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Ramli John:
All right. Hey, welcome everybody to this live session Q and A. We have Rizwan here. He is the VP of product at 7Shifts. How's it going Rizwan? How are things with you?
Rizwan Jihan:
Great. Doing well. Today is going to be a warm day by the looks of it, maybe in a little later. So I'm looking forward to working on my laptop outside. How about you?
Ramli John:
Same. For people tuning in, we're both based out of Toronto. Go Torontonians.
Rizwan Jihan:
Yes. Go Raptors.
Ramli John:
All right. Oh man, let's not go there.
Rizwan Jihan:
Fair enough.
Ramli John:
We're going to be talking about product-led growth and transitioning into productled. You have some frameworks around that, but also around PQLs. But if we jump into that, I'd just love to know a little bit about your journey at 7Shifts with productled and how... I looked at your website 7shifts.com, and there's a free trial. Has it always been like that, or has that been a journey where you guys are at?
Rizwan Jihan:
7Shifts was originally founded by Jordan, to help solve his dad's restaurant issues with respect to scheduling. And very quickly thereafter he decided to make it into a full business, and almost from the beginning, it's been a free trial for 7Shifts that's been offered. We definitely a high velocity, high volume SaaS business. Our ticket price is not particularly high, and so a free trial and just trying to get people to be qualified from a product perspective by the product definitely helps with the whole sales process. But I think over the years, and specifically since I've been here we've definitely evolved a lot towards being far more efficient with our business, and our conversion by leveraging PQL thinking I'd say.
Ramli John:
Well then, let's get there.
Rizwan Jihan:
Cool.
Ramli John:
Let's talk about PQL, because I've talked to Phil who was there before, and one of the things we talked about is PQL. Another question we already have from the audience is how do you define PQL? And what did that look like before? And from what I understand, your PQL looks a lot different now than it did when it first started.
Rizwan Jihan:
So a lot of things that I've encountered over my career, a lot of the things emerge from various parts of the business experiencing similar problems. The way I define PQL is not so much a binary qualification, but a scaling or a evaluation of how likely is this person going to be interested in moving to the next stage of our conversion funnel? And specifically towards buying. And so for us, it's the signals that a user gives off. So a person clicking on an ad to come to your marketing site is a signal. Going through the signup flow to create a trial account is a signal. Using the product in certain ways are signals. And then if you add the other signals, and how you evaluate those that helps you get to a better understanding of whether a given customer is going to derive value and convert for you ultimately, and that's the way I define and look at it. Does that answer the question, for anyone who's out there?
Ramli John:
Yeah. I'm curious more specifically, what signals did you look at early on? Particularly for 7Shifts, knowing that it's helping restaurant owners schedule, what were those early signals?
Rizwan Jihan:
So I think that people don't call it PQL, but lots of facets of the business have similar ideas to PQLs. The salespeople want to talk to a lead that is the hot lead, the lead that's going to be easy to close. The marketing folks, and the demand gen folks want to make sure that the trials they're sending are the ones that are going to close. And so for us, the start for PQL, I would say actually would have been more so on the demand gen side, and less so even in the product group, which just with respect to the demand gen team would evaluate their sources of traffic that they are paying for. So Google ads, Facebook ads, random marketplaces, wherever else they're advertising, and then categorize those sources against them becoming trials as being their first step.
Rizwan Jihan:
And because the trials had certain fields that had a certain amount of friction, it was a form of qualification. And so that would be where it started for 7Shifts. I think that it started on the other end from the sales side, secondarily, as a followup where the sales guys were saying, I've got too many leads, where do I spend my time? You've got too many leads coming in, what do I do? How do I know who to talk to? We have a huge amount of traffic that comes through 7Shifts, a huge number of trials that come through 7Shifts, how do I spend my sales dollars efficiently with respect to engaging, and helping to push these leads over the edge? And that was initially developed with, okay, we'll only talk to organic traffic maybe, which is very simplistic. That, and then they said, okay, we'll only talk to people who happen to publish a schedule, was the next thing, let's say.
Rizwan Jihan:
And it went from there, and then eventually we actually developed a far more complex solution, which is taking all the activities that a lead does on our site, and run it through a five level artificial neural network that we've trained to try to score them. And then basically you say we've got, the salespeople start at the highest score and just work their way down, and as far as they get, is as far as they get. So there's no hard binary qualification anymore, it's entirely a score based system based on this plethora of factors that we've compiled.
Ramli John:
Really fascinating. So first of all, it was trying to identify who the good leads to talk to for sales. So it started off with, let's just talk to everybody who came in from organic.
Rizwan Jihan:
Yes.
Ramli John:
And then the next one was, you're looking at "an activation" activated one, who publish a schedule, 7Shifts will be putting you in this, it's a scheduling tool for restaurant owners. And you probably just schedule, you're probably done something in it.
Rizwan Jihan:
Yes.
Ramli John:
Now you're saying that it's evolved and it's this black box. You're talking about now machine learning. That's what I remember talking to Phil on that Podcast. It's like, there's this magical black box that's calculating all this stuff. Before I get there, how long did it take from organic to figuring out, well, let's go deeper down the journey and identifying... Did you stick with organic for seven months and then decided, well, we're still getting way too much. Let's zoom in deeper into that journey to identify that need.
Rizwan Jihan:
So I think the organic, it wasn't a question of too much. So it started with saying, we have a problem. We don't know who to target. We know that these are the best to target, probably organic, so just a guess. And they said, okay, cool. But how do I know who's next to target? And how do I get better? And some organic is good and some organic isn't. It's a very crude factor to qualify folks into have an activation threshold. So it was pretty quick. I think it was, like everything else that evolved over time, we said, we solved the first problem then we solved the second problem, and then we said, how do we get better, and better, and better? Because we started seeing the value from a sales efficiency perspective.
Rizwan Jihan:
So I don't know for sure, but I think it was about a year from initially starting on this journey towards getting to the level of machine learning. But we continually iterate now on our machine learning system and retrain anything for anyone to anyone who decides to do something along the lines of machine learning. If there's something to remember is you don't want your machine learning, or your qualification criteria to become self-fulfilling. As in the machine tells you to talk to these people, therefore those are the only people who get talked to therefore, they're the only ones who convert. Because you have to always have a set of people you continue to provide the white glove sales experience to that the algorithm doesn't tell you, you should talk to. So that you can have the algorithm continue to learn, to say if the market's changed, if you've added a new traffic source, if you've added a new flow in your product. And a new flow is actually better for a different segment that you didn't know before, and you have to always be testing and challenging how you defined activation.
Ramli John:
Really fascinating. If it's okay, there was somebody in the audience who wants to ask you a question. I'm going to let them ask. Alex, you have a question, you have the floor.
Alex:
Sorry, I joined a little bit late, but you may have mentioned this but, and maybe for some context here. My company has done a pretty sophisticated job of routing high intent leads, and managing our mix pretty efficiently on what's going into the sales funnel. Doing the same for product qualified leads is something that we're in the process is building out. So when it comes to systematizing it like you have, I may have just created a word. Did you build all of that in-house? Are you leveraging some third-party tools? Are you then feeding those into Salesforce cues or whatever? How are you managing both the scoring, and the routing from a product perspective?
Rizwan Jihan:
So for us, we do machine learning. So we have a data science team that used Python and other off the shelf libraries, TensorFlow to train the model. And then it actually gets routed through Salesforce after to feed into the sales process for us. So it's as you described, but I think the question is whether... I think you're going to have to build this yourself, or contract someone to build it custom if you're doing machine learning, given it's so unique to your business probably. Unless you're going to rely on specific activation criteria, which is also totally fine, get somebody to go run a regression and see what correlates to high quality, and that can be your first version, if you want to avoid the machine learning approach. Does that help Alex?
Alex:
No, that does. And sorry, I had missed the machine learning bit at the beginning, which led to the question. Thank you.
Rizwan Jihan:
Cool.
Ramli John:
Thanks for asking that, Alex. One other question I had, and this is something that I talked to Phil about is, you actually didn't call it a product qualified lead. And it's fascinating because language matters. And for a lot of people who are tuning in the challenge they have is trying to get buy-in from particularly the sales team. You call it a tier one lead, and I'm curious, first of all, is do you still call it a tier one lead? And can you explain briefly why your team, your company decided not to call it a product qualified lead?
Rizwan Jihan:
It's again, a case of how it emerged in the organization. It started with again, demand gen wanting to make sure they were sending good traffic, they weren't wasting money. And in sales saying, I have too many leads to give the white glove treatment to all of them, how do I know who to talk to best? And I think just tier one leads started with the sales folks. It's very salesy, and on the sales side we still call it that. I haven't fought with people, I don't think it's worth it from my perspective to go and try to change the sales culture. I can just tell them, hey, anyone with this PQL tag on above this number is a tier one lead, and they go, cool, I know what my tier one leads are. So I'm not too concerned organizationally about me trying to change that whole sales culture, that the sales folks do what they want to do, but inside of product we've certainly moved away from the tier one terminology, and towards this scoring of leads, and PQL.
Ramli John:
That makes a ton of sense. And in terms of the score, what score are you getting right now with the machine? Like I said, it's probably a block box where it's doing its magic. And is it giving you a score out of 100? And what is the threshold that somebody, the lead becomes qualified enough for sales team's, more direct outreach, than more passive.
Rizwan Jihan:
So it does give a score. I actually don't know what the range is, but I don't think it's actually material. It's zero to 10, zero to 100, whatever it is. But the way in which we work through it is we actually just say, we've got X sales resources and they just get the list in their queue and it's already ordered, and they just work down as far as they can. Now, there is a threshold at which point they will say, okay, this score is below a certain number. I don't know what the number is, but below a certain number. And then they just say, okay, that's where we pause, or we'll put them in an automation flow. And if they come back through the automation flow, we'll bring them back and talk to them one-on-one.
Rizwan Jihan:
If you think about it in classic economics argument, it's marginal cost, marginal return. Is my marginal revenue going to be higher than my marginal cost? So if I hire one more salesperson, and you can get five farther down the list, will I make more money? Is the question. And if you get more efficient at your qualification criteria, you can get that list to be smaller and have a smaller cost with even higher revenue. That's the game we're playing.
Ramli John:
Interesting. 7Shifts is one of the most advanced, you have the most advanced PQL system I've seen. For companies that are just starting out with PQL, and they're trying to define what you call a tier one lead, what advice would you give to companies who are making that transition? And PQL it's an unknown term and PLG might not be a fully adopted in the organization and there's resistance from the sales team. What would be your suggesting to driving PQL?
Rizwan Jihan:
I'm a product person. I'm assuming most people listening to this are going to be product manager types. I would put on my product management hat and say, let me understand your problem, you pretty know where it's going to go. Let me understand the problem. Let's talk to some few of the sales folks let's say to them, what are the problems you're running into? And if you're in a high volume sales organization, they're very quickly going to be like, I don't know which leads to talk to. I don't get a lot of sales, or if they say I want to get more people to convert. Wouldn't it be better for you to know who's more likely to convert? You can lead the conversation a bit, but I think just don't worry about the terminology I'd say initially. Worry about their pain points, their problem, just like any good product manager would. And once you say, oh, cool, you really want to make sure that you are better able to get the converting leads versus the non converting leads.
Rizwan Jihan:
You want to get the people who are hotter, who are more urgently going to need our solution. That's interesting. Let me talk to you about some stuff that, and just say what things make you know that people are going to be higher converters? They'll usually say, oh, they used our product a bunch or something like that. And you're like, oh, wow, that's product qualification. They qualified through product usage, so what if we were able to give you a way to know whether they've used the product X amount, or Y amount, or hit certain thresholds? And then you can say, I've made up a term, it's called product qualified lead, and they go cool. That's how I would approach it. Honestly it's very, let's understand the problem. Let's get on the same page about the problem. And then let me explain to you a possible solution and then terminology later, but that'd be my advice.
Ramli John:
That's so good. I'm re-reading How to Win Friends and Influence People by Dale Carnegie, and he said, the best way to influence people is to speak their language and what people truly want. And you just drove home that point. What do the sales team want? They want hotter leads. They really do want hotter leads, and you're just framing this conversation around that conversation. In terms of also community communicating this idea, one of the things you were talking to me about was this retail, grocery store analogy, about getting buy-in around this. Can you talk a little bit about how that framework you use to communicate ideas, particularly if it's something that might be new for people, or they're not fully bought in about it. Can you share a little bit about that analogy that you have?
Rizwan Jihan:
Sure. Maybe I'll start with a little background of just how I got to it. Because I think it's interesting, at least when I was in school, one of my professors made us do a project where we literally had to sit in a retail store for a full day and just observe. We didn't do anything, just observe and note what things had happened. And so I went and I found a retail store, and I sat down and I watched people come into the clothing store, and I'd watch. A few things I saw were a bunch of women would walk in to the men's section, look at a bunch of clothes, go away, and then come back an hour later with a guy in tow. I would see a bunch of guys come in on their own, put on a shirt or something, look around, take it off, put on the rack and leave, because they were looking for a mirror.
Rizwan Jihan:
So you get these insights. And from that, I got turned on to this idea of really the presentation and the organization of the retail environment has a big bearing on the conversion rate and the money that you're going to make as a retail store. And so there's a great book by Paco Underhill called Why We Buy, it's old now, but the central idea, and I brought this same idea towards the digital world as I got more and more into e-commerce and eventually into SaaS, which is the retail world has got a lot of great practices and ideas, because they're trying to do the same thing you're doing, which is someone walks in the door and they want to get them to pick up items and walk out with the biggest cart size possible.
Rizwan Jihan:
And they've been around forever. So there's lots of analogies and ideas, optimize your own solution, but also communicate to others because everyone's been to a grocery store, everyone's been to a retail environment. So when you use that analogy, people can relate to it when you're trying to communicate the idea. So a easy one is if you walk into a grocery store and there's rotting meat at the door, you're turning around and walking out. So the presentation of your front door matters. If the carts won't separate easily, or the wheels are squeaky, someone may leave quickly. So you want to make sure that those funnels are optimized. You want to give free samples, everyone likes free samples. You want to make sure you figured out the path of maximum revenue, and in a grocery store, that's an outside row.
Rizwan Jihan:
When you get to the counter, I actually recently encountered this. If you don't have the payment method you want to use, the customer might walk out. During COVID, I want to do tap with everything, and I went to a place that would say cash and debit only, and I was like, thanks, and I left because I wanted to use my Apple Pay. So those analogies, just thinking about when you go to a grocery store, just even observing the things they've done, and saying, why did they do that? Because, they are very deliberative about what they do. Every product goes into a certain spot, in a certain order.
Rizwan Jihan:
And how often do you talk to a representative there? They have a very low ticket price. They can't afford it, but they do have the ability for you to talk to people. And that's a question you have to ask yourself, how do I do that as well? It's a nice thought experiment to think through your funnel and saying, when you go to the grocery store, how does this, what they're doing here relate to what I'm doing, and just sparks ideas, then helpfully allows you to communicate it to others. So that's just something, a tool I use.
Ramli John:
That's fascinating. I guess, I've been on a few podcasts lately and people ask me Ramli, what is product-led growth? And I tell them, it's this obsessive nature of focusing on the customer experience, because if you don't, then it's really like focusing on getting people to experience the value before they purchase it. And I love this analogy because now I'm going to use it for every Podcast I'm in. This is exactly like you're talking about that experience. And if you don't nail that from the get go, because in a sales led world, you can just sell the contract, a year contract, and who gives a care about the experience? Look at how Salesforce used to be sold. They lock you in for a year, and their customer experience is not the best because they've already gotten you locked in for a year.
Ramli John:
And in a productled world where people are easy come, easy go, and if they don't have the pay that you have they're going to walk out. And this is exactly like if you use Canva or another productled tool like 7Shifts and your experience is not great, they can just Google another solution and they can just walk the heck out. I love this analogy. I just asked another question particularly about product qualified lead, and I'm going to pass the 401 scan to him. And for anybody else who wants to ask feel free to just drop it in the comments as well. So Alex, you have the floor.
Alex:
Sure. Sorry, I'm about to derail us. So I assume when you settled on the whole ML journey, you probably started by doing some big regression analysis, and said, these are the variables our systems is going to analyze. And I'm no machine learning expert, so if some of this doesn't make sense, bear with me. So what were the initial things that your model was analyzing in terms of these are the things that impact qualification? And then over time, has it surfaced some things that impact qualification that you hadn't originally considered?
Rizwan Jihan:
So I don't know all the factors anymore. Our machine learning team has gone and taken this off into, or data science has taken off into crazy land well beyond me. But the initial set of factors is really tying back to your value prop. The idea of product qualified lead is the lead has experienced some of the value, or seen the value, and expressed an interest in that value. So for us, if a manager signs up to 7Shifts for trial and doesn't invite employees, that's a pretty bad sign because you're not going to build a schedule unless your employees can access it. They also need to see the schedule. So working through it and saying, having others in the system is a value, potentially. Having a schedule published is a value. Sending messages to your employees are a value. Other things on it, are just like certain page views. So if they even clicked on the buy now button to see the pricing, is an indicator. So it's a lot of theory crafting initially to do the regression.
Rizwan Jihan:
And then I think depending on, as you get more and more complicated, or more and more sophisticated in this, you can add more complications into it. And almost let's go cool, we've got an event system. Let's just feed all the events in and see what happens. So for example, we did that eventually we just took every event and we're like, feed it in, and see what comes out the bottom. Does that help?
Alex:
Yeah, it does. I was also interested to see if there was, so some of the things around activation and product usage are fairly common in terms of doing the assessment. I didn't know if there was other things that your model had actually identified, like maybe to pick a bit of a silly example, users who access the product in evening hours in their local time zone are four X more likely to buy the product because they're testing it outside of work as well. Scenarios like that, that your model would identify that are frankly, a pain in the ass to try to identify manually, and we need to offload to the machines, so to speak. So anything like that are interesting tit bits?
Rizwan Jihan:
I think that those are going to emerge from a theory perspective as well. I think it's a great theory, honestly, that the time zones... Because I've actually experienced it in a different business, not 7Shifts, where we found that conversion by time of first landing on the site varied. And then we actually made a landing page that was more night themed and that actually increased the conversion rate, and the number of trials we've got from that time period. So it's not a bad theory. It's not a silly theory, but I think it's one we have to test and see where it goes. And you have to figure out the factors you want to include in the ML or regression to have them included. So it's a great theory. I think you definitely tried at some point.
Ramli John:
All right. I think Alex, thank you for that question. I have another one from Sam, and I'm going to pass it off to him, another question around PQL, if you don't mind, really sorry, we'll appreciate that.
Rizwan Jihan:
Sure.
Ramli John:
Sam.
Sam:
Sure. Hey, Rizwan. Thanks for taking the time to speak with us today. My question is, before you got into using ML to identify your product qualified leads, what was the MVP you used on the data side before deciding yes, this is worth us going to build an ML model. I'm particularly interested as I'm in a very early stage company. So not at the point of, I guess, having the volume of data or the expertise inside to do the full on ML approach.
Rizwan Jihan:
You're still going to need data, so you need to get to some statistical threshold. And the only problem with that is that being a small scale is that the more factors you add, the more data you need. So what I would try to do is collect the data, run through with your sales resources. If possible, reaching out to everyone, depending on the scale, or reach out to a random sample of people initially, and track who they reached out to versus who they didn't reach out to, and the random factors.
Rizwan Jihan:
And then you can subdivide by one factor at a time, and just say, do a correlation coefficient, if you want, or a basic progression. And then just look at the adjusted R squared value basically is what I would do. I'd do the regression to make sure you get statistical significance. This is some statistical stuff and you can definitely look this up online, but using R or something, you can figure that out. But that's how I would start. Just make sure you always have controls. So track who's reached out. Track who's not reached out. And don't let the salespeople choose who they're reaching out to. They've got to go by random initially, or at least have a subset that's random, so you're able to collect the data. And they're going to hate it, but you've got to convince them in some way to say, within one month we'll have enough data, and I'll be able to get you more efficient leads.
Sam:
Thanks a lot.
Ramli John:
If there's anybody have any other questions, feel free to drop it in the chat. I've asked your advice around PQL before already, Rizwan, I'm curious if you have any particular advice that you'd like to give to companies who are making that transition to productled, or they're already productled and they want to scale up their business. And you've already said a few steps here. You talked about the retail grocery store analogy. What one or two piece of advice you'd like to give to them? And it could be something that we talked about now, or something we haven't talked about yet.
Rizwan Jihan:
I think that this applies to almost everything productled or not. Decisions are not, product decisions or productled decisions, or product-led growth decisions or PQL decisions, they're business decisions. And what demand gen does, what marketing does, what sales does, those are all just business decisions, they're not sales decisions. They affect your whole business, and they have to be tied together to work effectively. The business that has a mediocre strategy, or a mediocre approach, but has the business heavily aligned will outperform a business with a great strategy, but misaligned parts where people are all going in slightly different directions. So I think that the important thing here is really trying to get alignment with those folks. And the way you get alignment, as I talked about earlier is really trying to understand the business as they see it. Understand the problem, and I'm fairly confident you're going to see the exact same problem across the business in lots of ways.
Rizwan Jihan:
People always want to be efficient with their money, efficient with their human capital, and that is in my mind, the path to introducing product-led growth and PQLs, because it makes the business efficient at every level. And it's really how you multiply the valuation of your business, if you're in that startup world, is the efficiency is key. And so that's just what I would say, is don't try to think about product-led growth and PQLs as a product decision or a product problem. It is a business wide problem and understanding those stakeholders and the idea of efficiency is going to be an important way to drive it forward.
Ramli John:
That's really fascinating. I'm curious also, you talked about getting buy in from... Does the product team meet with the sales team consistently? You're nodding, now I'm curious. What is that cadence of how often do you meet? What do you talk about? And what does that partnership between product and sales look like?
Rizwan Jihan:
So the sales team actually has a team meeting every week. And our product person who is focused on the product-led growth side of things attends that meeting every week. And in that meeting, it's an open forum where the sales team talks about problems they're running into, talks about opportunities, and this sparks ideas to the product person, which they then catalog. And then they meet with actually the managers of sales every two weeks, and discuss about initiatives, ideas, how do we improve things? What are your problems? So it is very tight from that perspective. There's a lot of constant contact. That sales meeting is not a sales meeting for product, that is a sales meeting for sales. Just the product person is going to help surface these opportunities, and hopefully get closer to sales. So it's pretty frequent.
Ramli John:
Really fascinating. I love how you're embedding each of the team members in each other's meetings. That's something that I've seen in growth teams like Cleo and other places where, that's one way to build partnership is they attend each other's meetings, and really does build. Sam has another question around the model and factor around buying B2B software. I'm going to pass it off to Sam again, just to let him ask his question.
Sam:
Cheers, Ramli. I was around with some B2B tools. They require lots of different users to be on the platform in order for them to see value. So take an example of Amplitude or an events platform. You need product growth, marketing engineering, to all get into that. How do you model that when it comes to looking at PQLs in terms of trying to identify across an account, or across a company? What are the different factors that would lead to becoming a PQL?
Ramli John:
Sorry, can you list the companies again? You mentioned Amplitude...
Sam:
Say Amplitude, other analytics platforms, or just thinking of another, I guess, some email automation softwares as well would require multiple people to be on them, for them to be adopted and task tracking software as well.
Ramli John:
So I think that my initial, my head goes to when someone is first entering those pieces of software. So we've used Amplitude, and still do use Amplitude today at 7Shifts. When we went into that sales experience, it was one person using it, they were exploring. So I don't think at least until the initial conversion, you can count on the other people being there. I think in those modes, I guess my question is often you would probably have those analytics platforms, like a demo mode, probably come with demo data I'm expecting, because I think it's very hard for those analytics platforms specifically to provide a great experience because they're not ready to wire up the actual data, because they probably have compliance, and data privacy and have to update their sub processor list. So they're not going to do it until they bought. And they're probably not going to invite a bunch of people.
Ramli John:
So something like a demo mode maybe, and that allows you to see the person using it, playing with it, going deeper in the product, the number of reports they looked at, maybe. And I would think about something along those lines, observe how your current users are working. But I think that the users being invited in that use case is probably not a good activation criteria. You just wouldn't be inviting them until you made the decision to buy, I don't think. Does that help Sam?
Sam:
Yeah, for sure. Thanks a lot.
Ramli John:
Awesome. I'm looking at the list of questions from folks. If you have any other questions, feel free to just drop it in the comments, or I might wrap this up sooner. But, Rizwan, if people have more questions, where can people find you? Do you have a blog? Do you have LinkedIn? Where can people find you online if they want to take a peek at your brain, anything about product or anything else?
Rizwan Jihan:
I'm a pretty crazy privacy nut, so I've actually deleted most of my social media accounts over the years. So hashtag delete Facebook, but I do have LinkedIn. So you can always just look me there, Rizwan Jihan, and my company's at 7Shifts. You can just straight up email me if you want, rizwan@7shifts.com if you want. And I am on Twitter, but I barely ever tweet, but I have I think 10 followers. But I'm happy to chat with people. Always happy to grab a coffee. I always learn something from people, which is always interesting. So a virtual coffee, obviously during COVID, but a coffee non the less.
Ramli John:
Well, rather than prolonging this and just listening to crickets. I just want to thank you for your time and for people who, thank you for being generous. You just blasted your email, I'm not sure if you want that to be here or anywhere else, but thank you so much to Rizwan. I really do appreciate it. This is a different level of PQL understanding, and I really love how you explained how to get buy-in around PQL through sales. That's something that seems so obvious, but it's not something that I put together. But for everybody else who are tuning in, thank you. And, Rizwan, and everybody else have a great rest of your day. Thank you.
Rizwan Jihan:
Great. Thank you.
Group:
Thanks.
Ramli John:
Have a good one.
