In our latest episode, on Napier’s Marketing B2B Technology Podcast, Mike, Managing Director of Napier, interviews Indrek Vainu, co-founder of AlphaBlues, who discusses why there is a need for chatbots in B2B marketing and shares how AlphaBlues technology helps his clients be successful.

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Transcript: Interview with Indrek Vainu – AlphaBlues

Speakers: Mike Maynard, Indrek Vainu

Mike: Thanks for listening to marketing b2b tech, the podcast from Napier, where you can find out what really works in b2b marketing today. Welcome to another episode of marketing b2b technology, the podcast from Napier. Today I have Indrek Vainu, from alpha blues, alpha blues promises to deliver conversational AI for the enterprise. So they’re delivering chatbots that enable your customers to interact with the website at any time. Welcome to the podcast Indrek.

Indrek: Hi, great to be here.

Mike: Thanks very much for coming on the show. My first question, I have to ask this because I keep hearing from, from my clients, how difficult it is to create usable chatbots for b2b. Do you actually see b2b companies using chatbots successfully?

Indrek: Yeah, that’s a great question. So yes, there are companies that are using chatbots in the b2b environment. But you know, touching on the first thing you said, you know, it is somewhat difficult to create good chatbots. Because ultimately, when you think about it, you are creating something equivalent to a digital human being. And that is supposed to understand people’s questions, talkback provide intelligent, helpful answers. So sort of that on its own already is, is a challenge for sure. But, you know, there’s definitely ways how to do it so that these are helpful for customers and also for the business itself.

Mike: Fascinating. So, I mean, obviously, there’s a lot of work involved in creating these chatbots, and people are investing the time and clearly getting the benefit. Why do you think there’s such a move to put the effort into offering chatbots on b2b websites, rather than perhaps putting the money into some other activity?

Indrek: Yeah, that’s a good question. So I think in general, what we’re seeing now is, and there’s a movement towards how do you actually communicate with your customers? And I think one of the things that has been happening in the background over the past few years is really the rise of messaging apps. So I think, you know, Whatsapp now has 2 billion users messenger has 1.3, or 1.5 billion. And these are huge, right? These are mean huge, huge networks. And they are used so often. I mean, I think people open WhatsApp, 30 times per day on average, right? And why these are used so much is that you, you can reach people, you know, if you want to reach someone, they’re in your network, you reach them, and they actually answer you in, you know, a minute in a second, at least an hour or so. and that type of thing that’s happening with, you know, person to person communication, actually, the expectations are carrying over also to the business world. So no longer is it sufficient that you have an address, you send an email, and you wait for the reply for three or four days, that sort of already is in the past, because the world is more immediate. People want answers quicker. And really the speed of reply, can in many cases, you know, seal the deal or lose that customer to your competitor. So really, really the customer experience is something that comes to matter more and more.

Mike: That’s fascinating. And certainly, I think I’ve seen that trend for more immediacy. So I definitely agree with that. And in terms of creating chatbots, I’m really interested, I mean, you’re using AI for your chatbot. I mean, presumably it means that this makes it easier than the old method where people had to effectively forecast all the questions the bot might be asked and then provide the answers. And explain a little bit as to how your system alpha blues works.

Indrek: Yes, sure. So what I could spend hours and days on the topic of how to create a good job but to put it very briefly, what is the central thing you have to solve and then I’ll touch upon if you’re a business what you have to think about before you create the chatbot. So the central thing you have to solve is that you know it comes no surprise people are different, right? And with that the way that they ask questions is absolutely different. So if I approached let’s say a bank or a company and I want to know something, I would ask it in a in a I would phrase it in a way of like, you know XYZ, you would phrase it in a way of ABC and now the computer has to understand that X, Y, Z and ABC are actually the same thing and provide the same answer. And what’s difficult with computers is that they don’t have inherent knowledge of the world. So they don’t understand what things mean. But they’re really good at just, you know, cramming things. So they are really good at just, you know, going through a bunch of information, and then finding patterns and saying that, you know, x means this and y means that. So, this is what you have to teach your bot is that if people ask things in literally a million different ways, the answer should always be a. And this is, you know when people talk about chatbots, and sometimes they say that the bot doesn’t understand me is that they fail to understand that the way that they ask questions is very different from the way that their peers or colleagues ask questions. And you know, this is the same for if you’re looking at Siri, or Alexa or Google Assistant, any of those things is that there is no way that you can ever be able to come up with all the different things that people might ask the bot like, it’s literally impossible, right?

So that’s the thing, where you start, however, the way around this is that you create a bot. And what we usually suggest companies to do is, you know, think of think of two axes, think of the frequency of things that are asked. So you want to aim for the most frequently asked things, things that are asked like hundreds or thousands of times per month, right? And then think on the second axis, think about the solve ability. So can the machine on its own actually solve it. And we usually, you know, break down solvability into three buckets. You know, bucket number one is that the machine can just private provide an answer without any third party information. bucket number two is that with some API connections, the machine can give you an answer. So for example, you say, you know, I want to reset my password, then you know, the machine is connected to the backend system, and then is able to perform the task. And then bucket number three is something where there’s always a need for a human. So let’s say a human needs to verify your information from five databases, and then do a cross check with the sixth one, and then you know, off you go, right. And what you actually want to look at is where you have the highest occurrence of topics that fall into buckets, one or two. And these are the things you should automate, because you have a lot of volume. And the machine can solve this on its own. And this is, it sounds relatively simple. But over time, when we work with enterprises, this is what comes up again and again, is that you know, pick your battles carefully, where you actually want to deploy the machine so that it’s able to help the user and also able to solve the request on its own. And of course, the things that the machine cannot solve on its own. There’s a fall back to a human support agent, and they take over the conversation and helps the user out.

Mike: That fascinating. I mean, how do you as a user of a chatbot, for example, the alpha AI, how do you tell the system to fall back is there are certain questions that automatically fall back to a to a human?

Indrek: Yeah, so how so we have built an end to end our chat automation product, which means that you know, you have the whole interface of building the bot where you want to deploy it either on the website or in your internet or in, you know, Facebook or WhatsApp. You build it to train it, and then you have the also the live chat system where the handover happens. So if the bot cannot help you, it has the conversation over to human agents. The solution works is such that if you come in and you ask us to ask a question, then you ask it in natural language. And what it’s able to do is it’s able to take the phrase that you asked from the bot, and it’s able to match that to its trade database of phrases. So it doesn’t need to be an exact match. It can be a partial match. And it sort of has its own confidence scores. So let’s say if the bot is 80% confident it gives you an answer. If it’s let’s say between 30 and 70%, confident it gives you three answers in a way of you know, did you mean a, b or c? And if it’s let’s say less than 30% confident, then it directs you to a human being. And once the conversation is going, you ask a question the bot is answering. Then at the end of each, each content, each answer or conversation we asked was it helpful? So the user has an explicit way of saying, you know, yes, this was helpful or no, this wasn’t helpful. And when they say no, it was not helpful, then is when we connect the user to the customer service agent because you want to solve the issue, you don’t want to leave the customer in the dark, saying, you know, hey, we cannot help you sorry. But then what happens is that you connect the user to the right agent, who knows the topic that you want to ask about. And also, who can help you, let’s say, you know, speaks English, and German. And if you’re a German customer, then that agent can help you, and also in the right type of priority. Because you know, if you’re a business, then if somebody comes in and asks about when is your office open, and then another person comes in and says, Hey, I want to order like, I know 10,000 units of your product, then clearly, the second customer is more important for you, because you can actually close a large sale, right, so you want to prioritise also those by relevancy to your business. But the big part of the bots that we see are actually, how do you build a bot to handle the bulk of the incoming messages and the front end, and then if the bot cannot help, then transferring that to the right agent at the right time in the right order priority, so that the people are actually doing just the work that they need to do so a big part of why we started the company in the first place is humans should be doing things they are good at, which is solving difficult problems and creativity. And machines should be doing things that are simple, and things that humans should not waste their time on.

Mike: Fascinating. And I have to ask this question, we’re afraid? How often does a visitor type in a question and it hit that 70%? confidence level? So you feel confident enough to? To give a single answer, is there a typical range for your customers?

Indrek: Usually, when the board is built, and the company starts out with a chatbot, they have the bot knows about maybe 50 or 100 topics, because that’s the word that they operated. You know, if you think of, if you think of, if you think of the finance world, it’s all about, you know, pending payments, when will the transfer? How do you get a credit card, right? If it’s like b2b, it’s about order tracking, or ordering new supplies or things like that. So, so the world is not, it’s not really infinite, it’s kind of finite, and you pick those 50 to 100 topics. So there, what you do is, you know, if each topic has related about, I don’t know, 20 phrases to it, it’s pretty okay to start with, so, you know, you have maybe around thousand 2000 phrases that the bot should know. And then if the world is not infinite in that amount of topics that the board gets asked, then, you know, fairly often you just give the one answer to the customer, of course, you know, then the customer has follow up questions, dialogue. And that’s where, you know, the additional training and understanding of context, and those things come in, so that you provide for a fluid conversation. And so we spend a lot of time when actually creating the product where, you know, companies have dedicated bot trainers where they can see how the conversation is going. What was asked when, and then what should that relate to? Because a lot of the things people ask me depend on context and being really able to help them.

Mike: Sure. I’m also interested, you know, you quite often when you get on a website, there’s a bar, it doesn’t answer your question, it then tries to drop back to a real person, and maybe there’s nobody stopping the line, or perhaps they’re all busy. How much of an issue is it when the bot tries to drop to a real person, and there’s no one available.

Indrek: So that definitely is an issue from the point of view of the user. So if I’m a user and I, if I’m a user, and I reached out to your company, you know, most likely I have a problem, because I just I don’t do it for fun, right? I think that what I’m always saying is that, you know, if a person calls your company, they either love you a lot, or they hate you a lot. Right? And it’s mostly the latter. So, you know, you just don’t pick up your phone and just call your own telecom companies just for fun, you know, because you have some time, like you’re only calling the last resort and that’s why chat is good, because it’s something that you know, it’s so effortless to use, so that you reach out more, you don’t just go in the last instance when you’re full of rage and you want to bend out right. But coming to the question, so, when the bot is giving an answer. Then of course you want to be as specific with the answer that you can have. But when you transfer to the human, what would we have found over time and also then we built this into our function is that the bot monitor If agents are available, because you don’t want to say, Hey, I will connect it to an agent and then saying, well, all agents are busy. So, we’ve in our solution over the past year built-in as a thing where the bot monitors and pings, yeah. And if agents are available, then only does it do a handover. If agents are not available, then it leaves, essentially, in the chat window, it leaves an option saying, Hey, leave us a message, like put your email and your question and we will come back to you within the next few hours or a few days, right. So you always want to create the user the feeling that somebody is dealing with their problem, and, you know, you have received their problem and you’re dealing with it, I think that’s from the user point of view. Very, very important.

Mike: I think that’s a, that’s a great point, actually, you know, if you send an email to a company, you, you always kind of have this feeling that nobody’s read the email, whereas giving that feeling that someone’s received the message and is dealing with it, I think is a That, to me, is the summary of why you’d want to do this. Is that what your customers are also saying they want to give that feeling of really paying attention to customers?

Indrek: Yeah, I mean, you, you want to be there for them and say, I genuinely care about you as a customer, and I want to help you. So the intent is there to help you. And even if you say, you know, it’s gonna take us a little while or, you know, we got your message, and we’ll get back to you, we’re still working on it, you know, you as a customer, if you think of personally, you know, you feel good, you know, okay, like, that’s fine. Somebody is dealing with it, and, and they care. And, you know, this, I think touches upon the thing I was alluding to earlier is that, how do you create customer loyalty in today’s age, when everything is available for everyone everywhere? And many products are the same? I mean, you know, they’re commodities. So it’s really the customer experience that stands out, you know, being there for them quickly. But of course the question for large enterprises, and you know, we work with fine banks and telecom companies is that, you know, great if you have 1000 customers, then you’re a small business, that’s fine. You can do that. What if you have a millions of customers? How do you make them feel all personalised and special. And this is really hard, right. And this is where these types of automated chatbots come in, where you know, you’re able to, you’re able to help your customers in a way that they feel that they’re being taken care of, they see progress, and ultimately, you’re there for them, and generally show you want to help them and you are able to help them. I think this is, in today’s day and age, this becomes more and more important as time goes on.

Mike: Perfect. I mean, one of the things that interests me and I don’t know if this is true or not, is that there’s kind of an I guess it might be a myth that it’s younger people who like using chatbots, and older people tend to shy away from them. Do you think that’s true? Do you see that reflected in the way Alfredo ironed out for chatter used?

Indrek: So I mean, definitely the younger generation is more accustomed to messaging, right. So you know, WhatsApp, messenger, all those things. But ultimately, it comes down to convenience. So if if you see the chat window, either in your mobile app, or it’s available on social media, or it’s, it’s on your website, and you get help from there, then next time, if you have the same issue, you will go back to the channel that helped you. Of course, you know, phone is not going to go away. AI is not going to take over the world and replace all the customer service staff. I mean, I think this there’s so much hype being in that field, that sometimes it feels ridiculous, I think, you know, people are not going to all lose their jobs. Because ultimately, you know, some things you just, you know, you have to call, you want to resolve them with a person they’re taking you taking a problem and solving it in real time. I think that’s absolutely fine. But you know, the nature of issues that users turned to companies are different. And if you see that, you know, chat is a channel where I always get to reach the support agent or I get an answer from the bot. And that works for me the next time you are very likely to go to go back to it. And you know, this is one of the reasons we’re now we’re also offering that we can build our chat bots into WhatsApp, because I think you know, if you think about trends, in at least in the b2c context, in in many cases, companies are, you know, users are always quicker to new environments than companies are following them. But if you think of To share volume of, you know, billions of people using WhatsApp, then most likely, you know, your customers are there as well. And if you can be in the environment where they are, most of the time, and you can help them in their environment of choice, instead of pulling them to your own channels and forcing upon them, your own methods of communication, customers appreciate that a lot, because it just takes out all the friction, and they know you’re there for them. And it’s easy. So. So, you know, I definitely see that, you know, more and more companies are, are coming to WhatsApp are creating their smart solutions, and you know, with that increase their customer loyalty.

Mike: Fascinating. I mean, you said something there that I think is very interesting, you said, you need to help people on the channels they prefer, rather than dragging them to your, your own channel. So you obviously believe it’s really important not just to have chat on your website, but also to offer it across social media platforms.

Indrek: Yeah, and of course, it depends what type of business you’re running and who your customers are. But you know, if you are going, if you’re going the b2c route, then, you know, be there where your customers are, because the, the, the friction, and you know, you might think how we, you know, we, as its digital society are spoiled, so that, you know, making a phone call feels really, really hard sometimes. And, you know, it’s not just, you know, you press couple of numbers, and then there’s a tone, and you start speaking to someone, so it’s, it’s not physically hard, but, but there’s, I think there’s like a mental barrier is that, you know, you don’t have to go somewhere, I have to look it up, and most likely, I won’t find it, and then I’m disappointed and why and I’m not going to do it. But if you’re right there in your app that I use all the time, and I know that you’re just like, not like five clicks away, but you’re one click away, it is so much easier, it is so much easier. And you know, ultimately, the customers will do things that are easy and simple for them. So for brands to keep up, it makes sense to be where they are. So you know the same thing, like when people move to Facebook, then it took companies a bit of time to see like if this social media is actually something real. I think now in 2020, like, nobody doubts that nobody asked like, should we be there, it’s kind of you know, must have that you’re there because everybody’s there. And I think, you know, and of course, customer preference has changed. So if you’re a large company, you know, it’s it’s sometimes difficult to keep up with all the tiktoks and the snapchats, and the WhatsApp’s, where you have to be, but you know, when you see a platform, becoming a major, major player and a dominant form of communication in that space, I advise companies to take it seriously because it can create them a competitive advantage over their competitors, to be where, you know, they’re competitors or not. And I think, you know, if you look at companies with, you know, excellent customer service, like Amazon, then you know, these companies set the expectations for everyone else. And, you know, that becomes also difficult because, you know, you’re not just competing against the companies in your own country or in your own city, you know, you are being compared globally with everyone else. So, the US users expectations to service and, and, and answers, immediacy is just growing. And I think, you know, this is something that, you know, we, we, we help companies to deal with. That’s, that’s

Mike: Very interesting. So I think, I mean, what you’re saying is that, even if b2b might be harder to, to manage the same level of interaction on a chatbot actually, people are expecting it because of what they see in their consumer lives. Is that what you’re saying?

Indrek: Yeah, absolutely. Because, I mean, your b2b customers are, are humans. And they in their personal life use WhatsApp for messaging anyway. So now they have repetitive orders from you, or they have some, I don’t know, it support troubleshooting or, you know, whatever you’re providing as a b2b thing, or, you know, they need some quotes. If you provide them as a similar experience that they are used to in their personal communications and somehow they see that oh, like b2b doesn’t need to be this clunky hard enterprise thing, but it actually can be also simple then they’re like wow, you know, I take simplicity that works any day over the complexity that doesn’t work right. So I think you know, also what you see in many of the Enterprise Solutions these days are you know, they you know, slack is enterprise thing but it you know, feels there’s like chat right so and so is you know Microsoft Teams, right? So all these solutions, they heavily borrow from the from, you know, the personal space and bring that experience to the enterprise space. Because ultimately, you know, we’re all people and we want to get things done. And you know, if you can provide a nice, clean, simple interface, then, you know, that’s, that’s all. That’s amazing.

Mike: That’s great. So a great explanation. And I’m really interested in the business as well. And any business that involves AI typically seems to overhype it, but you seem to have done almost the opposite in saying that, actually, AI is not going to replace everything. So can you talk about a little bit about how to use AI, what it’s doing and what that means for the user? And then maybe you know, a little bit about why you don’t think that AI is going to make every customer support? Assistant redundant?

Indrek: Yeah. So you know, the first thing we start out with companies is that you don’t have to use AI. Right? If you have a soul, if you have a problem, then you can solve it without AI, like, go for it. Right. And you know, when companies start out, I always first tell them that, don’t think about AI. Think of the KPIs you have to deliver to your boss or your shareholders. And just think in terms of that. And then if you say, okay, we want to achieve, you know, x has to be greater than five, or we want to reduce costs, or make more revenue or whatever or be more efficient, then, you know, if you see that AI can help you there, you know, definitely try it out. So in our case, the AI comes in, in the product in the natural language understanding.

So you know, the thing I talked about earlier is that if you ask a question, What do you mean? What is the intent behind it? What’s the meaning? If I say, hey, how much does it cost? You know, do I refer to a credit card? Do I refer to a, I don’t know, a mortgage? You know, what, what does it mean? Right? So, so that’s that, that’s at the core of it. And you know, we’ve solved it with an ensemble of different algorithms, because there’s no sort of, there’s no single one algorithm to rule them all doesn’t exist, because, you know, languages are different data sets are different. People ask things differently. So we’ve, we’ve sort of created this kind of like a solution that kind of works like Eurovision where you have a dozen algorithms, and they all compete against one another. And the one that, you know, gets the most points wins, right? So it’s sort of this day, we have created this competition among them, which is great, because then the best, the best ones prevail. And then you know, you get the answer from, from the bot in terms of, in terms of what’s the most relevant thing that we think that the user is asking? Because the just as a side note, you know, understanding humans for humans is hard. But then think how hard it is for machines to understand humans, right? So if we’re having a conversation, then you know, if you ask something, then sometimes I might say, Hey, what do you mean by that? Well, if one brain is asking that from the other brain, then you know, you actually, you know, think what, what AI today is, it’s a bunch of, I don’t know, neural networks running in the cloud. I mean, it’s, it’s nowhere near the complexity of our biological brain. So we’re kind of you know, there’s so many magnitudes of difference between intelligence that it’s really hard for an iPhone to understand what you want, right? So that’s just like, it’s just a side note. But, so we create those types of systems where they can intelligently get what you want, and provide you an answer. And then coming, coming to the thing about AI taking over the world, I think it’s a good story to sell, it’s good to say that now we beat chess or we can play go and you know, all the next thing is we will have all the car self-driving and everybody out of work. But you know, the real world is so much more complex and nuanced. Know, the fact that you can play a game like go or chess that has, you know, it’s a, it’s like a bound mechanism with its own rules, then, you know, there’s no rules in communication are so random and chaotic that to, to do to have the level of intelligence of a human being, we still don’t know how the brain even works a little low to replicate it or create something that’s smarter than it. So I think we’re just sort of kind of, you know, I don’t want to say but kind of like monkeys looking at fire, but having no idea like, how it comes about or what it is, right. So and I think he’s going to change and we’re going to get smarter, but you know, we’re ways away from this, you know, Terminator life. So, what we as a company advise is that, you know, have your expectations set, you know, realistically, because ultimately, you know, you want to achieve your KPIs. You know, the CEO of a company doesn’t care what do you use, whatever it is, they don’t care. They just want to see like revenue going up or down, right?

So, in that regard, you want to provide something that’s useful. And also that customers understand what are the current opportunities, but also limitations of the solution. And I think where we see a lot of things happening, actually is how can you make your staff be more efficient. So let’s say you have a tonne of simple queries, well, let’s give those to computers so that we don’t have to waste, you know, hours of our time on simple queries. But let’s say we can, we can focus on more complex offers for our customers, or let’s say some big customers want like, quotes for our products, or they want support for others things that you know, machines cannot solve. So let’s focus on those more complex creative things. And let lets computers do simple things. And I think, you know, they’re the gains are really enormous. And I think this is, you know, what, what a lot of companies are focusing on, instead of just saying, hey, like, let’s replace humans, you know, one, why should we replace them like think the brains are so genius things, they can do so much more than just simple things? Let’s just have computers do simple stuff, and us do more complex stuff and be more efficient.

Mike: Fascinating. That’s, that’s a really interesting insight into, you know, what you can really achieve with AI rather than maybe some of the more science fiction predictions. And so I’m interested, I mean, it sounds like what you can do is, obviously, if someone wants to deploy a chatbot, they have to think of the questions and how to answer them, but they don’t have to think of, you know, the 50 different ways the same question could be answered. So how quickly can people deploy a chatbot using alpha blues technology?

Indrek: So today, when companies approached us, you know, as a preparation, you know, what I always ask them, okay, what was the KPI you want to achieve? You know, what’s, what’s the, what’s the function, what’s the use case for the bot, because you can be used for sales or support or external internal support, process efficiency, whatever that may be. If you say, you know, I know, my use case, I want to achieve this, these are the 50 topics that that is that our time being asked, you know, then, you know, getting the bot ops know from our side, you know, it’s, you know, the day or so like, it’s, you know, we can measure it in hours. The complexity, of course, with large companies comes because they have their own existing systems. If you have to do API integrations, you maybe want to authenticate users, you have to deploy to a certain cloud or on premise environment, you know, those things, then realistically, can of course, take weeks. But other than that, what we also usually advise companies is that, you know, if you have a use case, and you’re not really trying to complex, something, like really, really crazy complex, then you know, you can get it up and running in a few weeks, and then the next few weeks, you can test it out. So you know, if you run a project for about three months, then you know, you have your bot, you have tested it out and customers you can assess its, its benefits, and then you can take it from there, because it always makes sense that if you haven’t, and I think this goes for all, like all AI projects that companies are doing is that if you really haven’t done it before, and you don’t know how it’s going to go, you know, do a do a pilot, you know, say 345 months, let’s, let’s take this problem, let’s, you know, apply things to it, let’s see how it works, we’ll measure it, and then we’ll see if it works for us. Because it’s one thing to read about it in newspapers, and you know, what your competitors and everybody else is doing. But it’s another thing to try it in your own environment. And by doing this, these few months, you know, proof of concept or pilot, you know, you get so much smarter, and then maybe you uncovered something that you didn’t know before or something works that you didn’t expect or things go the way you expected, then you know, perfect, you can just then roll it out. company wide and then get the benefit.

Mike: That’s amazing. I mean, I could talk to you for hours on this. But I think that’s perfect advice to end on. I guess, you know, the important thing is, if anyone listening to the podcast would like to find out more, how could they get in contact with you and Rick?

Indrek: Yeah, so the easiest is just you can send us an email at Or you can go to our website Naturally, we have a chatbot there. So you can interact with the bot, leave your context, and then we will get in touch. So your method of choice, how you want to contact us.

Mike: That’s perfect. Thanks so much for being on the podcast. It’s been absolutely fascinating. And I’m sure a lot of people have learned not just about chatbots, but also a lot about AI. Thank you very much. Thank you Thanks so much for listening to marketing b2b tech. We hope you enjoyed the episode. And if you did, please make sure you subscribe on iTunes or on your favourite podcast application. If you’d like to know more, please visit our website at Napier b2b dot com or contact me directly on LinkedIn.