have to repeat all the questions we already asked the customer.

Brian Morin: Right. Good example. So under here, lesson number three, this really is about data. Some of this, Marilyn goes back to even what we were just talking about the guardrails themselves, so they’re impossible without data.

If we didn’t have the data to work with we just couldn’t really even define the happy path that someone can take with a virtual agent or even define some of those exclusions where they would be transferred over.

Maybe you could share just a little bit into how your customers are using data in some of these areas.

Marilyn Cassedy: Yeah, well, for personalization and prediction, I actually would go back even to Choice Hotels that we talked about a moment ago.

This is a case where depending on the level of status that you have with their organization, so basically how often are you staying in one of their hotel brands, we’re going to give you a different experience at the front door, maybe greeting you by name and talking about thank you for being a gold member.

Or maybe you just stay with them once or twice a year, you have a membership, so we’ll still be able to greet you by name, but you’re not going to be on this sort of special queue for those more elite members. And I think that, that kind of data that allows us to get that personalization can make a huge difference.

Brian Morin: Right, so Mark, one of the more advanced things that we do with data is an area prediction.

Mark Landry: Right. So if we have access to the customer record and we know that for one use case, for retail that they have recently bought a pair of shoes, we can say oh, hey Mark, I see you recently had an order, are you calling about that order? Because it’s very likely that they are like calling for a status on the order, I’m calling with a problem on the order and I want to exchange or something. So that’s very typical.

Also with a lot of our energy companies that we work with, someone will be in arrears on their bill, and they’ll be in sort of a collections status. They’ll call in for some other reason, but we know that they have an outstanding amount due that’s past due on their bill.

And we’ll just sort of funnel them off to the side for a minute and say, okay just so you know, you are past due on your bill by X number of dollars, would you like to go ahead and take care of that today? And we’ll take care of it and then let them go on to their intended reason for the call.

Brian Morin: So I am going to share a real world example here for the audience. This, I think, is a good example of how we use data to drive different experiences. This is for Electrolux, so the second largest home appliance manufacturer in the world, the umbrella for a lot of brands that you know and are familiar with.

What I can do is, I can just play these samples, they’re pretty quick samples. But we’ll just give you context for how Mark and his team, how they’ve designed for different scenarios and how we use data for these scenarios. So in this first example, this is an example of a repeat caller. We know in this case, that they already have a product that’s registered.

And you can see that the time to route because everything that we do with Electrolux is triage for the person with the beginning of the call, authenticate triage for some questions, gather information, then route. But you can see if we know that some of these data sources have already been captured we don’t have to ask some of those questions. And we can really reduce the friction for the customer to get where they want to go. So let’s play this repeat caller example.

IVA: Hello, this is IVA, I see that you recently called. Are you calling about your side by side refrigerator?

Customer: Yes.

IVA: Thank you, I’ll connect you with one of our representatives. Please wait while I transfer you.

Brian Morin: Okay, so in that case, we used as Mark talked about prediction, what that they called in likely was about the similar issue they were already talking to us about in a very quick route. So let’s go over to this next caller.

This is somebody who is calling in and they have registered their product, we will prompt somebody calling in about a product registration so that way the live agent doesn’t have to deal with product registration. But here’s somebody who already has their product registered. This is what this experience sounds like.

IVA: Hello, this is IVA. I believe I’ve located your account. To confirm are the first three letters of your last name M-A-N?

Customer: Yes.

IVA: Thanks. Now please say or enter your five digit zip code.

Customer: 92220.

IVA: I have your address as 4953 Oak Avenue, Desert Springs, California. Is that correct?

Customer: Yes.

IVA: Are you calling about your side by side refrigerator?

Customer: Yes.

IVA: Thank you. I’ll connect you with one of our representatives. Please wait while I transfer you.

Brian Morin: Okay, so let me share you now this example of, this is a brand new caller, where we have not gathered any information you can just get an idea of what this sounds like.

IVA: Hello, this is IVA please say or enter the 10 digit telephone number that we may already have on file for you or the 10 digit telephone number that you want to use to register your appliance.

Customer: 5678886.

IVA: From caller ID I have your name as Peter Samuels, is that correct?

Customer: That’s correct.

IVA: Please say or enter your five digit zip code.

Customer: 19947.

IVA: What’s the first line of your address, like 123 Main Street?

Customer: 304, North Bedford Street.

IVA: Please say your apartment or unit number or just say you don’t have one.

Customer: I don’t have one.

IVA: Thank you. Including the month, day and year, such as January 1st 2016, please tell me the date of purchase.

Customer: I don’t have it.

IVA: Was the date of purchase within the last eight years?

Customer: Yes.

IVA: Thank you, we’re almost done. Please say the appliance serial number.

Customer: BA 43453622.

IVA: Your appliance is now registered. Thank you. I’ll now connect you with one of our representatives.

Brian Morin: Okay, so that might have been a little painful to have to sit through and listen to that minute and a half call. One thing I do like showcasing there is the alphanumeric capture is fast as he can say it.

But as you can see what this does for the rep on the back end on the Electrolux side is that this has significantly cut down the average handle time that they would otherwise have to do. And getting back Marilyn to some of that symbiotic relationship between the AI and-

IVA: Hello this is Iva. Please state-

Marilyn Cassedy: He’s back.

Brian Morin: Okay, I guess so. Okay, so where are we at? Lesson four. All right, virtual agents, what do they need? They need a team of CX experts. A lot of times when we do get into these conversations, particularly with folks who have yet to make a journey to artificial intelligence as part of their self-service strategy. A lot of those conversations are almost exclusively around the technology. Of course, the technology is very important. But the technology is really only just a very small piece of the overall picture.

It takes real humans to do something with that tool set. Marilyn, this is a conversation we have with every prospective customer on expectations to understand that when you’re entering the world of conversational AI, this is not just an off the shelf, out of the box experience where you design build and done.

These are conversations that you’re trying to have with machines that’s something that’s enormously complex. And the way that you really get to those frictionless experiences is not just through a day one effort, it really is a journey effort, week in and week out, iterating. And maybe you can just speak to that just a little bit.

Marilyn Cassedy: Yeah, absolutely. We won’t get too far in the details because it would take a little more than we have time in the webinar. But I think that the high level takeaways are that, this is definitely a journey and in mapping out our client journey, we find that our customers engage with a huge number of resources from across the organization.

We have a dedicated CX team comprised of six or more departments, depending on your specific needs, to make sure that we’re going through all of these processes, both at the design stage when we’re identifying use cases and Mark and his team are writing them out for us in the development phase, when a team of engineers are picking this up and making sure we have those data integrations in place to make it work.

Through testing when our QA team puts it through its paces and we’re able to pass it over to our client for user acceptance testing. And then after go live. Go live is in many ways the finish line here but there’s so much more after that because when people start to engage with the product, we can actually see what is happening in the real world and continue to tune and enhance based on that feedback.

Brian Morin: Right. So we’ve stepped through the kind of process methodology a little bit for those that just want to, you kind of looked through the looking glass and say, what does this actually look like? We can say, hey, this is what at least looks like for our team. If you were taking your own, do it yourself, your path, your area of voice automation, you would likely be building out a similar team.

Every face on screen has an entire team that’s behind them. These are distinct CX functions specialists where we have teams that are trained in these specialties. Some of them are on the front end on design, some have to do with the build while others have to do with the ongoing operation.

And Marilyn I know you intersect with a couple other teams on the back end on the ongoing operation. Mark, you’re all the way at the front end this with your team on the CX design. And we talk a lot about this, what we call our human centric design, making sure that the AI is in your words ergonomically serving somebody’s needs.

Mark Landry: That’s right. So my team in CX design and strategy is involved even in a consultative role at the beginning, during the sales process once the CX consultant and Liam the solutions expert and Arnold the project manager. Even as they’re all getting involved with a potential project, they’re consulting with my team. And my team is, us being in California, in Southern California, we’re around Hollywood. So we are a team of screenwriters. And what we do is, our training is writing conversations, We’re making up conversations in what if someone said this? And what if someone said that? What would be a good response?

So there’s a lot of education and experience that comes to our CX team in the design area from Hollywood, from having written thousands of conversations experienced by tens of millions of people across the globe. All the way through quality assurance, where we have game testers who have cut their teeth on video game testing. So they are trying to break this bot, whenever they’re testing and they do a really good job. Analytics and tuning like Brian and Marilyn alluded to, after go live the app gets out into the world and we get actual humans using it, we get all that data.

And I had mentioned, I think Will had a question about what about a robocall or call again into the app and what do you do about that. Our analysts will identify that and remove it from the statistics so that they’re not skewing your stats for your executives or your customer base or your CSAT scores.

And we’re also identifying areas where we can improve and tune the app. These prompts may be confusing to a certain age group of people maybe let’s split that out and have it worded a different way for them. Those are the kind of things that we do and that’s why it’s a circle and it’s an ongoing improvement through the lifecycle of the application.

Brian Morin: So Tammy has a question or just wondering, what kind of feedback is being received from customers? Now, we know that our own clients are doing their own surveys and measuring of CSAT with their customers who are calling in, those are things we track very closely with them.

But Marilyn we track a lot of data and a lot of different types of data so we could dig into that data, identify are there friction points? Are there hang ups or transfers in certain areas? Or timestamps in a conversation where we know we need to dig into deeper, listen to call recordings which puts us in a place where we’re either expanding grammars, finding data sources, do a A/B testing. This is all part of the secret sauce that you do with your team.

Marilyn Cassedy: Absolutely. And I think that when we asked our customers for feedback or review, we started to hear two different things. This product works great for me and this solution was custom built and performs as designed and that’s a fabulous thing to hear about it, but they always compliment our team as well.

This is a hard working group of people who are looking to bring you the needle not the haystack. And the data that we can get you from the calls coming into the system is really interesting and can really have big business impact.

Brian Morin: And there’s a follow up question around that, is that are customers willing to put in the effort required dealing with a virtual agent? I think some of that Mark, that’s something that we talked a lot about before going back. If were to rewind time, we don’t talk about that nearly as much now. We know that if somebody would call into the system, if it’s not human sounding their anxiety level goes up, they’re less likely to interact.

If it’s not being personalized or predictive to show that there’s some intelligence involved, they’re less likely to interact. Do you design in some cases where, in fact I know you do, in some cases where you give the call or sometimes an option? They can either wait on hold for a live agent or they can try to use self-serve with a virtual agent right now. And invariably, I think that you see more and more of that, just opt for the self-service.

Mark Landry: Yeah, absolutely. So there are two aspects of that. One is the demographics that we’re talking about, like you just mentioned, more and more people are adopting self-service options and especially in the millennial generation as they grow up. But there’s also a design aspect. So are people willing to work with a bot? Well, yeah if the bot is designed well enough.

I’ll give you an example for roadside assistance. We were tasked with redesigning AAA’s roadside assistance bot, which initially in its original incarnation had basically interrogated the caller, who is stuck on the side of the road and has a problem and they have tension and anxiety they need solved.

We took that tension and that anxiety and said, okay, what does this person want to hear? They want to hear, what’s your problem? And as soon as we understand that problem, they want to hear, that’s no worry, we can take care of that for you.

Okay, now give me your name and membership number. Instead of interrogating them up front, we design our experiences with empathy so that customers are more likely to stick it out and not press zero to go to an agent.

Brian Morin: Yeah. I know that we are at the bottom of the hour but I will give this one example, Mark, you’re just kind of speaking on that whole design sensibility-

Mark Landry: The makeover issue?

Brian Morin: This is the before and after. Yeah, right. The makeover and this is actually gets into some of the question that was just asked, don’t know that we have time to play the app after somebody wants to hear it after in Q&A, we can play it, but we can at least play the before. They can see the results between both designs on screen and this had nothing to do with technology, by the way.

This just had to do with the CX design. And the before Mark, I believe this is kind of what the client said, hey, here’s what we got to start with. But then ultimately you were able to point out the inefficiencies of doing a call like this and it led to a significant uplift. So I’ll at least play the before and then Mark we’ll give you a chance to comment to it.

IVA: I can help you book a new reservation or change, cancel or confirm an existing reservation. Which would you like to do?

Customer: Book a new reservation,

IVA: Great. Let’s get started. How many rooms would you like to reserve?

Customer: One.

IVA: How many adults over the age of 17?

Customer: Two?

IVA: And how many children?

Customer: One.

IVA: How old is the child?

Customer: One.

IVA: To confirm, there will be two adults and one child.

Mark Landry: Right.

Brian Morin: It just stopped on us?

Marilyn Cassedy: I think it might have.

Mark Landry: Well, that was one of the issues. So I’ll go into the commentary quickly.

Brian Morin: No, it’s still going.

Mark Landry: It’s still going. Okay.

IVA: Great. Please tell me your desired check in date, such as May 9th 2019.

Customer: January 25th 2019.

IVA: Please tell me your desired checkout date.

Customer: January 26th 2019.

IVA: To confirm I have your check in date for Friday, January 25th 2019. And check out date for Saturday, January 26th 2019. Is that correct?

Customer: Yes.

IVA: Great. Please say what city you’ll be traveling to.

Customer: San Luis Obispo.

IVA: Did you say San Luis Obispo California in the United States?

Customer: Yes.

IVA: Thank you please hold while we search for the best possible rates.

Brian Morin: So that was painful for me to even just sit here and listen to.

Mark Landry: Right. Yeah, the issues there, we try to do our best and give counsel to our clients but sometimes the clients say no, it has to be this way. We want exactly this. And so we say okay, you’re the boss, you’re the client, you’re paying for it. And in this case they said, we need to know how many rooms, how many adults are going to be there, how many children, the ages of the children and the confirmation to confirm you have two adults and one child.

And then the confirmation didn’t say, is that correct? It was just like you have one child and then silence. And the person doesn’t think about, oh yeah, I need to say yes to this. And then dates, and then that you confirm the dates. And then the city confirmation includes the country but we know that this is going to be in the US, most of these are in the US. And if they said that it was a city in another country then we could ask a clarifying question. So what we did in the re-do or the redesign was, alright look, we know 100% of these calls are going to go to the agent for the final payment, right?

They told us, we’re not going to take the payment over the phone the agent needs to upsell them. At the end of the day, we’re just saving the agent time by collecting all this data. I said, all right, well look, let’s get the number of rooms, the number of human beings, the number of travelers don’t confirm right away. And then the city and state confirmation, not the country. We don’t need to do that unless they’re actually going to travel to another country.

So all of that plus the biggest sin of all was the first prompt in this thing says, I can book a new room or change, cancel or confirm an existing room, which would you like to do? People are calling in to book a room, like we looked at their stats and it was like 80, 90% of people were calling in to book a room. So the first question should be, are you calling to book a room? Simple. By the end of the first menu here, you don’t even remember what your choices are. So if we have time to listen to the follow up, then that’s what you’ll hear as a revision.

Brian Morin: Sure. Well, if somebody asks for that as we go into Q&A right now, we’ll do that. I’ll just skip here to the next demo or the next steps and then we can go into Q&A if there’s any questions that we didn’t get to. And I would just ask you if you’re sitting at your keyboard now, bang up the question now and we’ll answer whatever comes through. So as far as next steps, you can see it engagement on screen, info@smartaction.com, most do start with one or two paths.

One is just requesting a demo, you may want to see or hear what we’re already doing in your vertical with someone adjacent to you, there’s a high likelihood we’re already automating one of the high volume interactions over voice and chat that you’re already seeing in your contact center. So we can tailor something for you in your space or may just be sitting down with you doing what we call free AI-Readiness Assessment.

That’s where it’s talking to you about before the threshold for change, we sit down, we look at the characteristics of your call types, your chat types, we understand volumes, we understand what is right for automation, what are the expected completion rates, what are the expected handle times that would be with a virtual agent and that ultimate deflection from your call center then we could help you with that ROI calculation.

So if you like that engagement, just let us know, happy to chat. And once this webinar is over we will send you a copy of the deck and then an on demand, then you can access the share and stakeholders as needed. So with that said, let’s go ahead. In fact, I don’t see any other questions that have come in right now. While we wait maybe to see if there’s any more, I will tee up just a couple of the most common that do end up coming in. And one of the most common ones Marilyn is this is, when changes do need to be made to the application, how difficult is it to work in changes?

Marilyn Cassedy: I’m going to give you the typical consulting answer, which is it really depends. Are we doing a complete makeover, like what we talked about with get a room, that’s a little bit more on the sort of plastic surgery end of things. And so it definitely took time and it was an iterative process where we worked together.

On the other hand, if we just want to clean up one prompt because it’s a little unclear how the caller needs to respond, we can get that done in a matter of days. So bigger changes will take more time, but some quick changes can happen really overnight.

Brian Morin: Yep. A common one that we get and I will just tee this up for the audience is oftentimes when somebody that’s using, like a Genesys and wonder well, why would I use a SmartAction IVA over a Genesys IVA?

Mark Landry: Well, this question comes up quite a bit and it’s that we tune the experience, we design the experience, you’re not having to do that in house. We do the analytics for you, we provide briefings of those analytics and where we might need to tune or go in the future. And our voice is human, it sounds like a person. So it’s just a cut above the out of the box, sort of Lego set that you’re going to get with a do it yourself offering.

Brian Morin: And we know that organizations can have success in do it yourself platform perhaps if their roadmap is chat and only chat. But it is when you do, get over to the area of voice where there is just far more complexities in voice to deliver a stellar experience.

And so we are part of that market where we are delivering not just technology but also the manage services around that and at least up to this point in the market place, that’s just where the market is when it comes to voice generally opting for a managed service approach. I don’t see any other questions that are coming in at this time.

So I don’t want to commandeer our audience here with questions that they are not handing out. So I think that if there’s anything that we didn’t tackle with you, of course, we’re happy to engage you one on one after this is over. Mark and Marilyn, any closing remarks from your end?

Mark Landry: No I appreciate the time-

Marilyn Cassedy: This was fun. Appreciate the beverage and I think that it is good to look back at those lesson learned, right?

Mark Landry: Indeed.

Brian Morin: Good. Well thank you everyone for joining and we hope to continue the conversation with you soon and look forward to our next virtual happy hour. Thank you everyone.

Mark Landry: Cheers.

Marilyn Cassedy: Thanks.

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