Advice from a Call Center Geek!
Advice from a Call Center Geek is a weekly podcast with a focus on all things call center and contact center. Tom Laird, CEO of 600+ seat award-winning BPO, Expivia Interaction Marketing and Ai auto QA startup OttoQa, ICMI Top 25 Contact Center thought leader discusses topics such as call center operations, hiring, culture, technology, and training while having fun doing it!
Advice from a Call Center Geek!
Will Auto QA Platforms End the Era of Traditional CX Analytics?
Tune into our latest ACG podcast episode, "Will Auto QA Platforms End the Era of Traditional CX Analytics?" where we dive into the world of customer experience analytics.
We’ll explore and compare the tools and techniques behind both Auto QA platforms and traditional analytics methods. This friendly chat is packed with insights about how each approach handles customer data and what makes them unique.
Join Tom as he unravels the complexities and discuss the future possibilities of integrating or balancing these technologies in the world of customer experience management. Whether you're a tech enthusiast or just curious about the latest trends, this episode is for you!
Tom Laird’s 100% USA-based, AI-powered contact center. As the only outsourcing partner on the NICE CXone Customer Executive Council, Expivia is redefining what it means to be a CX tech partner. Learn more at expiviausa.com.
Follow Tom: @tlaird_expivia
Join our Facebook Call Center Community: www.facebook.com/callcentergeek
Connect on LinkedIn: https://www.linkedin.com/in/tlairdexpivia/
Follow on TikTok: https://www.tiktok.com/@callcenter_geek
Linkedin Group: https://www.linkedin.com/groups/9041993/
Watch us: Advice from a Call Center Geek Youtube Channel
This is advice from a call center geek a weekly podcast with a focus on all things call center. We'll cover it all, from call center operations, hiring, culture, technology and education. We're here to give you actionable items to improve the quality of yours and your customer's experience. This is an evolving industry with creative minds and ambitious people like this guy. Not only is his passion call center operations, but he's our host. He's the CEO of Xpevia Interaction Marketing Group and the call center geek himself, tom Laird.
Speaker 2:Welcome back everybody to another episode of Advice from a Call Center Geek the call center, contact center podcast. We try to give you some actionable items. Take back in your contact center, prove the overall quality, improve your agent experience, hopefully improve your customer experience as well. What's going on? It's been way too long. I've been doing a lot of recordings and speaking engagements as podcasts, but I have not done a live LinkedIn podcast with you guys in a really long time. It's been pretty crazy. It's been good. It's been good. I have a lot to talk to you guys about a lot to talk to you guys about a lot that I've learned in the last even probably I don't know 30 to 60 days since, since really I last talked and kind of did one of these, uh, did one of these podcasts. So again, if you're listening, please, if you're on LinkedIn, any questions that you have, please, please, ask away. I don't want to really talk about auto QA. I want to talk more about auto with an A right, just the technology in general, things that I've seen, my changing perceptions on where analytics is going. This could be a little bit I don't know, maybe a little eye opening for you as well.
Speaker 2:I had a chance to speak in Philadelphia at the Philadelphia Museum of Art for a nice CX1 event on roadma your AI two days ago as I kind of record this here on the 25th of April. It was really amazing. First of all, the venue was awesome. Right, it felt like just a really cool. You know it's in the Grand Stair Hall was a really awesome venue, you know. But the questions that I got had a lot to do not just with AI venue, you know, but the questions that I got had a lot to do not just with AI. And again, I don't want to really go down this AI path with this podcast episode. But you know, when I asked the question, how many of you guys and there's probably, I don't know, say, 50, 60 people in the audience, how many people are in their companies, are on path and feel comfortable about where they are from a roadmap standpoint, from AI, and literally nobody raised their hand. Again, same thing that happened to me when I was speaking in Miami. You know, a lot of people are, think they're behind, but I think if, if, if these people are behind, then everybody's behind. I, I look at the low hanging fruit, the, the, the really easy stuff, that that you can utilize, that's extremely inexpensive, that you're not going to have to get a huge push from, maybe, senior leadership to start to move on this roadmap.
Speaker 2:And there's specific tools that I talk about and again, I'm going to post that probably early next week in my full talk that I did in Philly to kind of discuss what I'm just saying. But one of the tools is AutoQA and it's been fascinating to me to see as we go down. I'm looking at my board now 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17 beta customers right now that are in the process of moving to become, hopefully, paid members. Right, three of them four of them right now, I guess four as of today have full paid subscriptions. So we've learned a lot in this.
Speaker 2:You know, having these kind of 20 initial customers, and while AutoQA does a really good job of just scoring calls and scoring agent behaviors, we have found that more and more companies have a kind of a thirst for real analytic data. More and more companies have a kind of a thirst for real analytic data that the tools out there right now, while look cool, the bottom line is they just do not meet the exact needs of of customers, and I think you're starting to see a shift in analytics away from bubbles right, right, that were cool when they first came out and they show really well on demos, but the actual data that they're giving you really have to dig deep and it takes a little bit of time and work when now, with these auto QA platforms, we're just literally asking questions of the data. You could ask questions on agent behaviors very easily, right? Did we read the correct scripted greeting? Did we show empathy? Did we use probing questions? Was the proper disclosures read Like all of that stuff? You know, auto QA platforms can do extremely well, but we're finding that we're getting customers that want more, that they want to ask questions about cancels. So they'll literally create a form right now that just deals with auto scoring, cancel calls and answering questions.
Speaker 2:Right, is this a true cancellation call? Yes, it is. Well then, why did the customer cancel? Was it because it was too expensive? Did they have an issue with a contractor? Like blah, blah, like eight different reasons why somebody would cancel, and normally we would kind of stop there if it was, you know, too expensive, right, because that's what the customer says. But too expensive can mean a ton of things. Does it mean that they lost their job, that they just don't have the money anymore, that the product is actually too expensive? Right, there's a lot of different financial questions that can be asked of that data, data, and so, instead of just kind of looking at the you know the phrase, you know too expensive or can't afford, you know we can go deeper into these actual questions, that analytics maybe can kind of give you that kind of macro level. But now we can really say, out of the you know 2,032 calls that we QA scored last week, you know I don't know 1,722 canceled because of too expensive and it's because of the product was too expensive. 232 did because they just can't afford it anymore, because they lost their job, whatever. But you're getting granular data that you can actually use to create marketing products or marketing tools or campaigns to meet specific customer segments.
Speaker 2:And I was I'm a huge analytic guy, as you guys know. I think it's it's one of the greatest I don't know inventions. I guess it's an invention, one of the greatest tools that we've ever had in CX, and it's totally reshaped our company here at Expedia, right, and you've heard me talk a lot about that, not just from the I always use the phrase, you know the too expensive, and that's what really blew my mind right. So normally when people talk about, hey, you have an analytics platform, yes, I'm like, yeah, we do. It's really cool, it's interaction analytics, because it's on CX1. We can do things like tell you how many, what percentage of, and we kind of stopped there because that's really how far we went.
Speaker 2:Now we probably could have gone to some categories, subcategories and really dug deep. But it's kind of a pain when now we're just literally asking that question right and having auto then think and hear what is being said. So we're asking a question of the data, auto is answering and auto QA platforms are answering questions on the data now, where that's what we said analytics was going to do. But I think analytics is trending. It's macro. If you really want to dig deep into a topic and build a form out, just from a analytics standpoint, I think it blows away just regular kind of keyword.
Speaker 2:Looking analytic platforms right. Looking for intent. Looking at outcomes right. Asking questions on outcomes not just did the agent say this or did the customer say this on a call, it's. It's totally changed my thought process on where analytics is going what we can do now, because so many of these, these beta customers are using this from just a compliance tool, using it just for a retention tool, and again, there's still a lot that are using it for just regular QA, because we're really good at that too. But it's totally changed my demos. It's totally almost changed the marketing that we're going to do on the website, and I think that's why you beta test too, right To to see what the tool, what, how are customers really going to use the tool? And now that we have access to things that that we never really had access to before and I think here's the other thing we can do this Literally we can do this at scale for extremely cheap. So, analytic platforms right, you're paying like a per seat charge. You're kind of getting all of this kind of trending data in extremely useful.
Speaker 2:Still love it, like not saying that there's not a use case for it, right. But especially if you're a smaller contact center like let's say that you do, I don't know, say, a thousand or under calls a day you could pretty easily score a vast majority of those calls and ask the actual questions Like how many times again have we we even look at analytics or we talk to the analytics person who's in charge of that and try to get questions and they kind of give us answers, but it's a little general. You're not really sure from what percentage of the calls that those really happen. Was it all right and now there's no more? There's no more debating it. You know exactly how many calls you did. You know how many times they said that on a call you're asking questions.
Speaker 2:So we're not looking for just. Did they say these specific words? But was the intent to do this? Did somebody say I just? Hey, you know I've lost my job, right? We're not. We don't have to look for the keyword lost my job. The AI understands that. This is the reason why they have to cancel. So it's almost like talking to a human and asking a human being the same questions that they would listen to on a call, but doing it at mass amounts of scale.
Speaker 2:So at this second and I see, you know I know CX1 is doing it and I have not demoed all AI kind of analytics platforms, but I think it's I don't think it's called Enlightened Actions. I don't think that's wrong, but anyway, they have a tool that basically just looks like a Google search bar that you literally just ask questions to, and I think that that's a really cool. That's where analytics hardcore analytics is moving to. Not everybody has that yet. I think it's pretty expensive. So I don't think that, you know, auto, auto QA platforms are going to be the end, but we're a bridge to get to where the analytics of tomorrow is going to be, Because this is a step, I think, above what analytics platforms are doing, because you're digging into the data.
Speaker 2:Now, here's the one thing that analytics does do better, right Is is they can tell you things that you don't know, right? So if, if certain trends come up, if there's, you know, maybe an email went out and you start to get, like these weird calls coming in, right, you can do proactive customer support with that right. Still, use case auto QA is not going to be great at that. But if there's specific things and I think for the most part, that's what we want to know, right we want to know why are customers buying, what do they like, what do they not like, why are they canceling, like all of these type of questions we can put in and literally score and say yes or no, did these happen? And then just kind of take all of that data and make it an extremely useful tool. So it's pretty crazy and the thinking around here has been has totally changed and I think that you know auto QA can be something that's absolutely amazing as people start to, I think, trust the data a little bit more, trust the aspect of how a LLM is scoring and listening to calls.
Speaker 2:But it's been a really cool journey to kind of go through this process, figure out the prompting aspect, figure how things um can be heard on calls. Right, I kind of call it. You know, seeing the unseen um. I think you know. The best example I have is I love there's, there's one client that we're doing a proof of concept for now that they, when it, when a customer calls an agent, the, the customer on file, has to give their name, like their member ID, their phone number, address, something like that, like three pieces of information. But only the A customer on file can do it, not a third party. But if the customer on file gives that information, a third party then can take the call over, like maybe their son or their daughter or something like that. So again, how do you figure out prompting like that? It's been amazing. We've been able to kind of figure out, basically by what's being said on the calls and things, to kind of listen for not specific keywords, because keywords you're going to lose out, right. You have to look at intent, right, you have to look at outcomes and if you think of it through the lens of intent, what are they intending to do? How do we kind of listen for what the end result is, what do we want this to look like at the end and then kind of work back. You know we've been able to do some really cool things from that aspect, I think you know.
Speaker 2:One other thing with this is I want people to try this stuff out. Right, you don't have to buy it, but I think that this is an unbelievably cool use case for what AI can do very quickly to an organization, how it can kind of change the data that we're utilizing, the data that we're looking at, the efficiencies that we can have. Like it is just an amazing. Autoqa is an amazing tool in customer experience because it checks all the boxes right More data, less people, less expensive, better product, quicker, right, no learning curve curve.
Speaker 2:I just want to make sure that people that are doing the auto qa are doing it the right way. Right, because it is not a plug and play thing. Right, it's taken a seven, eight, nine months to figure out the proper prompting for a lot of stuff that are on on the regular aspect of a form, right. Right, it takes a little bit of time, if there's a something proprietary to a customer, to make sure that we're we're hearing for that or listening for that specific outcome. Right, and I get really worried when I see just kind of you know, upload your form and we'll figure it out, or like it's instantly ready to go, cause I think you're going to have you're not going to have as much success as if somebody is really taking the time to kind of build this thing out. We're willing to do that. Right, this is kind of all we're doing, right, we're just auto QA. So I think any of the bring me, bring me your, your regular, but give me your unique right.
Speaker 2:Try to think of some things that you can pull from the data that you've always wanted. I think you know, coming to an organization like AutoQA, like our auto OGGO, you know we're willing and wanting to do those, those kind of really cool things. So again, I think there's a place for regular traditional analytics, but I think it's going away quickly. And I think once you see the power of of asking questions to data is what ChatGPT and Cloud3Sonic do extremely well, but using APIs to do it at scale then we have a tool right now that I think for a snapshot in time because I'm not naive to think that this may progress pretty quickly, but for a snapshot in time you can get some unbelievable insights into your customers, into your agents, into what is actually happening. So I don't know, it's really blown my mind. I've been really excited about all the work that we've done on this. I think the customers that we've had have been excited. Nobody's left us right Like, so everybody's going through the beta.
Speaker 2:The only thing that we stink at and we're not good at is is data on screens? Right, that's coming, but you know, at this point, if you have to, you know, click a cr, click a box on a crm, or you know, did they input the address? Uh, exactly correctly like? We don't see that yet. We just see kind of what is happening on calls, what is happening on on transcripts, so it's basically emails, voice chat, any type of it, tickets, help, desk tickets, any of that stuff that there's actual data from that we can kind of get our hands on and either create transcripts or have a transcript for then. Uh, then we can, we can rock it. So thank you guys.
Speaker 2:Again, not a huge long podcast, but something just of interest of me. I'd love for you guys to check it out. Even if you're just utilizing the desktop of ChatGP, be careful because it's not as secure. But to kind of play around with scoring and asking some questions of your transcript, upload a very vanilla transcript and start querying it right and then think about wow, what if I did this at scale and had a ton of questions to ask and we were doing thousands of these a month? What insights, what data? How much would that be worth? And I think now it's all within the realm of cost for a smaller contact center that's 20 seats up to those enterprise guys as well. So again, if anybody is interested, contact center that's 20 seats up to you know that, those enterprise guys as well. So again, if anybody is interested, just head over to the auto.
Speaker 2:Ottoqacom, again ottoqacom. If you don't want to set up a demo with me right now, that's fine, but on the pricing page there is a, there's just a video demo. It's like 11 minutes so that you can kind of get an idea of what we're doing. But please, please, uh, even if you're not interested in buying or doing anything right now, I'd love to show it to you. I'd love to get as much feedback as I can from everybody. And, yeah, and we'll keep trucking down the road, try to get these back, these bad boys back to once a week.
Speaker 2:I miss a lot of you guys. I miss the questions that I've been getting. Again. The following is still there right, we've actually gone up since I haven't done as many of these in the last 30 to 60 days, so I don't know what that's telling me, but in fact, one of the largest one that we had was about 60 days ago and it was just an upload from one of my talks. So, again, I'll have the Philly AI roadmapping discussion that I that I gave you know live next week. But thank you guys, thank you for all of the the time. Love to talk to some of you on the auto QA stuff. Love to talk to some of you guys on the Expedia stuff. If you're looking to outsource your contact center, we throw auto QA right into it. So it's full, full QA of pretty much 100% of your calls for no cost. But sounds good. Thank you guys. Talk to you all next week.
