Global Benchmarking Series 2022, March 22 Transcript

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COPC Inc. Global Benchmarking Series
Contact Center Technologies


View the Video Recording and Download the Report

[/heading][divider line_type=”No Line”][divider line_type=”No Line”][/vc_column][vc_column column_padding=”no-extra-padding” column_padding_tablet=”inherit” column_padding_phone=”inherit” column_padding_position=”all” column_element_spacing=”default” background_color_opacity=”1″ background_hover_color_opacity=”1″ column_shadow=”none” column_border_radius=”none” column_link_target=”_self” gradient_direction=”left_to_right” overlay_strength=”0.3″ width=”1/1″ tablet_width_inherit=”default” tablet_text_alignment=”default” phone_text_alignment=”default” bg_image_animation=”none” border_type=”simple” column_border_width=”none” column_border_style=”solid”][vc_column_text]Alex Boland (00:00): 

Today is quite an interesting topic as we think about technology in contact centers, so technology in our customer experience operations. I’m going to be sharing with you, not the full report at all, but a chunk of it, some of the key findings that stood out to me, I think some things that were really quite interesting. And then as Shree said, subject to time, we’d love to go through some of your questions at the end of the session. 

Alex Boland (00:25): 

So to begin with, we will start by talking about actually technology being used in contact center operations. What is there now, how prevalent it is, and how satisfied people are with various different solutions. I’ll touch on artificial intelligence, so how many operations are using it and why. What are they trying to achieve with that? Then we’ll spend a little bit of time talking about technology from a customer point of view. Primarily what we mean by that is around the channels that customers use, which channels seem to be best from a customer point of view? Which channels struggle? So a little bit of channel strategy coming in as well in this technology section. 

Alex Boland (01:05): 

To give you some grounding in where all the data comes from, the data I’m going to share today is from two data sources. The first one here is the industry report. This is a report where we ask contact center leaders from all around the world, I’m based in the APAC region. So we have people from here, but Asia, Africa, Europe, North, South, Central America. We have contact centers represented from all over the world. And if you look at that list of industries on the left, that’s just a snapshot of nine of the industries. So we’ve got loads of contact centers to provide data into the operations, and that’s what we’ll share with you in this section. 

Respondent Profile - Corporate slide

Alex Boland (01:46): 

The second set of data is from actual consumers, people who engage with contacts center operations, try to get issues resolved and whatnot. We have quite a representative sample. So we have people from the ages of 18, all the way up to above 60. We asked them about the most experience with operations. And they shared the industry. The majority of those fell into that category in the middle. So telco, banking, utilities, insurance, government, and consumer electronics. And what we asked actually like the consumers, the customers, people who actually engage, the end users, we asked them about what matters to them, about their channel preferences and their experiences using the different channels. And some things that particularly stand out to me in that section, I think you’ll find it interesting because it gives you a snapshot of what the future looks like, how channels are changing and how 2022 is not the future. We will see what the future will turn into. 

Alex Boland (02:48): 

Let’s start though by talking about the technology piece. And we focused on eight different technology solutions. So from call recording and telephony, which may or may not be combined, to collaboration tools like Microsoft Teams and knowledge management and CRM chat bot, then mobile apps for customer, and finally things to do with speech, so speech analytics and natural language processing. We asked essentially, what category do you fall into? Are you using it or not using it? Are you planning to use it? Are you using it and planning to upgrade? That’s the breakdown that you can see. So the dark blue is that we are using with no plans to upgrade. The next category there is, we’re using and we’re planning to refresh. The light green, olive sort of color is planning to implement it in the next 18 months. And the pink at the top is no plans. 

Alex Boland (03:39): 

So I’ve organized this in descending order, based on the proportion of centers that said they currently have the solution. Call recording and telephony and internal comms being the most dominant. And then chat bot, mobile app and natural language processing being used less, but a significant proportion planning to implement it in the near term. I think this chart though, it has quite a lot of information. I find it actually better to think a bit with an adoption lifestyle chart. So let me just explain what I’m about to show you, and then I’ll plot it for you here. So what we see is that, essentially, when technology is released, you have the innovators that adopt it. That’s on the left hand side here, the two and a half percent. If a sufficient amount of them adopt it, and they really are the innovators, then we have those early adopters, the people who are very much looking for the latest technology. And this is true for centers as well as for individuals, like with the latest iPhone and whatnot. 

Alex Boland (04:44): 

Then we have that big chasm. And the chasm forms. And sometimes technology looks to be quite exciting, we have innovators and early adopters using it, but they hit the chasm, and the technology never really takes off. I think a good example of that, if you think of maybe 10 years ago, Google glasses, like the glasses from Google that had a webcam built in. The tech enthusiasts had it. The visionaries perhaps had it as well. But it never really got past the chasm to the pragmatist who said, “Really, do I need a camera on my glasses?” And we never got to that early majority, let alone that late majority and the skeptics. I think, on some level, if we think about channels, video calls are kind of in that chasmic area right now. And they might end up with the early majority, but a lot of centers have been talking about video chat for a while. Some have it. It seems to be growing. Whether we get the early majority or not, it’s yet to see. 

Alex Boland (05:44): 

So let me plot each of these solutions on here. And I’m plotting it from the perspective of how many are using the solution right now. So further to the right means more and more are using it. So I begin with call recording. It’s absolutely in the laggards camp. As in, the innovators, the early adopters, the early majority, the late majority, everybody has call recording. Very, very few do not. When it comes to telephony platforms, you’re in that same sort of group. There are some contact centers out there that don’t have an integrated telephony platform, but the vast majority have something in place. We also see those internal collaboration tools like Microsoft Teams, vast majority of organizations have that. And right behind that, similar sort of rate. So around 80% of centers having knowledge management solutions and CRMs, which still, I suppose, means about one in five don’t have a knowledge management solution. 

The 'Technology Adoption Lifecycle' Slide

Alex Boland (06:43): 

Lagging a little bit further behind, but still in the late majority is chat bots. And I find this particularly interesting because my region, Australia and New Zealand, when we did this analysis just for in region, Australia and New Zealand’s only about 35% for this. So from a global benchmark, we’re well and truly lagging behind. Many centers in Australia plan to implement it in the next year. But right now, the adoption rate for chat bots in ANZ is significantly less than what we see globally. Behind chat bots we have mobile apps that are a little bit above 50%. And then language processing, NLP, that’s still in the early majority. So it’s kind of jumped over the chasm. The industry essentially has decided it’s not a gimmick. There’s a lot of value to this, and we have lots of people adopting it. But it’s still more common that a center doesn’t have any sort of NLP involved in their center at the moment. 

Alex Boland (07:40): 

We also asked centers, what actual solution do you have? So you have a telephony solution. Is it Avaya or a Genesys, or Cisco or something else? Is it Amazon Web Services? So we asked, what do they have and are they happy with it? So on the horizontal axis here, you can see penetration. So the proportion of centers that have that solution. And on the Y axis, the vertical, that’s a satisfaction. Perhaps if you’ve been in the industry, you won’t be terribly surprised that when it came to telephony, the big three that stood out were Avaya, Genesys and Cisco. So Avaya, having the biggest share out of those three at around 25%. Genesys, a little bit under 20. And Cisco around 15, a little bit less than 15%. There was a whole bunch of others though. So the others had about 40% of the market, which still means, from this sample size, 60% of contact centers we spoke with had one of those three solutions. And satisfaction with those three solutions is considerably higher than satisfaction with the non big three. 

Solutions for Contact Centers

Alex Boland (08:44): 

When you look at those others, and perhaps they’re dragged down by some solutions that aren’t very good. But top two, people who are satisfied and very satisfied for the others. It was only about one in four people were happy with those solutions. Whereas when it came to Avaya, Genesys and Cisco, it’s around one in two, to about two in three being satisfied with them. So the big three have lots of the market and they have a high amount of satisfaction as well. In this particular report, Cisco had the highest satisfaction. But you can see Genesys and Avaya are quite similar. 

Alex Boland (09:18): 

We asked the same question when it came to CRM platforms, plotted on the same axis here. So unlike telephony, a number of centers actually have a custom, in-house built solution. So you can see that on the right hand side. And that’s actually 35%, roughly, had that. So around one in three aren’t using Salesforce, they’re not using HubSpot or Microsoft Dynamics. They’re building their own solution and using it themselves. Satisfaction though, being lower than Salesforce or Dynamics. So Salesforce was the second largest here. So the largest single provider was Salesforce at 30%, so around one in three. Microsoft Dynamics and SAP in that 5% to 10% range in market share. And then all others combined, a little bit less than 20%, but relatively high satisfaction in that group. 

Alex Boland (10:11): 

Went again to knowledge management solutions, there’s less of a cluster. Unlike telephony where we have those big three, there are lots of knowledge management solutions. So the others here accounted for more than 50%. Around a third had their own custom solution. And then about 15% had the Salesforce solution. So if I flick back for a moment, around one in three use Salesforce for their CRM. And about half of those use it also for the knowledge management. Some of the others there, like Confluence, ServiceNow, Shelf and LivePro, who’s a partner of ours, well actually not technically a partner. They are a provider that we have worked with so well, that we know they work well for contact center solutions. And we have listed them as an approved technology provider, they fall into that other category. And relatively high satisfaction we see in the other group. 

Alex Boland (11:07): 

Let’s move out of technology in terms of adoption and satisfaction. I want to dwell just briefly on artificial intelligence. And it’s a fairly broad topic. And people understand that term differently. The question we asked was, does your organization use artificial AI for its customer care support function? And it fell into thirds roughly. A third said that they do use AI now. A third say that they’re planning to use it. And a third say that they don’t plan to use it yet. So a little bit off on that. So 37% using it, 38% planning and 25% no. But you can roughly say a third, a third, a third, but where do they use AI in their operations? 

Alex Boland (11:54): 

Well, we gave a few different options. Customer facing, meaning that it’s AI that your end users directly engage with. So 83% say they use it for customer facing activities. You could select more than one, so it adds up to more than a hundred. But 48%, one in two, use it to support the agents. And then 30% use it for data processing. So lots of centers are using it. Not the majority, but lots of centers are using AI. The majority are either using it or planning to adopt it though. And mostly, at the moment, the primary purpose is customer facing. But a considerable amount happening in the agent space as well. 

Alex Boland (12:36): 

What is the goal of what they’re trying to achieve with the artificial intelligence? Well, it’s a thing that our industry loves to talk about, either we’re trying to reduce cost or improve customer experience. So 87% says that their goal is to improve customer experience, and 78% is to reduce cost. You can see there’s some other things here as well. Overwhelmingly it’s about customer experience and about cost. Clearly, if you look at cost per transaction, artificial intelligence and chat bots and whatnot, it may be just a couple cents per transaction compared to dollars and dollars per phone call or face to face interaction. 

Alex Boland (13:18): 

Let me slide into channels. Around channels that are provided, and then how customers actually engage with those channels. And just keep in mind, we expect that we’ll have time at the end for one or two questions. We’ll see how we go. So anytime that you can use that Q&A button to submit your questions. And if we don’t get to them, we’ll have your details so we can just send you an email afterwards if it pops up. So what channels do you offer? Do you only do self-service? Do you only do human assisted? So non self-service, like email and phone and web chat and whatnot, or is it a combination? And essentially, it’s either a combination or it’s human assisted only. There was a couple of centers who had self-service only, but that’s not very common at all. 

Objectives for using AI at Contact Centers Slide

Alex Boland (14:05): 

So keeping in mind that the majority of centers use most types of channels, do you have a strategy for managing volume between channels? So you can think about this as a channel migration strategy. So is your organization actively trying to shift customers from one channel to another? Yes, no, don’t know. So more than half, 53%, have that as their strategy, they are actively trying to move customers from one channel to another. And from which to which? From human assisted to self-service being, not the majority at 41%, but the biggest single response. You can see, within human assisted, still at 10%. And then the other options significantly low. So from self-service to human assisted was 1%. So overwhelmingly, when it comes to channel migration strategies, the goal is more in self-service, less with people. 

Alex Boland (15:09): 

We asked our organizations, and I think hold onto this finding as we go into next point. We asked organizations, “What channels do your customers want to use?” So this is just based on perception here. And we’ve organized it on most popular to least popular. So phone and self service, web chat, email being the foremost popular. Then messaging, in person and video being the least popular ones. So hold that in your mind because we also asked consumers, not the same question, but the same meaning, more or less. We asked them, “If you knew your issue would be resolved regardless of channel, which would be your preferred channel?” And I’ve broken this into assisted, deferred. So assisted being that real time like phone and web chat. Deferred being emails or async messaging. Then the self-service down the bottom. 

Alex Boland (16:04): 

So 90%. So keep in mind, if I flick back for a moment, self-service was considered the second most popular channel by the industry, but only 10% of consumers would choose to use self-service. Even if they knew it was good, even if they knew it was going to resolve their issue, 9 out of 10 people would still prefer to deal with a person. So that’s quite interesting. Phone being the most popular, 30%. Email, second most at 24%. And then web chat. So the big three in this regard are all human assisted transactions, human assisted channels. So self-service coming in at fourth, as opposed to second on the industry view. 

Alex Boland (16:46): 

We also asked, and this was to organizations again, “What proportion of customers do you believe would prefer to get their solution with the help of customer service staff, as opposed to using self-service technology?” What you see is that the majority, around a third in both, at 41 to 60, and then at 60 to 80. As in the perspective of organizations, people who run contact centers, is saying that most of them think that customers, most customers, would want to get the help with customer service staff. So 40% to 80% of customers would rather deal with somebody rather than deal with self-service. Only 14% thought it was going to be above 80% and only 18% above 40%. As in, the perspective is we actually think lots of customers really want to deal with people, which is kind of interesting when the view was also that self-service was the second most popular channel. 

Alex Boland (17:47): 

I think you can say, based on the slides here or the charts just here though, that self-service is less popular than what we think it is. Having said that, it is increasing in popularity. So we asked organizations, “What is your preferred contact?” Not organizations, sorry, customers. “What is your preferred channel and has it changed? So in the last,” I think the wording was like, “last 18 months, last three years. So has it recently changed, the channels that you like to use?” And now I’ve split out the data. So the blue is preference when the preference has changed. The red. The red? The light brownish color, sandy color, is when the preference has not changed. And the best way to interpret this is if you’re looking at a channel like a phone, for instance, and you see that the blue is smaller than the brown, that is telling you that people who have changed their preference are finding phone less desirable, people who haven’t changed their preference are finding it more desirable. As in, people are moving away from that channel. This is, I think, one of the best indications of what the future looks like. So anything that has blue lower than brown, people are moving away from. Anything where blue is bigger than brown, people are moving towards. 

Channel Preferences

Alex Boland (19:04): 

So what we see is that the two most popular channels are indeed phone and email. However, both of them are losing in popularity. They’re becoming less popular. When it comes to web chat and messaging, when it comes to video, which is still not very popular, and self-service, all of those are increasing. So phone, email, in person, all decreasing in popularity. Web messaging, video and self service, all increasing. Some of the pretty big ones has been video going from 3% to 7%, and self-service from 8 to 13. So I think you can view this as brown being the past, and blue, what we expect to happen in the future years. 

Alex Boland (19:50): 

Let’s unpack that a little bit more. So we asked, “Based on your most recent interaction, did you use multiple channels?” So this was asking customers, “Did you use multiple channels to resolve an issue?” And 82% said yes. 82% said they did use multiple channels to resolve. Which, if you take nothing away other than that point, I think you should be almost staggered by that finding. Four out of five customers, when they talk about the most recent interaction with their organization, say they used more than one channel. They’re not able to get it resolved on this initial channel and they need to change channels. That is huge. That is indicating so much waste in our operations. If we were able to fix it on the website or in the chat bot, or on the phone or in face to face, if we’re able to fix it when customers first come to us, how many transactions, how much work can we potentially save? 82% of customers are saying, “I can’t fix it on the first channel. I need to change channels to get my issue resolved.” So that’s quite big. 

Alex Boland (20:57): 

And that, to me, indicates that some channels are not very good at fixing issues. So the way we dive into that one is we asked, “In which of the following channels did your engagement begin and end? So if you see, phone on the left hand side there, is 26% for begin, and end at 39%. What that indicates is that whilst not many people, not many, still a very popular channel, 26%. Whilst not many, compared to the overall, started in phone, lots finish in phone. As in, when a customer starts on the website or email, and has a problem, a lot of those customers will end up going to phone. Phone is a good channel for fixing issues. It fixes more issues than start with it. We see email and messaging, it’s kind of the same. 

Alex Boland (21:48): 

But self service is where the big opportunity is. For self-service, 1 in 10 transactions started on self-service, but only 4%, so 1 in 20 transactions, less than half of those that started finished on self-service. We can infer then that self-service for the customer, I’m suspecting, 60% of transactions on self-service don’t get resolved in self-service. They start in self-service and then they need to go to assisted real time, so phone or something like that. You can see that email and messaging, it holds its own. It’s about equal. But assisted real time is really needed to fix issues. So there seems to be significant opportunity from a self-service perspective. 

Alex Boland (22:34): 

Another point we considered, and thank you for the questions coming through. We’ll cover as many as we can at the end. Another point we considered is COVID. What has been the impact of COVID for engaging with organizations? And 47% of people said that the COVID pandemic has changed the way they engage with organizations. 17% said they weren’t sure. And 36% said no. And why we talk about the pandemic and the issues of that, COPC is a global company. We have consultants all around the world and we travel to lots of places. The impact hasn’t been uniform globally. There have been some parts of Australia that have been quite negatively affected, and some parts that have not been impacted very much at all. So it doesn’t overly surprise me that a third say it hasn’t changed. But more or less the majority, 47%, say they have changed. They have changed how they engage with the organizations as a result of COVID. 

Alex Boland (23:33): 

Have those changes been bad or good? What is interesting here is that 52% say, of the changes that have occurred, it hasn’t been a bad thing. So yes, I have changed how I engage with organizations, but 52% say that this change wasn’t bad. Only 28% say the changes have been bad. And 20% say they don’t know, no opinion on that. We can tailor that into the preference changing, people moving away from phone and email, and then moving towards self-service and video and whatnot. So, many people have been impacted by COVID in terms of how they engage with organizations. But of those that have been impacted, they haven’t actually been negatively impacted, the majority, 52%. 

Alex Boland (24:24): 

So let me wrap up. Let me share a couple of the points that stood out to me. I’ll invite Shree back in, and he can share some of the things that stood out to him, and then we will cover any questions, or as many as we can, comments or questions. So feel free to keep them coming through. So firstly, whilst only one in four centers are using chat bots with no plans to upgrade, almost one in two are planning to upgrade in the next 18 months. So there is a significant amount of organizations that are using chat bots and also planning to upgrade. I think that means that we are quite in a fluid situation. So there’s wide adoption of chat bots, but there’s also a large group of organizations that have adopted it and are planning to upgrade it. So there’s not necessarily a consensus of, this is the gold standard, and where we are at is where we want to get to. We’re still quite fluid when it comes to chat bot adoption and upgrading and changing and working with it. 

Channel Usage Title Slide

Alex Boland (25:24): 

Secondly, organizations are looking for artificial intelligence. One in three using. One in three planning to use. And the key motivations are around customer experience and reducing cost. Primarily, artificial intelligence at the moment is focused on customers. Some organizations are also using that to support their frontline staff, as well as to do some back office processing. And organizations, overwhelmingly, channel migration strategies are about self-service. We want more customers to go through a self-service, less customers coming through human assisted. And whilst we, as an industry center, overestimate how popular self-service is, the data does suggest that phone and email are decreasing in popularity whilst self-service technology, web chat, and video calls are increasing. So they’re increasing just not perhaps as much as we want them to increase. Let me pass across to Shree, and then we’ll get through some of these comments and questions. 

Shreekant Vijaykar (26:29): 

Thank you, Alex. This was quite a data heavy presentation. So I’m hoping that the participants and those who have taken the time out to be here, I hope you were able to get some sense of what is going on, and how the future looks like. We will share the recordings with you, since we already have your connection. I think there will be a link that will be sent to you, for those of you who want to refer back and get a sense of what goes on. 

Shreekant Vijaykar (27:00): 

But let me pick it up from, Alex, the point that you mentioned in terms of self-service technology. And it addresses one of the questions. I think that while [inaudible 00:27:11], see, when you talk about self-service technology, and if it is not designed correctly, then it adds to more challenges in customer experience than solves it. So it becomes a real challenge when it comes to customer satisfaction. And we saw that in your presentation as well, that a lot of people prefer to speak with a human assisted channel rather than getting things done themselves, probably because of their past experience in not being able to solve their issues in self-service. So I think the question that Vial has is that how does the adoption of artificial intelligence, have you seen any places where it actually helps in overcoming this challenge? And then, does our data suggest anything interesting in that area? 

Conclusion Slide

Alex Boland (28:05): 

Yeah. Thanks, Shree. I think that’s a really interesting question. So self-service being the real challenge, how has the adoption of AI helped overcome this challenge? The general principle, I think we would say, is that you need to seduce customers into using self-service. You need to make it desirable. You need to not say you must use self-service, but create self-service that is easy to use, that resolves things. So any AI that can either make it less difficult, reduce customer effort, or increase resolution, and those two things obviously overlap, will improve it. 

Alex Boland (28:46): 

There are a number of self-service solutions I have used that just aren’t very smart, that ask you to repeat information, that don’t understand you as you’re trying to engage with them, that just ask you too many questions and are not well designed. I shared something on LinkedIn a while ago, and it is one of my most popular posts. It was a government website where you had to go through to fill out a form and get information. And one of the questions, I won’t get quite the wording right, but it was around ordering a death certificate. And it said, “Are you currently dead or something?” It was asking, are you alive at the moment? And you’re like, what sort of question is that? How do you really expect people to engage with self-service when the computer’s asking you, are you still alive? We need to design self-service that seduces people, that makes it easier, AI that helps to resolve things. I think that’s kind of a general answer. But if AI can seduce customers into self-service, we will see self-service becoming more popular. 

Shreekant Vijaykar (29:48): 

If I can add to that. And then that’s a great point. But Vial, to your question, if I can also add some of my experiences. What we typically see is that customers need a set of pre-defined options. They don’t want too many options to be given to them. If we give them a lot of options as part of the self-service, then we are just making their journey more difficult rather than reducing the effort. Most of the self-service technologies are driven towards, how can I reduce the cost for the organization? And doesn’t take into consideration this aspect that, are we increasing the effort for the customer? So that is typically one of the drivers. 

Shreekant Vijaykar (30:33): 

I think, to your second point that you have mentioned in the Q&A, you also mentioned about chat bots. A lot of times, the problem with chat bots is that the customer logs into the chat bot. And then after spending a few minutes, the chat bot says, “Oh, I can’t solve this problem. Let me hand it over to a human chat agent.” And then the customer thinks, then why did I even waste my time in going through with you? And this is probably not part of this for particular research. But in the earlier research, we found that, to a certain extent, about 40% of the customers said that I used the chat bot and I’m never going to use it again because it was so robotic. It did not help me. And I would rather just speak with a live agent. So I think that’s probably where it comes. And Alex, to your point, and that’s probably what explains this trend that about one third people are saying that, okay, we have already implemented. And another one third say that we are going to change whatever we have already implemented. 

Shreekant Vijaykar (31:34): 

So that was, I think, one of the things that we talked about. But thank you for the questions. If there are any other questions, please feel free to keep typing it into the Q&A section. I’ll just pick up one question that is there in the chat window that [inaudible 00:31:51] asked about. This was about the research methodology. And I think your question was in terms of, did we have people which were COPC clients as well as other? And how many people do we have in this particular survey? So Alex, do we have some numbers around it? I think it’s around 900 plus. 

Alex Boland (32:10): 

Yeah. I would need to confirm the clients. We did a similar report just for Australia. And in the Australian region alone there was 150 contact centers. I’m not sure how many are included from the global database. But we’re dealing with lots of contact centers representing all the regions of the world. But I can’t just spit out a number. I don’t know what that is. I noticed the other part of the question was around whether it was COPC’s clientele or a larger audience. It was both. We asked anybody who runs operations to provide data into this. So it really is trying to be as representative as possible. And on the point around the slides and the recordings, the reports are publicly available. And for those of you on this call now, you’ll get an email that gets a link to the recording of this as well. Maybe we will do one or two more questions. 

Shreekant Vijaykar (33:06): 

[crosstalk 00:33:06] I think John has asked a question. What is the AI recommendation to implement in terms of first, second, third? I think what he’s trying to ask is, where do we implement it first? 

Alex Boland (33:20): 

Yeah. Thanks, Shree. That’s a really good question, John. I would say, when it comes to AI, it’s not necessarily any different from another solution in terms of the implementation part. The first thing is like a problem statement or a goal that you want to achieve. I think some organizations get excited about sexy technology like artificial intelligence, and then they go out and they try to look for a problem that they can implement the technology for. Really, it should be in the reverse. The first point is that you identify there’s an opportunity for improvement. And then you look at what solutions exist to fix that. Whether there’s a knowledge management solution, whether it’s call recording or whether it’s artificial intelligence. So really clearly defining the opportunity. Clearly define the needs of the business. 

Alex Boland (34:09): 

There is typically a learning process. Like when we talk about artificial intelligence, we’re often referring to that machine learning. So when organizations have identified and they start to deploy it, often there’s quite a long learning process while the AI improves. We worked with a center in Japan, I think initially, on go live, the resolution rate for the bot was maybe around 30%. So it’s kind of terrible. But within six months it got up to 85% for simple transactions. So work out what the problem is you’re trying to solve. You go through an implementation period once you have selected your vendor. And that’s a learning period. And there’s going to be a lot of training and getting feedback to that. And then it goes live from that stage. So that was quite general. 

Alex Boland (34:57): 

I’m seeing a bunch of questions flowing in now. But I also want to respect the time and not take up too much time. I can see names on the questions. So what Shree and I might do is send you an email afterwards, trying to explain the answers to each of your questions. So thank you for that. Thanks John, for the smiley face. And you can obviously respond to that. You’ll have our contact details. 

Alex Boland (35:18): 

Let me wrap up now though. So thank you for those comments. As a reminder, this is just the second report in a series of 12. Many of you have registered. We’ve had like a thousand people register for the series. So it’s great to have so many joining us. This is the second in the 12. We’ll have another one each month where we talk through the findings, myself or a colleague will jump on the line. So we would love to have you for those next year when we run it again and we start to get trended data. If you run operations, jump into the survey, provide the data, you’ll have access to the report. And that will be wonderful. So look forward to seeing you on the next one. And that is where I’ll wrap it up today. Thank you for those questions. And we will reach out by email for any questions we haven’t covered. Okay. Thank you, everyone. 

Shreekant Vijaykar (36:08): 

Thank you, everybody. Have a great day. [/vc_column_text][/vc_column][vc_column column_padding=”no-extra-padding” column_padding_tablet=”inherit” column_padding_phone=”inherit” column_padding_position=”all” column_element_spacing=”default” background_color_opacity=”1″ background_hover_color_opacity=”1″ column_shadow=”none” column_border_radius=”none” column_link_target=”_self” gradient_direction=”left_to_right” overlay_strength=”0.3″ width=”1/1″ tablet_width_inherit=”default” tablet_text_alignment=”default” phone_text_alignment=”default” bg_image_animation=”none” border_type=”simple” column_border_width=”none” column_border_style=”solid”][/vc_column][/vc_row][vc_row type=”in_container” full_screen_row_position=”middle” column_margin=”default” column_direction=”default” column_direction_tablet=”default” column_direction_phone=”default” scene_position=”center” text_color=”dark” text_align=”left” row_border_radius=”none” row_border_radius_applies=”bg” overlay_strength=”0.3″ gradient_direction=”left_to_right” shape_divider_position=”bottom” bg_image_animation=”none”][vc_column column_padding=”no-extra-padding” column_padding_tablet=”inherit” column_padding_phone=”inherit” column_padding_position=”all” column_element_spacing=”default” background_color_opacity=”1″ background_hover_color_opacity=”1″ column_shadow=”none” column_border_radius=”none” column_link_target=”_self” gradient_direction=”left_to_right” overlay_strength=”0.3″ width=”1/1″ tablet_width_inherit=”default” tablet_text_alignment=”default” phone_text_alignment=”default” bg_image_animation=”none” border_type=”simple” column_border_width=”none” column_border_style=”solid”][vc_column_text]For more information about any of the above, please send an email to[/vc_column_text][/vc_column][/vc_row]