Join us with guest Prithwi Dasgupta, CEO of SmartKarrot, a leading customer success platform.
Today Prithwi shares his insights and experience around customer success technology and where it is heading in the future. We look at things such as how using Artificial Intelligence (AI), predictive analytics, and other tools will help scale customer success programs and empower CS professionals.
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Meet Our Guest
SmartKarrot helps companies deliver winning customer outcomes at scale with the most intelligent and actionable customer success software — spanning onboarding, adoption, operations, engagement, retention, CX, and revenue growth.
Learn more at https://www.smartkarrot.com
The annual SmartKarrot Global Customer Success Survey provides a yearly view of trends shaping the Customer Success industry. The 2021 edition offers key insights on the state of Intelligence and Automation in Customer Success.
- Scaling processes is the biggest challenge faced by CS teams.
- There is growing adoption of technology in CS, with ~70% using, implementing, or planning to use a CS platform.
- Customer Health Score is the most popular feature in a CS platform.
- Intelligence and automation will be even more important in the post-COVID world.
- Most (93%) believe actionable customer intelligence is the bedrock of CS, yet many (70%) are struggling, citing capturing all the data as their biggest challenge.
- 78% say Artificial Intelligence (AI) will make CS management more effective.
- Accurately predicting future events is the most useful capability of an intelligent CS solution.
- Most (84%) say automation is important, yet many (70%) have no or low levels of it—signaling a significant adoption gap.
- CSM time saved for higher-value tasks is the most important benefit. Reporting is where automation is sought the most.
- The biggest challenges to automation are process immaturity, budget, complexity, and lack of the right tool.
Future of CS (Next 2 Years)
- 70% say the CS tech stack will undergo notable consolidation.
- 57% feel AI adoption in CS will go mainstream.
- 86% say intelligent insights and automation will be critical for scaling CS ops.
- 84% say CS will go from defense to offense.
- 82% say industry-specific CS technology solutions will become increasingly available.
For more insights please download the full report.
Join our mailing list to get regular updates about all things related to customer success.
We share a weekly digest of the latest articles, publications, and events of interest to the customer success community.
Don’t miss out – subscriber today!
Jason Whitehead: [00:00:00] good morning, everyone. And thanks for joining us for another episode of the Jason’s take on. We’re really excited to have you here today. And today we are really excited to have a special guest for three Dasgupta, who is the CEO and founder of smart care. So we’re very excited here.
And today we’re going to be talking with, Prithwi about. CS tools and tech of tomorrow and how it will empower CS professionals and scale CS programs. So really an exciting topic. I think in a lot of interest about what’s next in the CS landscape, a lot of things are moving. A lot of things are changing.
I think this is a great chance to go in before we go in getting the topic. Perfect. Just give a brief introduction to yourself and smart, caring a little bit about your background and what you’re doing these days.
Prithwi Dasgupta: [00:00:37] Absolutely. Yeah. Thank you. Firstly, Jason, for having me, it’s great to always connect with you and hopefully we’ll have a good discussion, a quick background.
I’m pretty two years in terms of experience in the industry and I began my career in India and has always been in some sort of a customer facing role. I’m an engineer by profession. And then I worked in the factory for a bit building products for customers, and then did my MBA and then went largely into B2B customer management, sales, product management.
Solutioning presales and so on and so forth, and eventually grew up the ladder over the years and was lucky enough to handle a large book of business. In my last role as president of the healthcare technology unit of a large services firm and the learnings of all these years handling customers in different roles, we used to call it account management.
We call it client services and now. We are calling it customer success. We have tried I’m very happy and proud to represent smart gathered. Phase of the industry and where we are seeing all of this head and manage customer success. So we believe that we have something which aligns very well to making customer success accessible for the practitioners.
And I’m very happy to discuss in general. About customer success with you and give some examples of smart characters. Oh,
Jason Whitehead: [00:02:03] fantastic. And thank you. And I also realized as part of the intro, I got so excited. I completely skipped over my co-host Jason Noble from sunny London. So
Prithwi Dasgupta: [00:02:11] pleased
Jason Noble: [00:02:12] you forgot me now.
Hello everybody. I’m excited to be here. I think a really fantastic person to have. We all know smart carrot very well, and they’re doing really some great things. So I’m not that worried that Jason forgot about me. It happened
Jason Whitehead: [00:02:26] shameful. I know. So when for three, and I’ve had many conversations over the past year and a half or so, and I’m always excited to hear it.
Where they’re taking their platform and tool and in near chatting the other day about things such as the future of AI in customer success and predictive technology. And for three years, the term augmented intelligence as well. And I think these are terms that some people have heard of in buzzword format, but not everyone really knows a lot of details.
I guess to create some context here, can you actually share a little about what do we actually mean with these terms and what does this mean for like functionality and what people would expect in any tool that they use? These kind of,
Prithwi Dasgupta: [00:03:00] no, it’s a good question to start off with. Again, I’m trying to simplify it as much as I can extend.
I know. So CEO and you’ll agree with me, Jason and Jason, that. This is a complex world of customer success. We live in today and it’s complex because there are so many moving elements to it. Customer success is a function is sandwiched between the customer on one side and the organization on the other, within the organization itself, there is so many different aspects.
That is product management and more that is obviously delivery and development and board is supporting more the sales involved at times, marketing in word, and then financials in word, the hierarchy you need to report to and so on and so forth and something similar on the customer side as well. What that means and all customer success platforms and tools today start off by integrating, four or five other underlines systems exactly for that reason, because you need to create a good view of the customer.
The very basic. What did that also means though? That there is an overload of. No, there’s so many aspects and you’re tracking so many things for the human practitioner. It becomes next to impossible to always be aware of all the moving parts meaningfully together. So that’s where technology comes in.
The platform helps in assimilating all of that, but where the world is headed to, especially with technologies. ML and AI is first. You need to make sense of all of these disparate sources of data and learn. So the simple word is learning. How do you learn. Learning means you are gaining insights about your datasets.
And that is where the technology of ML comes in machine learning. That’s why it’s called machine learning. So it just learns and stops there. The AI, which is the artificial intelligence is one layer above machine learning, a subset over machine learning, which now not only learns it takes the learnings and actually does applies the learning into some meaningful way.
So it’s trying to mimic what a human would do. And also applies that knowledge you have learned. It’s a difference between learning and application of learning, which is why it is machine learning and artificial intelligence. So it’s a simple way to look at it as a difference between intelligence and learning.
Okay. No so tools like smart character, for example, already happened. What we are trying to do is now take it to the next level and embark the practitioner by helping them learn more about their customers and them learn more about the disparate moving parts that customers and portfolios have.
And as we go up that ladder, when you hit the true AI as I see it, then you have to be able to apply that learning into meaningful work for, to meet your warmup tickets and your customer’s objectives. So that’s. I know on rough high level definition of these complex terms, isn’t it.
Jason Noble: [00:05:48] How do you, this is, this is such a fascinating area and it is technology is helping more and more.
We’re seeing lots of organizations trying things around data, big push on customer operations, customer success, operations, to make that data visible and to get the insights from it. How do you, what are some of the key things that you guys are doing that differentiate yourselves from existing technology out there today?
Because there are a number of players that have tried. But not got as mature of you guys have. So how are you leading this charge when it comes to data reporting, predictive intelligence and what things are you seeing really working for you guys that are making big difference with smart?
Prithwi Dasgupta: [00:06:26] Yeah. And the quite a few things, Jason, again, a very good question. So I think and this is my view and the smart gadgets view they’re off, but the first thing is not to make it more complex than it actually already is right now. So all of the technology is there. The algorithms are there and people can build a go.
It comes as much as they want. So one of the things which has been working for us significantly is we have. The whole concept comprehensive, but simple. And we have taken the effort to simplify the same views the same sort of datasets, but you don’t need to see everything at every point of time, right?
You don’t need a hundred columns and, 50 columns and 200 rows of data to overload you, you need to see what impacts your day-to-day in terms of your managing. And overtime we can, and I’m not saying we are completely there yet because no one will ever be there completely, constantly be in the cycle of evolving what that view needs to do.
But what we have done well so far is started off by keeping the views available and simple and reaching out to multiple channels even to the practitioners. So do you need to log onto the platform as a simple example for you? That a key stakeholder in an account of yours has left? No, you can actually receive a text and say, okay, this is a very simple example.
Now that it feel stretched ahead. So it’s simple, but it’s difficult to pull off at the back. End, keep things simple for the practitioner, but that’s something which sets us apart significantly. The second piece is right from the beginning. We are a single architecture platform, so the aspects of it.
The customer outreach, the aspects of the customer sentiment management, the aspects of, actually logging a touch point and so on and so forth are all inbuilt the aspect of capturing usage, data, product usage, and a lot of our competitors. And a lot of the other platforms actually have all of those, but then they integrate with third party systems or they have.
Or a different tool altogether. So it’s still boarded on and at times it is as good as the different integration. So when you look at how do you actually impact an outcome, the ability to capture the data and the intelligence. Take action has to be seamless. And the moment you have, multiple siloed systems talking to each other, this simple concept breaks, or it takes more time and it’s not as efficient as you would imagine.
So it’s smart being an integrated system that allows us the ability to connect the intelligence to the actions and the automation very seamlessly. And the overall concept is. So I think those two are the biggest differentiators we have. That’s
Jason Whitehead: [00:09:12] great. And I think this is going to be such an impact over the years, and I imagine evolving technologies like this doesn’t happen overnight.
You have to test it and prove it and make sure that you’re actually getting the results, things that you want. Let me ask you though, when you’re looking forward to five years down the road 10 years, whatever the right timeframe may be, how do you see things like AI and predictive technology really impacting customer success, operations, and what does this mean?
For the day-to-day CSM. So will they need different skillsets? Will they spend their time differently? Will they just be better able to handle a larger book of business? What do you see are the biggest impacts there?
Prithwi Dasgupta: [00:09:45] Yeah you’re absolutely right. I think the biggest impact for the practitioner and across the hierarchy in customer success.
Would be really efficiency and bade themselves being successful, actually. Even when I look at our own customer success team, we cannot have successful customers and successful implementations without having successful customer success managers in our own organizations. How do you make the team successful?
And that’s the biggest impact. I see technologies such as AI and automation. Impact the first impact. To make your life easier so that you can either handle more accounts or even in your existing accounts, you are more successful, your whatever KPIs you are being measured on and incentivized on, you’re able to hit more consistently.
So you build trust, start trusting the technology to actually do a fair bit of work on your behalf. And dumps up, replacing you. So the human and artificial place in a customer situation is my opinion, which is why I use the term of mentored intelligence. That whole concept of intelligence is actually to help the practitioner do their jobs better.
So that is the biggest impact. You’ll meet your numbers and then your customer is happy and then your organization is happy. And then we live in a beautiful ecosystem. So that’s laid back in there trying to look at
Jason Noble: [00:11:08] wow, that’s something that we’ve spoken about before. Isn’t it, Jason? This idea. We’ve we talk about tech enabled approach and it is by enabled, it’s not tech touch.
It’s not just technology, but you need the human in there. And this is what customers want from customer success. There is still a human to human relationship about that trust. And I think it’s so important. And I think one of the benefits we’re going to see, and I’d like to ask you about this is when it comes to scaling and growing customer success and the impact of it is the real benefits of this.
So how do you. The likes of AI and machine learning, impacting the ability of scaling for customer success. What specific things do you think need to change or will change?
Prithwi Dasgupta: [00:11:51] Yeah. So I think, look a very good question. And you’re going to, we can look at it two different ways. One is there a need to change what you are doing right now?
So even today, without technology, you are still able to let’s say, segment your customers into high value, high touch customers. And let’s say low value tech, touch customers, very common. Nothing stops you from continuing on that route, that same role, but. How efficient are you in either of these?
So let’s take even high touch customers when you are having the significant impact of human touch and human relationship. Can you are interactions, human input, human interactions with the customer will get better. Yes, absolutely. And how, because then the technology and the intelligence actually enables you to know more, to know in advance and be more proactive than being reactive today, you are probably interacting with the customer more often than not.
Once things go south. With the technology enablement, you could do the same prior to things going south so that you actually stop it from declining. So that is the impact, even in a human to human interaction that no more, you’re no faster, you are more prepared. And then you have the automation to also help you, not only in your own internal workflows, but also at times.
Customer workflows. So nothing needs to change materially. However, having said that trivial needed to change. Technology will eventually evolve in four or five years from now. We’re in a fair bit of customer interaction. You can pull off with technology to human. Also, what that means for you is it’s not that your job is going away.
It’s just that you. Your question of scale, just handle more customers because they, human interaction is not required. You can actually get by with the machine interacting on
Jason Whitehead: [00:13:37] your behalf. No we’ve it’s success chain. We’ve been working with a client really facilitating a variety of executive round tables.
One thing that keeps coming out of this is that Mo many of the customer success leaders we’re talking to are finding that their customers don’t always want the hands-on touch with a CSM. They want to have their problem solved at the right time on demand with the least amount of effort possible.
And they don’t want. Be bothered with a lot of stuff that may not add a lot of value to them. So I’m wondering is as you think about the future of this tech and AI and predictive churn or predictive analytics and all this good stuff, what do you see as the biggest impacts for the customers and how will.
Change their expectations of what a CSM should be doing. Yeah.
Prithwi Dasgupta: [00:14:17] And I think the expectation is the same of efficiency and speed and ease of interaction. It’s just flipped the other way around that. And you’ve said it, Jason, that not necessarily every customer wants a human interaction all the time. They want their problem to be solved.
And if the, if there is a machine. Helping solve it. They don’t mind human to machine interaction also. As long as it’s what the expectation from the customer is that now that these technologies are available is the efficiency getting better? When I look at my vendors, if I’ve purchased.
On a service over time. I’m expecting better work, better results, better interactions and better value because of all of these underlying technologies being available. So that’s absolutely going to be. You cannot have the excuse of, Hey, I’m sorry, customer. I didn’t know. That is no longer an excuse.
Jason Whitehead: [00:15:11] Yeah. I think that’s definitely gonna be a challenge for a lot of organizations and then that excuse goes away. But I think it’s a good sign moving forward as
Prithwi Dasgupta: [00:15:18] well, too. Absolutely.
Jason Noble: [00:15:20] What do you see as some of the biggest risks from this big shift in technology, particularly when you look at things like AI, And, all the, some big risks and challenges that we need to think about when it comes to customer success and our interactions with customers.
I think there’s a great, there’s a real big debate we could have about kind of technology and the human interaction, but what do you see as some of the potential risks that customers, organizations need to be aware? Yeah,
Prithwi Dasgupta: [00:15:43] I think the one very important risk element is our understanding and knowledge of these technologies and what they can impact itself.
There are too many people in the industry right now with, very subliminal or very basic knowledge are talking about and actually trying to implement. These technologies and that is the biggest risk you can have. So first thing is to understand what it is, what our boundaries, what it can pull off, what it can’t pull off like everything else.
This is not a magic round. In fact, it might actually take more time to show you value then any other things, because it needs time to optimize itself over multiple iterations. That’s how it learns. And that’s basic premise of this. So you have the data, I’ve seen a lot of discussions and forums, et cetera, that AI will not work because there’s not enough data.
No I completely disagree. That is enough and more amounts of data and data sets and it increases every year. It is about our understanding of how to leverage that data and give it time implemented the right way and optimize and learn because what it will throw up to you and you should also be prepared to that.
Some skeletons will come out of the closet once you implement such technologies. So you use find some data, which you may not like, and your baby may not be the most beautiful. You, but then the way to go forward is to accept that and then leverage that learning to optimize whatever it shows up as improvement areas.
And that’s the way, and that’s the other risk. I expect a lot of people. A decent bathroom. Hey, no, this is not good. Not many people are happy with skeletons coming out.
Jason Whitehead: [00:17:30] With the data piece too, I’m always reminded the whole garbage in garbage out mentality. And there’s a ton of data available, but machine learning may be able to correct for some of this, to what degree is data quality going to be an issue.
And is it more, less around transactional data that may be generated and stored directly by systems versus data entered to maintain by end user? Sales folks and CSMs and things like that. How do you see all the potential data quality issues impacting this? No,
Prithwi Dasgupta: [00:17:56] absolutely. Look, the whole concept that as we have evolved into, analytics, big data and stuff like that.
So there’s a huge raging debate about data quality. And that is absolutely at the hygiene level. If you don’t have quality data, All of this falls flat, that’s the lowest level of the pyramid without that you can’t build a structure at all. I think that’s a bare minimum hygiene expectation that your data is right.
You made a very important point and we are seeing this also ourselves as an example, in smart getter today, you don’t, you have bare minimum amount of data entry. So we are probably five, 10%. Data entry platform and everything else is really assimilated. I’ll give you an example. So when you’re looking at mapping a customer journey, for example, and what a customer has done on your product and where they have been doing, what are they accessing? Your sticky features are not you are capturing the data right from the point of source, and that is the source of data. So there is more chance that there’s no human intervention.
Entering that data in. So data like that will always be much more valuable than a CSM adding a sentiment. For example, north separate AP, having said that there is also other aspects of data, like your you’re having a call with the customer. So you have, you can have the recording of the call. So that’s unstructured.
Which is there for you now, the system can, instead of waiting on you to actually within the notes, Hey, this happened on the call. The system can actually overtime, and this is how I expect the technology to evolve should be able to parse out what is the relevant aspects of their conversation, and actually provide you with that data.
So again, it is data. Taking the right from the source, this neutral quality. So that is how by keeping the human data import as low as possible, you avoid the garbage in garbage out. And this is a common trend and a lot of systems have been built on the CRM sort of mindset, which is really garbage in garbage out here.
I did this. I’m importing 15 data sources and know it’s not as good as they are. Yep.
Jason Whitehead: [00:19:58] No, I’ve worked on a lot of cm projects over the years, and I always hear people complain that I don’t want to be a data entry clerk or, all of that other to your point, so much stuff is missing or just the hygiene is completely off.
That’s always a concern, but it’s great to hear that this is evolving.
Jason Noble: [00:20:11] What do you think one of the, as organizations and businesses prepare for this and this change, I think it’s going to affect us with the way we interact with customers. The way we all interact with systems.
What what are some of the things and changes that we could be doing to prepare for this and to prepare for kind of this transition, we’ll go.
Prithwi Dasgupta: [00:20:29] Yes. So it’s a complex one, but actually it’s the other way to look at it. It’s fairly simple also because all of us in our own personal experiences, as a consumer of technology, across different businesses, we interact with Actually are already using a lot of these tools.
So when you talk to city, when you talk to Google, you are actually already using it. When you are having an apple watch, you’re already using it. When you are interacting with multiple systems, which are giving you data, capturing your data, you’re already using it. What do you need to do is plus start thinking, Hey, how is this applicable in my business?
And in my world. To my benefit and to the benefit of my customers, that is all there is to it. These are the same things. It’s the same element of data being captured, relevant data, being captured, correlations, being made learnings happening at a random rapid scale. And then the insights provided to you so that you can take better decisions as a practitioner.
So accepting that and moving on. So I use technology when it benefits me as a consumer, but I stay away from it because I’m scared of the take away. My job is something you need not to be scared about because actually impacts there’ll be more jobs created just because of this. You will be able to handle more customers and more.
And grow faster than your career. So use technology to your benefit. I think that’s one repetition. The other, as I said earlier, it’s probably about education right now, the next four, five years on the us to educate one steam. If you really want to implement technology I think you need to give it time and then you need to educate your hierarchy top to bottom.
Jason Whitehead: [00:22:01] I’m glad you talked about the timeline too. When do you think a lot of the technologies that you’re talking about here, the AI and the machine learning and predictive stuff up, when will they really hit their prime? But they’re like, okay, this is fully functional, no longer an emerging technology. Is that a five-year window?
A 10 year window? Yeah. I
Prithwi Dasgupta: [00:22:18] think that technology is already there. Jason. So as far as technology availability and what’s possible, all of these are possible today and then people will start. Smart gutter, all the other vendors we’ve also started adopting some aspects of this. And then the question really is when is it going to get mainstream adoption?
And I think sooner than we think I am sensing it’s really a two year, one year, 18 months timeframe. We’ll start seeing more and more, better adoption. Intelligence based survey beginning of your and our team for this year was general intelligence. And I think overwhelmingly, most of those answers are around.
We are ready. We just don’t know what is available or what can be done. So it boils down to the awareness. So as vendors, such as ourselves are able to educate the market more and more on what is possible. I think the adoption will distinct. Wow.
Jason Whitehead: [00:23:12] Fantastic. Awesome. Jason, you have anything else you want to throw out here right now?
I know we’re almost
Jason Noble: [00:23:18] out of time. No, I think this has been a, this is a fascinating discussion and I think we could go into any of these specifics and actually, maybe that’s worthwhile doing it later stage, but there’s so many different areas. I, and I’d love to your own roadmaps.
Talk about that and maybe a couple of months, time to see what you’re finding out as this technology starts getting out of there. Cause it is a lot of organizations. If you go back only a couple of years were struggling with setting up customer success. They’ve now done that, but they’re still struggling with what I call customer operations, which this whole data piece.
And when you look, organizations still have many decentralized disparate data sets. That inevitably, we require people to go from one source to another, and they’re not bringing them together. And even when they do bring them together, what do they do? And as you scale, in terms of the numbers of customers, that problem grows exponentially.
So something like this, what you guys are doing at SmartCare is so exciting and game changing. And it’s something that we’re beginning to see a lot of. Kind of core CSM platforms try to do something with but it’s going to, there’s a lot of struggling with it and a lot of struggling and I’m so excited by where this will be in, in a couple of years.
So in us two questions. Yes. Right now, no other questions, but I am sure in three months time, there’ll be so much more and I’d really like to dive into.
Jason Whitehead: [00:24:40] Yeah, absolutely. And for three, I’m really excited about what you said too about use technology for your bench, because I think there’s such potential here to free up CSMs the mundane piece.
It’s not adding a lot of value and it’s not fun for them not growing there. Yeah. Career and their skill set. So I’m excited about see how this goes, would love to hear from some CSMs out there who’ve been using these technologies as quickly as they come in. Yeah. And here, what’s working for the most exciting piece and this is really great stuff that you guys are doing really looking forward to seeing it.
Since we’re almost out of time here, before we go, I want to invite you to do a shameless plug. If you would tell people a little bit more about spark care, how they can get in touch with you, how they can learn more about this and the other great stuff. Really appreciate you coming on the show and sharing this with everyone else.
Jason Noble: [00:25:17] before you do that, you forgot one other thing I
Jason Whitehead: [00:25:19] did forget. I’m forgetting everything to
Jason Noble: [00:25:23] go. Go ahead. Do the plug and then Jason will ask you one more thing.
Prithwi Dasgupta: [00:25:26] No, I think we’ll keep it simple again. The best place to learn about us is our website. Please visit smart category. I’m available to discuss I’m happy interacting.
This is very exciting times for us. A lot of what’s upcoming may or may not be available on the website, as you would imagine. And we are keeping some things under the radar. You’ll be able to see as it slowly starts hitting the market, but you’ll get a good sense of what we mean. By intelligence.
You’ll get a good sense of our alignment too. And our passion in making lives easier for the customer success practitioner. That’s what we truly believe in. So whatever you find today on the platform and what you’ll find tomorrow, there’s always going to be aligned to three things, making life easy for you, making you successful.
And we will do that to more smart use of technology, and intelligence and automation and built into that. There is a reason why we are called smart character. I just hope we live up to it the next couple of years, and then we’ll be able to justify that. So I just keep that simple and stop there.
Jason Whitehead: [00:26:25] Fantastic. We’ll obviously have links to that in the show notes below, but the one thing I forgot and thank you, Mr. Nobel for calling me out on this is we always like to ask everyone all of our guests, the full challenge question. So what one bowl action would you like to see CS leaders take today to really embrace emerging technologies and AI technologies to improve their operations?
What’s the one thing that they could do to really be bold and be aggressive in making this work.
Prithwi Dasgupta: [00:26:49] Yeah, I think the first thing is for them to actually show value in their own organization and in themselves, because the moment that happens, there are too many instances I’ve seen very expected. The value and by value, you show value, you get the budget, you get the necessary approvals.
You you get the buy-in from what have you. So are you able to show value to your board, to your management team? Are you able to define what value is for the function? Because if you’re able to do that, the rest of it fall in place, whether it’s technology, whether it’s the sources of finally points.
Budget and intent. So that’s the one thing, I expect CS leaders and the community to do a question themselves on value and be clear on what value they’re bringing to the table.
Jason Whitehead: [00:27:34] Fantastic. Awesome. Thank you so much for being with us. Really appreciate it and enjoyed the conversation so much.
Thank you. Yeah.
Prithwi Dasgupta: [00:27:41] Thanks. Thanks.
Jason Noble: [00:27:42] Thank you. Wonderful. Have
Prithwi Dasgupta: [00:27:44] a great day guys. Bye-bye.